LibRapid#

What is LibRapid?#

LibRapid is a high performance Array library for C++. It supports a wide range of calculations and operations, useful classes and functions, and even supports CUDA! It uses SIMD instructions and multithreading where possible, achieving incredible performance on all operations.

Getting Started

Write your first program with LibRapid.

CMake Integration

See all available CMake options to make the most of LibRapid’s features.

API Reference

View LibRapid’s API and documentation.

Tutorials

Learn how to use some of LibRapid’s features.

Performance Tips

Learn how to get the most out of LibRapid.

Benchmarks

See how LibRapid compares to other libraries.

Caution

Learn about potential issues that may occur with LibRapid

Getting Started#

Installation#

To use LibRapid in your CMake project, first clone the project:

git clone --recursive https://github.com/LibRapid/libRapid.git

Warning

Make sure to use the --recursive flag when cloning the repository. This will ensure that all submodules are cloned as well!

Make sure you have a structure similar to the following:

yourProject/
    CMakeLists.txt
    main.cpp
    librapid/
        CMakeLists.txt
        ...
    ...

Next, add the following to your CMakeLists.txt

add_subdirectory(librapid)
target_link_libraries(yourTarget PUBLIC librapid)

Note

If you are not familiar with CMake, I suggest you follow a quick tutorial on it just to get the hang of the basics. After that, check out the sample CMakeLists.txt file in the examples directory of the repository.

(examples/templateCMakeLists.txt)[https://github.com/LibRapid/librapid/blob/master/examples/templateCMakeLists.txt]

That’s it! LibRapid will now be compiled and linked with your project!

Your First Program#

#include <librapid>
namespace lrc = librapid;

int main() {
    lrc::Array<int> myFirstArray = lrc::fromData({{1, 2, 3, 4},
                                                  {5, 6, 7, 8}});

    lrc::Array<int> mySecondArray = lrc::fromData({{8, 7, 6, 5},
                                                   {4, 3, 2, 1}});

    fmt::print("{}\n\n", myFirstArray);
    fmt::print("{}\n", mySecondArray);

    fmt::print("Sum of two Arrays:\n{}\n", myFirstArray + mySecondArray);
    fmt::print("First row of my Array: {}\n", myFirstArray[0]);
    fmt::print("First row of my Array: {}\n", myFirstArray[0] + mySecondArray[1]);

    return 0;
}

Your First Program: Explained#

#include <librapid>
namespace lrc = librapid;

The first line here allows you to use all of LibRapid’s features in your file. The second line isn’t required, but it makes your code shorter and quicker to type.

lrc::Array<int> myFirstArray = lrc::fromData({{1, 2, 3, 4},
                                              {5, 6, 7, 8}});

lrc::Array<int> mySecondArray = lrc::fromData({{8, 7, 6, 5},
                                               {4, 3, 2, 1}});

These lines create two Array instances from a list of values. Both arrays are 2-dimensional and have 2 rows and 4 columns.

fmt::print("{}\n\n", myFirstArray);
fmt::print("{}\n", mySecondArray);

Here, we print out the Arrays we just created. Try changing the numbers to see how the formatting changes!

fmt::print("Sum of two Arrays:\n{}\n", myFirstArray + mySecondArray);

This line performs a simple arithmetic operation on our Arrays and prints the result.

fmt::print("First row of my Array: {}\n", myFirstArray[0]);
fmt::print("First row of my Array: {}\n", myFirstArray[0] + mySecondArray[1]);

As you can see, Array instances can be indexed with the traditional square bracket notation. This means you can easily access sub-arrays of higher-dimensional array objects.

Now that you’ve seen how easy it is to use LibRapid, check out the rest of the documentation to learn more about the library’s features! There are more example programs in the examples directory of the repository.

(examples/)[https://github.com/LibRapid/librapid/tree/master/examples]

Troubleshooting#

While I have done my best to make LibRapid compile with as few issues as possible, there are cases where it will not work the first time around. Some issues I have experienced myself or have been told about by other users. Some of these issues and their solutions are shown below:

Linux with CUDA#

If you want to use LibRapid with CUDA on a Linux machine and your code is not compiling, please ensure you have the development OpenGL packages installed.

On Ubuntu and similar distros, this can be done with the following:

sudo apt-get install libgl1-mesa-dev

CMake Integration#

Installation#

Link librapid like any other CMake library:

Clone the repository: git clone --recursive https://github.com/LibRapid/libRapid.git

Add the following to your CMakeLists.txt

add_subdirectory(librapid)
target_link_libraries(yourTarget PUBLIC librapid)

Tip

For a template CMakelists.txt file, see the examples directory: examples/CMakeLists.txt

CMake Options#

`LIBRAPID_BUILD_EXAMPLES`#

DEFAULT: OFF

Build the suite of example programs in the examples directory.

`LIBRAPID_BUILD_TESTS`#

DEFAULT: OFF

Build LibRapid’s unit tests.

`LIBRAPID_CODE_COV`#

DEFAULT: OFF

Enable code coverage for LibRapid’s unit tests.

`LIBRAPID_STRICT`#

DEFAULT: OFF

Enable strict compilation flags, turn on all warnings, and treat warnings as errors.

`LIBRAPID_QUIET`#

DEFAULT: OFF

Disable all warnings from LibRapid. This is useful if you are using LibRapid as a dependency and want a cleaner compilation output. Warnings should be minimal in the first place, but this option is provided just in case.

`LIBRAPID_USE_PRECOMPILED_HEADER`#

DEFAULT: OFF

Enable the use of precompiled headers within LibRapid’s compilation. This can be useful to accelerate compilation, but can lead to some strange build errors, which is why it is disabled by default.

Warning

One such build error occurs on some macOS systems with GCC. The resulting error is something along the lines of:

Unknown flag -Xarch_amd64

If you encounter this error, try disabling LIBRAPID_USE_PRECOMPILED_HEADER.

`LIBRAPID_GET_FFTW`#

DEFAULT: OFF

Add FFTW as a dependency and link it with LibRapid. This is required for FFT support unless CUDA is enabled.

Danger

FFTW is licensed under the GPL, which is not compatible with LibRapid’s MIT license. If you are using LibRapid as a dependency in an open source project, you may need to use LibRapid under a GPL license. If you forget, you’ll probably be fine, but I can’t guarantee anything. I’m not a lawyer, so don’t take my word for it.

`LIBRAPID_GET_BLAS`#

DEFAULT: OFF

Download a precompiled OpenBLAS build for your platform, and link it with LibRapid. This is useful if you don’t (or can’t) have BLAS installed on your system.

Warning

Always prefer to use your system’s BLAS installation if possible.

`LIBRAPID_USE_OMP`#

DEFAULT: ON

If OpenMP is found on the system, link LibRapid with it. This is required for multi-threading support and can significantly improve performance.

Warning

If this flag is enabled and OpenMP is not found installed on the system, the build will continue without OpenMP support.

`LIBRAPID_USE_OPENCL`#

DEFAULT: ON

Search for OpenCL and link LibRapid with it. This is required for OpenCL support.

If this flag is enabled and OpenCL is not found installed on the system, the build will continue without OpenCL support.

Danger

If you are using OpenCL as a backend in your code, you must call librapid::configureOpenCL() before using any OpenCL arrays. This function will initialise the OpenCL context and queue, compile the OpenCL kernels and configure the OpenCL device for optimal performance. See the documentation for this function for more information.

`LIBRAPID_USE_CUDA`#

DEFAULT: ON

Search for CUDA and link LibRapid with it. This is required for GPU support.

If this flag is enabled and CUDA is not found installed on the system, the build will continue without CUDA support.

Danger

LibRapid’s CUDA support appears to only works on Windows, for some reason. I have no way of testing it on Linux or MacOS, so I can’t guarantee that it will work. If you have experience in this area, please feel free to contact me and we can work together to get it working.

`LIBRAPID_USE_MULTIPREC`#

DEFAULT: OFF

If MPIR and MPFR are found on the system, LibRapid will automatically link with them. If not, LibRapid will build custom, modified versions of these libraries. This is required for arbitrary precision support.

Warning

This can lead to longer build times and larger binaries.

`LIBRAPID_OPTIMISE_SMALL_ARRAYS`#

DEFAULT: OFF

Enabling this flag removes multithreading support for trivial array operations. For relatively small arrays (on the order of 1,000,000 elements), this can lead to a significant performance boost. For arrays larger than this, multithreading can be more efficient.

`LIBRAPID_FAST_MATH`#

DEFAULT: OFF

Enabling this flag enables fast math mode for all LibRapid functions. This can lead to a significant performance boost, but may cause some functions to return slightly incorrect results due to lower precision operations being performed.

`LIBRAPID_NATIVE_ARCH`#

DEFAULT: ON

Enabling this flag compiles librapid with the most advanced instruction set available on the system. This can lead to significant performance boosts, but may cause the library to be incompatible with older systems.

Compiling with this flag may also cause the binaries to be incompatible with other CPU architectures, so be careful
when distributing your programs.

`LIBRAPID_CUDA_FLOAT_VECTOR_WIDTH` and `LIBRAPID_CUDA_DOUBLE_VECTOR_WIDTH`#

DEFAULT: 4

Set the default vector width for SIMD CUDA kernels. This must be in the range \([1, 4]\). Higher values will lead to better performance in most cases, but can increase register pressure which may lead to lower performance than expected. For optimal performance, you should try changing this value to suit your specific use case.

Warning

This setting requires CUDA support to be enabled.

`LIBRAPID_NO_WINDOWS_H`#

DEFAULT: OFF

Prevent the inclusion of windows.h in LibRapid’s headers. Sometimes the macros and functions defined in this header can cause conflicts with other libraries, so this option is provided to prevent this.

Danger

It is not possible to fully remove windows.h when compiling with CUDA support on Windows, but many of the modules are still disabled. There is a possiblity that conflicts will still arise, but I am yet to encounter any.

`LIBRAPID_MKL_CONFIG_PATH`#

DEFAULT: ""

If you have Intel’s OneAPI Math Kernel Library installed on your system, you can provide the path to the MKLConfig.cmake file here. This will force LibRapid to link with MKL and ignore any other BLAS libraries. On systems with Intel CPUs, this can result in a significant performance boost.

API Reference#

Important

This list is INCOMPLETE! If you think something is missing, try searching for it first. If you still can’t find it, please open an issue on the LibRapid GitHub repository.

Arrays, Matrices and Linear Algebra

Multidimensional arrays, matrices, linear algebra and more.

Machine Learning

Machine learning in LibRapid.

Vectors

Fixed-size vectors and supported operations.

Complex Numbers

Complex numbers and their operations.

Sets

Mathematical sets.

Bitset

Optimised arrays of Boolean values with support for bitwise operations.

Maps

Maps and dictionary-like objects.

Mathematics

General mathematical operations that work on most data types.

Multi-Precision Arithmetic

Arbitrary-precision integers, floating points and rationals.

Utilities

Utility functions and classes to support development.

Topics and Usage Examples#

Array Iterators#

LibRapid provides many methods to iterate over the elements of an array. Each one has its own advantages and disadvantages, and the best one to use depends heavily upon the situation.

Implicit Iteration#

This is the simplest and easiest way to iterate over an array, but is also the slowest. This method should only be used when performance is not a concern or when the array is known to be relatively small.

auto a = lrc::Array<int>(lrc::Shape({4, 5}));

for (auto val : a) {
    for (auto val2 : val) {
        val2 = lrc::randint(1, 10);
    }
}

for (const auto &val : a) {
    for (const auto &val2 : val) {
        fmt::print("{} ", val2);
    }
    fmt::print("\n");
}

Warning

Due to the way LibRapid works internally, the iterator type returned by Array::begin() and Array::end() makes use of the GeneralArrayView class. Since this is not a direct C++ reference many IDEs will claim that the value is unused and will suggest removing it. Do not remove it! The GeneralArrayView is still referencing the original array and your data will still be updated correctly :)

Keep in mind that this issue only comes up when you’re using the non-const iterator, which is when you’re assigning to the iterator.

I am currently looking into ways to fix this issue, but it is proving to be quite difficult…

Subscript Iteration#

This method of iterating over an array is slightly faster than implicit iteration, but is still slow compared to other methods. This involves using a for loop to iterate over each axis of the array and then using the operator[] to access the elements.

auto a = lrc::Array<int>(lrc::Shape({4, 5}));

for (auto i = 0; i < a.shape()[0]; i++) {
    for (auto j = 0; j < a.shape()[1]; j++) {
        a[i][j] = lrc::randint(1, 10);
    }
}

for (auto i = 0; i < a.shape()[0]; i++) {
    for (auto j = 0; j < a.shape()[1]; j++) {
        fmt::print("{} ", a[i][j]);
    }
    fmt::print("\n");
}

Direct Iteration#

This approach is the fastest safe way to iterate over an array. Again, using a for loop to iterate over each axis of the array, but this time using the operator() method to access the elements.

This method is much faster than using the operator[] method because no temporary GeneralArrayView objects are created.

auto a = lrc::Array<int>(lrc::Shape({4, 5}));

for (auto i = 0; i < a.shape()[0]; i++) {
    for (auto j = 0; j < a.shape()[1]; j++) {
        a(i, j) = lrc::randint(1, 10);
    }
}

for (auto i = 0; i < a.shape()[0]; i++) {
    for (auto j = 0; j < a.shape()[1]; j++) {
        fmt::print("{} ", a(i, j));
    }
    fmt::print("\n");
}

Direct Storage Access#

LibRapid’s array types have a Storage object which stores the actual data of the array. This object can be accessed via the Array::storage() method. This method is the fastest way to iterate over an array, but it is also the most dangerous, and you should only use it if you know what you are doing.

Danger

This method only works on ArrayContainer instances (Array types which own their own data). If you try to use this approach on any other datatype, such as an GeneralArrayView or Function, your code will not compile because these types do not store their own data and hence do not have a storage() method.

Note also that this does not give any information about the shape of the array, so you must be careful to ensure that you are accessing the correct elements.

auto a = lrc::Array<int>(lrc::Shape({4, 5}));

for (auto i = 0; i < a.shape().size(); i++) {
	a.storage()[i] = lrc::randint(1, 10);
}

for (auto i = 0; i < a.shape().size(); i++) {
    fmt::print("{} ", a.storage()[i]);
}

Warning

The Storage object stores the data in row-major order, so you must be careful that you are accessing the correct elements.

For example, if you have a 3D array with shape {2, 3, 4}, the elements will be accessed in the following order:

(0, 0, 0)
(0, 0, 1)
(0, 1, 0)
(0, 1, 1)
(0, 2, 0)
(0, 2, 1)
(1, 0, 0)
(1, 0, 1)
(1, 1, 0)
(1, 1, 1)
(1, 2, 0)
(1, 2, 1)

Benchmarks#

These benchmarks were performed on a Ryzen 9 3950x CPU with 64GB of RAM. The code used is included below.

\(25000 \times 25000\) array of floats#

MSVC#

Iterator Timer [     ITERATOR     ] -- Elapsed: 1.25978m | Average: 25.19570s
Iterator Timer [ FOR LOOP INDEXED ] -- Elapsed: 1.06851m | Average: 10.68511s
Iterator Timer [ FOR LOOP DIRECT  ] -- Elapsed: 1.03243m | Average: 2.13607s
Iterator Timer [     STORAGE      ] -- Elapsed: 1.00972m | Average: 712.74672ms

GCC (WSL2)#

Iterator Timer [     ITERATOR     ] -- Elapsed: 1.30497m | Average: 26.09936s
Iterator Timer [ FOR LOOP INDEXED ] -- Elapsed: 1.00171m | Average: 12.02046s
Iterator Timer [ FOR LOOP DIRECT  ] -- Elapsed: 1.00257m | Average: 222.79388ms
Iterator Timer [     STORAGE      ] -- Elapsed: 1.00265m | Average: 268.56730ms

\(1000 \times 1000\) array of floats#

MSVC#

Iterator Timer [     ITERATOR     ] -- Elapsed: 20.03113s | Average: 60.51699ms
Iterator Timer [ FOR LOOP INDEXED ] -- Elapsed: 20.01374s | Average: 20.56911ms
Iterator Timer [ FOR LOOP DIRECT  ] -- Elapsed: 20.00305s | Average: 3.65019ms
Iterator Timer [     STORAGE      ] -- Elapsed: 20.00049s | Average: 1.45257ms

GCC (WSL2)#

Iterator Timer [     ITERATOR     ] -- Elapsed: 20.03222s | Average: 75.30909ms
Iterator Timer [ FOR LOOP INDEXED ] -- Elapsed: 20.00276s | Average: 23.67190ms
Iterator Timer [ FOR LOOP DIRECT  ] -- Elapsed: 20.00003s | Average: 62.70073us
Iterator Timer [     STORAGE      ] -- Elapsed: 20.00014s | Average: 242.00937us

\(100 \times 100\) array of floats#

MSVC#

Iterator Timer [     ITERATOR     ] -- Elapsed: 10.00005s | Average: 594.18031us
Iterator Timer [ FOR LOOP INDEXED ] -- Elapsed: 10.00007s | Average: 210.48345us
Iterator Timer [ FOR LOOP DIRECT  ] -- Elapsed: 10.00003s | Average: 14.38816us
Iterator Timer [     STORAGE      ] -- Elapsed: 10.00001s | Average: 14.94997us

GCC (WSL2)#

Iterator Timer [     ITERATOR     ] -- Elapsed: 10.00055s | Average: 621.22918us
Iterator Timer [ FOR LOOP INDEXED ] -- Elapsed: 10.00001s | Average: 235.57702us
Iterator Timer [ FOR LOOP DIRECT  ] -- Elapsed: 10.00000s | Average: 650.03031ns
Iterator Timer [     STORAGE      ] -- Elapsed: 10.00000s | Average: 2.44980us

Code#

lrc::Shape benchShape({25000, 25000});

{
    auto a = lrc::Array<float>(benchShape);
    lrc::Timer iteratorTimer(fmt::format("Iterator Timer [ {:^16} ]", "ITERATOR"));
    iteratorTimer.setTargetTime(10);

    while (iteratorTimer.isRunning()) {
        for (auto val : a) {
            for (auto val2 : val) { val2 = 1; }
        }
    }

    fmt::print("{:.5f}\n", iteratorTimer);
}

{
    auto a = lrc::Array<float>(benchShape);
    lrc::Timer iteratorTimer(fmt::format("Iterator Timer [ {:^16} ]", "FOR LOOP INDEXED"));
    iteratorTimer.setTargetTime(10);

    while (iteratorTimer.isRunning()) {
        for (int64_t i = 0; i < a.shape()[0]; i++) {
            for (int64_t j = 0; j < a.shape()[1]; j++) { a[i][j] = 1; }
        }
    }

    fmt::print("{:.5f}\n", iteratorTimer);
}

{
    auto a = lrc::Array<float>(benchShape);
    lrc::Timer iteratorTimer(fmt::format("Iterator Timer [ {:^16} ]", "FOR LOOP DIRECT"));
    iteratorTimer.setTargetTime(10);

    while (iteratorTimer.isRunning()) {
        for (int64_t i = 0; i < a.shape()[0]; i++) {
            for (int64_t j = 0; j < a.shape()[1]; j++) { a(i, j) = 1; }
        }
    }

    fmt::print("{:.5f}\n", iteratorTimer);
}

{
    auto a = lrc::Array<float>(benchShape);
    lrc::Timer iteratorTimer(fmt::format("Iterator Timer [ {:^16} ]", "STORAGE"));
    iteratorTimer.setTargetTime(10);

    while (iteratorTimer.isRunning()) {
        for (int64_t i = 0; i < a.shape().size(); i++) { a.storage()[i] = 1; }
    }

    fmt::print("{:.5f}\n", iteratorTimer);
}

Arrays, Matrices and Linear Algebra#

The main feature of LibRapid is its high-performance array library. It provides an intuitive way to perform highly efficient operations on arrays and matrices in C++.

Linear Algebra#

Level 1 (Vector-Vector)#

Level 2 (Matrix-Vector)#

GEMV#

namespace librapid

namespace linalg#

Functions

template<typename Int, typename Alpha, typename A, typename X, typename Beta, typename Y> void gemv(bool trans, Int m, Int n, Alpha alpha, A *a, Int lda, X *x, Int incX, Beta beta, Y *y, Int incY, backend::CPU backend = backend::CPU())

General matrix-vector multiplication.

Computes \( y = \alpha \mathrm{op}(\mathbf{A}) \mathbf{x} + \beta \mathbf{y} \) for matrix \( \mathbf{A} \) and vectors \( \mathbf{x} \) and \( \mathbf{y} \)

Template Parameters

Int – Integer type
Alpha – Alpha scaling factor
A – Matrix type
X – First vector type
Beta – Beta scaling factor
Y – Second vector type

Parameters

trans – If true, \( \mathrm{op}(\mathbf{A}) = \mathbf{A}^T \), otherwise \( \mathrm{op}(\mathbf{A}) = \mathbf{A} \)
m – Number of rows in \( \mathbf{A} \)
n – Number of columns in \( \mathbf{A} \)
alpha – Scaling factor for \( \mathrm{op}(\mathbf{A}) \mathbf{x} \)
a – Pointer to matrix \( \mathbf{A} \)
lda – Leading dimension of \( \mathbf{A} \)
x – Pointer to vector \( \mathbf{x} \)
incX – Increment of \( \mathbf{x} \)
beta – Scaling factor for \( \mathbf{y} \)
y – Pointer to vector \( \mathbf{y} \)
incY – Increment of \( \mathbf{y} \)
backend – Backend to use for computation

template<typename Int, typename Alpha, typename A, typename X, typename Beta, typename Y> void gemv(bool trans, Int m, Int n, Alpha alpha, A *a, Int lda, X *x, Int incX, Beta beta, Y *y, Int incY, backend::CUDA)

Level 3 (Matrix-Matrix)#

GEMM#

namespace librapid

namespace linalg

Functions

template<typename Int, typename Alpha, typename A, typename B, typename Beta, typename C> void gemm(bool transA, bool transB, Int m, Int n, Int k, Alpha alpha, A *a, Int lda, B *b, Int ldb, Beta beta, C *c, Int ldc, backend::CPU backend = backend::CPU())

General matrix-matrix multiplication.

Computes \( \mathbf{C} = \alpha \mathrm{OP}_A(\mathbf{A}) \mathrm{OP}_B(\mathbf{B}) + \beta \mathbf{C} \) for matrices \( \mathbf{A} \), \( \mathbf{B} \) and \( \mathbf{C} \). \( \mathrm{OP}_A \) and \( \mathrm{OP}_B \) are either the identity or the transpose operation.

Template Parameters

Int – Integer type for matrix dimensions
Alpha – Type of \( \alpha \)
A – Type of \( \mathbf{A} \)
B – Type of \( \mathbf{B} \)
Beta – Type of \( \beta \)
C – Type of \( \mathbf{C} \)

Parameters

transA – Whether to transpose \( \mathbf{A} \) (determines \( \mathrm{OP}_A \))
transB – Whether to transpose \( \mathbf{B} \) (determines \( \mathrm{OP}_B \))
m – Rows of \( \mathbf{A} \) and \( \mathbf{C} \)
n – Columns of \( \mathbf{B} \) and \( \mathbf{C} \)
k – Columns of \( \mathbf{A} \) and rows of \( \mathbf{B} \)
alpha – Scalar \( \alpha \)
a – Pointer to \( \mathbf{A} \)
lda – Leading dimension of \( \mathbf{A} \)
b – Pointer to \( \mathbf{B} \)
ldb – Leading dimension of \( \mathbf{B} \)
beta – Scalar \( \beta \)
c – Pointer to \( \mathbf{C} \)
ldc – Leading dimension of \( \mathbf{C} \)
backend – Backend to use for computation

CuBLASGemmComputeType cublasGemmComputeType(cublasDataType_t a, cublasDataType_t b, cublasDataType_t c)

template<typename Int, typename Alpha, typename A, typename B, typename Beta, typename C> void gemm(bool transA, bool transB, Int m, Int n, Int k, Alpha alpha, A *a, Int lda, B *b, Int ldb, Beta beta, C *c, Int ldc, backend::CUDA)

struct CuBLASGemmComputeType

#include <gemm.hpp>

Public Members

cublasComputeType_t computeType

cublasDataType_t scaleType

Array Class Listing#

Defines

SINIT(SUB_TYPE)

SVEC(SUB_TYPE)

ARRAY_FROM_DATA_DEF(TYPE_INIT, TYPE_VEC)

Functions

ARRAY_TYPE_FMT_IML (typename ShapeType_ COMMA typename StorageType_, librapid::array::ArrayContainer< ShapeType_ COMMA StorageType_ >) LIBRAPID_SIMPLE_IO_NORANGE(typename ShapeType_ COMMA typename StorageType_

template<typename ShapeType_, typename StorageType_> struct TypeInfo<array::ArrayContainer<ShapeType_, StorageType_>>

#include <arrayContainer.hpp>

Public Types

using Scalar = typename TypeInfo<StorageType_>::Scalar

using Packet = std::false_type

using Backend = typename TypeInfo<StorageType_>::Backend

using ShapeType = ShapeType_

using StorageType = StorageType_

Public Static Attributes

static constexpr detail::LibRapidType type = detail::LibRapidType::ArrayContainer

static constexpr int64_t packetWidth = 1

static constexpr bool supportsArithmetic = TypeInfo<Scalar>::supportsArithmetic

static constexpr bool supportsLogical = TypeInfo<Scalar>::supportsLogical

static constexpr bool supportsBinary = TypeInfo<Scalar>::supportsBinary

static constexpr bool allowVectorisation = []() {if constexpr (typetraits::HasAllowVectorisation<TypeInfo<Scalar>>::value) {returnTypeInfo<Scalar>::allowVectorisation;} else {returnTypeInfo<Scalar>::packetWidth> 1;}}()

static constexpr cudaDataType_t CudaType = TypeInfo<Scalar>::CudaType

static constexpr int64_t cudaPacketWidth = 1

static constexpr bool canAlign = false

static constexpr int64_t canMemcpy = false

template<typename T, typename S> struct IsArrayType<array::GeneralArrayView<T, S>>

Public Static Attributes

static constexpr bool val = true

namespace librapid

namespace array#

template<typename ShapeType_, typename StorageType_> class ArrayContainer

#include <arrayContainer.hpp>

Public Types

using StorageType = StorageType_

using ShapeType = ShapeType_

using StrideType = Stride<ShapeType>

using SizeType = typename ShapeType::SizeType

using Scalar = typename StorageType::Scalar

using Packet = typename typetraits::TypeInfo<Scalar>::Packet

using Backend = typename typetraits::TypeInfo<ArrayContainer>::Backend

using DirectSubscriptType = typename detail::SubscriptType<StorageType>::Direct

using DirectRefSubscriptType = typename detail::SubscriptType<StorageType>::Ref

Public Functions

ArrayContainer(): Default constructor.

ArrayContainer(const std::initializer_list<Scalar> &data)

explicit ArrayContainer(const std::vector<Scalar> &data)

ArrayContainer(const std::initializer_list<std::initializer_list<Scalar>> &data)

explicit ArrayContainer(const std::vector<std::vector<Scalar>> &data)

ArrayContainer(const std::initializer_list<std::initializer_list<std::initializer_list<Scalar>>> &data)

explicit ArrayContainer(const std::vector<std::vector<std::vector<Scalar>>> &data)

ArrayContainer(const std::initializer_list<std::initializer_list<std::initializer_list<std::initializer_list<Scalar>>>> &data)

explicit ArrayContainer(const std::vector<std::vector<std::vector<std::vector<Scalar>>>> &data)

ArrayContainer(const std::initializer_list<std::initializer_list<std::initializer_list<std::initializer_list<std::initializer_list<Scalar>>>>> &data)

explicit ArrayContainer(const std::vector<std::vector<std::vector<std::vector<std::vector<Scalar>>>>> &data)

ArrayContainer(const std::initializer_list<std::initializer_list<std::initializer_list<std::initializer_list<std::initializer_list<std::initializer_list<Scalar>>>>>> &data)

explicit ArrayContainer(const std::vector<std::vector<std::vector<std::vector<std::vector<std::vector<Scalar>>>>>> &data)

ArrayContainer(const std::initializer_list<std::initializer_list<std::initializer_list<std::initializer_list<std::initializer_list<std::initializer_list<std::initializer_list<Scalar>>>>>>> &data)

explicit ArrayContainer(const std::vector<std::vector<std::vector<std::vector<std::vector<std::vector<std::vector<Scalar>>>>>>> &data)

ArrayContainer(const std::initializer_list<std::initializer_list<std::initializer_list<std::initializer_list<std::initializer_list<std::initializer_list<std::initializer_list<std::initializer_list<Scalar>>>>>>>> &data)

explicit ArrayContainer(const std::vector<std::vector<std::vector<std::vector<std::vector<std::vector<std::vector<std::vector<Scalar>>>>>>>> &data)

explicit ArrayContainer(const Shape &shape)

Constructs an array container from a shape

Parameters: shape – The shape of the array container

explicit ArrayContainer(const MatrixShape &shape)

explicit ArrayContainer(const VectorShape &shape)

ArrayContainer(const Shape &shape, const Scalar &value)

Create an array container from a shape and a scalar value. The scalar value represents the value the memory is initialized with.

Parameters

shape – The shape of the array container
value – The value to initialize the memory with

ArrayContainer(const MatrixShape &shape, const Scalar &value)

ArrayContainer(const VectorShape &shape, const Scalar &value)

explicit ArrayContainer(const Scalar &value)

Allows for a fixed-size array to be constructed with a fill value

Parameters: value – The value to fill the array with

explicit ArrayContainer(ShapeType &&shape)

Construct an array container from a shape, which is moved, not copied.

Parameters: shape – The shape of the array container

ArrayContainer(const ArrayContainer &other) = default

Reference an existing array container.

This constructor does not copy the data, but instead references the data of the input array container. This means that the input array container must outlive the constructed array container. Please use ArrayContainer::copy() if you want to copy the data.

Parameters: other – The array container to reference

ArrayContainer(ArrayContainer &&other) noexcept = default

Construct an array container from a temporary array container.

Parameters: other – The array container to move.

template<typename TransposeType> ArrayContainer(const Transpose<TransposeType> &trans)

template<typename ShapeTypeA, typename StorageTypeA, typename ShapeTypeB, typename StorageTypeB, typename Alpha, typename Beta> ArrayContainer(const linalg::ArrayMultiply<ShapeTypeA, StorageTypeA, ShapeTypeB, StorageTypeB, Alpha, Beta> &multiply)

template<typename desc, typename Functor_, typename ...Args> ArrayContainer &assign(const detail::Function<desc, Functor_, Args...> &function)

template<typename desc, typename Functor_, typename ...Args> ArrayContainer(const detail::Function<desc, Functor_, Args...> &function)

Construct an array container from a function object. This will assign the result of the function to the array container, evaluating it accordingly.

Template Parameters

desc – The assignment descriptor
Functor_ – The function type
Args – The argument types of the function

Parameters

function – The function to assign

ArrayContainer &operator=(const ArrayContainer &other) = default

Reference an existing array container.

This assignment operator does not copy the data, but instead references the data of the input array container. This means that the input array container must outlive the constructed array container. Please use ArrayContainer::copy() if you want to copy the data.

Parameters: other – The array container to reference

template<typename ArrayViewType, typename ArrayViewScalar> ArrayContainer &operator=(const array::GeneralArrayView<ArrayViewType, ArrayViewScalar> &view)

ArrayContainer &operator=(const Scalar &value)

ArrayContainer &operator=(ArrayContainer &&other) noexcept = default

Assign a temporary array container to this array container.

Parameters: other – The array container to move.
Returns: A reference to this array container.

template<typename desc, typename Functor_, typename ...Args> ArrayContainer &operator=(const detail::Function<desc, Functor_, Args...> &function)

Assign a function object to this array container. This will assign the result of the function to the array container, evaluating it accordingly.

Template Parameters

Functor_ – The function type
Args – The argument types of the function

Parameters

function – The function to assign

Returns

A reference to this array container.

template<typename TransposeType> ArrayContainer &operator=(const Transpose<TransposeType> &transpose)

template<typename ShapeTypeA, typename StorageTypeA, typename ShapeTypeB, typename StorageTypeB, typename Alpha, typename Beta> ArrayContainer &operator=(const linalg::ArrayMultiply<ShapeTypeA, StorageTypeA, ShapeTypeB, StorageTypeB, Alpha, Beta> &multiply)

template<typename T> detail::CommaInitializer<ArrayContainer> operator<<(const T &value)

Allow ArrayContainer objects to be initialized with a comma separated list of values. This makes use of the CommaInitializer class

Template Parameters: T – The type of the values
Parameters: value – The value to set in the Array object
Returns: The comma initializer object

ArrayContainer copy() const

auto operator[](int64_t index) const

Access a sub-array of this ArrayContainer instance. The sub-array will reference the same memory as this ArrayContainer instance.

See also

ArrayView

Parameters: index – The index of the sub-array
Returns: A reference to the sub-array (ArrayView)

auto operator[](int64_t index)

template<typename ...Indices> DirectSubscriptType operator()(Indices... indices) const

template<typename ...Indices> DirectRefSubscriptType operator()(Indices... indices)

Scalar get() const

ShapeType::SizeType ndim() const noexcept

Return the number of dimensions of the ArrayContainer object

Returns: Number of dimensions of the ArrayContainer

auto size() const noexcept -> size_t

const ShapeType &shape() const noexcept

Return the shape of the array container. This is an immutable reference.

Returns: The shape of the array container.

const StorageType &storage() const noexcept

Return the StorageType object of the ArrayContainer

Returns: The StorageType object of the ArrayContainer

StorageType &storage() noexcept

Return the StorageType object of the ArrayContainer

Returns: The StorageType object of the ArrayContainer

Packet packet(size_t index) const

Return a Packet object from the array’s storage at a specific index.

Parameters: index – The index to get the packet from
Returns: A Packet object from the array’s storage at a specific index

Scalar scalar(size_t index) const

Return a Scalar from the array’s storage at a specific index.

Parameters: index – The index to get the scalar from
Returns: A Scalar from the array’s storage at a specific index

void writePacket(size_t index, const Packet &value)

Write a Packet object to the array’s storage at a specific index

Parameters

index – The index to write the packet to
value – The value to write to the array’s storage

void write(size_t index, const Scalar &value)

Write a Scalar to the array’s storage at a specific index

Parameters

index – The index to write the scalar to
value – The value to write to the array’s storage

template<typename T> ArrayContainer &operator+=(const T &other)

template<typename T> ArrayContainer &operator-=(const T &other)

template<typename T> ArrayContainer &operator*=(const T &other)

template<typename T> ArrayContainer &operator/=(const T &other)

template<typename T> ArrayContainer &operator%=(const T &other)

template<typename T> ArrayContainer &operator&=(const T &other)

template<typename T> ArrayContainer &operator|=(const T &other)

template<typename T> ArrayContainer &operator^=(const T &other)

template<typename T> ArrayContainer &operator<<=(const T &other)

template<typename T> ArrayContainer &operator>>=(const T &other)

auto begin() const noexcept

Return an iterator to the beginning of the array container.

Returns: Iterator

auto end() const noexcept

Return an iterator to the end of the array container.

Returns: Iterator

auto begin()

Return an iterator to the beginning of the array container.

Returns: Iterator

auto end()

Return an iterator to the end of the array container.

Returns: Iterator

template<typename T, typename Char, size_t N, typename Ctx> void str(const fmt::formatter<T, Char> &format, char bracket, char separator, const char (&formatString)[N], Ctx &ctx) const

template<typename desc, typename Functor_, typename ...Args> auto assign(const detail::Function<desc, Functor_, Args...> &function) -> ArrayContainer&

template<typename desc, typename Functor_, typename ...Args> auto operator=(const detail::Function<desc, Functor_, Args...> &function) -> ArrayContainer&

template<typename TransposeType> auto operator=(const Transpose<TransposeType> &transpose) -> ArrayContainer&

template<typename ShapeTypeA, typename StorageTypeA, typename ShapeTypeB, typename StorageTypeB, typename Alpha, typename Beta> auto operator=(const linalg::ArrayMultiply<ShapeTypeA, StorageTypeA, ShapeTypeB, StorageTypeB, Alpha, Beta> &arrayMultiply) -> ArrayContainer&

template<typename ArrayViewType, typename ArrayViewScalar> auto operator=(const array::GeneralArrayView<ArrayViewType, ArrayViewScalar> &view) -> ArrayContainer&

template<typename T> auto operator<<(const T &value) -> detail::CommaInitializer<ArrayContainer>

template<typename ...Indices> auto operator()(Indices... indices) const -> DirectSubscriptType

template<typename ...Indices> auto operator()(Indices... indices) -> DirectRefSubscriptType

template<typename T> auto operator+=(const T &value) -> ArrayContainer&

template<typename T> auto operator-=(const T &value) -> ArrayContainer&

template<typename T> auto operator*=(const T &value) -> ArrayContainer&

template<typename T> auto operator/=(const T &value) -> ArrayContainer&

template<typename T> auto operator%=(const T &value) -> ArrayContainer&

template<typename T> auto operator&=(const T &value) -> ArrayContainer&

template<typename T> auto operator|=(const T &value) -> ArrayContainer&

template<typename T> auto operator^=(const T &value) -> ArrayContainer&

template<typename T> auto operator<<=(const T &value) -> ArrayContainer&

template<typename T> auto operator>>=(const T &value) -> ArrayContainer&

Private Members

ShapeType m_shape#

size_t m_size#

StorageType m_storage#

namespace detail

template<typename First, typename ...Types> struct ContainsArrayType

Public Static Functions

static inline constexpr auto evaluator()

Public Static Attributes

static constexpr bool val = evaluator()

template<typename T> struct IsArrayType

Public Static Attributes

static constexpr bool val = false

template<typename T, typename S> GeneralArrayView< T, S > >

Public Static Attributes

static constexpr bool val = true#

template<typename T, typename V> struct IsArrayType<ArrayRef<T, V>>

Public Static Attributes

static constexpr bool val = true

template<typename ...T> struct IsArrayType<FunctionRef<T...>>

Public Static Attributes

static constexpr bool val = true

template<typename T> struct SubscriptType

#include <arrayContainer.hpp>

Public Types

using Scalar = T

using Direct = const Scalar&

using Ref = Scalar&

template<typename T> struct SubscriptType<CudaStorage<T>>

#include <arrayContainer.hpp>

Public Types

using Scalar = T

using Direct = const detail::CudaRef<Scalar>

using Ref = detail::CudaRef<Scalar>

template<typename T, size_t... Dims> struct SubscriptType<FixedStorage<T, Dims...>>

#include <arrayContainer.hpp>

Public Types

using Scalar = T

using Direct = const Scalar&

using Ref = Scalar&

template<typename T> struct SubscriptType<Storage<T>>

#include <arrayContainer.hpp>

Public Types

using Scalar = T

using Direct = const Scalar&

using Ref = Scalar&

namespace typetraits#

Functions

LIBRAPID_DEFINE_AS_TYPE (typename StorageScalar, array::ArrayContainer< Shape COMMA StorageScalar >)

LIBRAPID_DEFINE_AS_TYPE (typename StorageScalar, array::ArrayContainer< MatrixShape COMMA StorageScalar >)

template<typename ShapeType_, typename StorageType_> ArrayContainer< ShapeType_, StorageType_ > >

#include <arrayContainer.hpp>

Public Types

using Scalar = typename TypeInfo<StorageType_>::Scalar#

using Packet = std::false_type#

using Backend = typename TypeInfo<StorageType_>::Backend#

using ShapeType = ShapeType_#

using StorageType = StorageType_#

Public Static Attributes

static constexpr detail::LibRapidType type = detail::LibRapidType::ArrayContainer #

static constexpr int64_t packetWidth = 1#

static constexpr bool supportsArithmetic = TypeInfo<Scalar>::supportsArithmetic#

static constexpr bool supportsLogical = TypeInfo<Scalar>::supportsLogical#

static constexpr bool supportsBinary = TypeInfo<Scalar>::supportsBinary#

static constexpr bool allowVectorisation = []() {if constexpr (typetraits::HasAllowVectorisation<TypeInfo<Scalar>>::value) {returnTypeInfo<Scalar>::allowVectorisation;} else {returnTypeInfo<Scalar>::packetWidth> 1;}}()#

static constexpr cudaDataType_t CudaType = TypeInfo<Scalar>::CudaType#

static constexpr int64_t cudaPacketWidth = 1#

static constexpr bool canAlign = false#

static constexpr int64_t canMemcpy = false#

Array From Data#

Defines

HIGHER_DIMENSIONAL_FROM_DATA(TYPE)

SINIT(SUB_TYPE)

SVEC(SUB_TYPE)

namespace librapid

namespace array

Pseudoconstructors#

Warning

doxygenfile: Cannot find file “librapid/include/librapid/array/pseudoconstructors.hpp

Array View#

Warning

doxygenfile: Cannot find file “librapid/include/librapid/array/arrayView.hpp

Array Operations#

Defines

LIBRAPID_BINARY_FUNCTOR(NAME_, OP_)

LIBRAPID_BINARY_COMPARISON_FUNCTOR(NAME_, OP_)

LIBRAPID_UNARY_KERNEL_GETTER

LIBRAPID_BINARY_KERNEL_GETTER

LIBRAPID_UNARY_SHAPE_EXTRACTOR

LIBRAPID_BINARY_SHAPE_EXTRACTOR

LIBRAPID_UNARY_FUNCTOR(NAME, OP)

IS_ARRAY_OP

IS_ARRAY_OP_ARRAY

IS_ARRAY_OP_WITH_SCALAR

template<typename ShapeType, typename StorageType> struct DescriptorExtractor<array::ArrayContainer<ShapeType, StorageType>>

#include <operations.hpp>

Extracts the Descriptor type of an ArrayContainer object. In this case, the Descriptor is Trivial

Template Parameters

ShapeType – The shape type of the ArrayContainer
StorageType – The storage type of the ArrayContainer

Public Types

using Type = ::librapid::detail::descriptor::Trivial

template<typename T, typename S> struct DescriptorExtractor<array::GeneralArrayView<T, S>>

#include <operations.hpp>

Extracts the Descriptor type of an ArrayView object

Template Parameters: T – The Array type of the ArrayView

Public Types

using Type = ::librapid::detail::descriptor::Trivial

template<typename Descriptor, typename Functor, typename ...Args> struct DescriptorExtractor<::librapid::detail::Function<Descriptor, Functor, Args...>>

#include <operations.hpp>

Extracts the Descriptor type of a Function object

Template Parameters

Descriptor – The descriptor of the Function
Functor – The functor type of the Function
Args – The argument types of the Function

Public Types

using Type = Descriptor

template<> struct TypeInfo<::librapid::detail::Plus>

#include <operations.hpp>

Public Static Functions

template<typename T1, typename T2> static inline constexpr const char *getKernelNameImpl(std::tuple<T1, T2> args)

template<typename ...Args> static inline constexpr const char *getKernelName(std::tuple<Args...> args)

template<typename First, typename Second> static inline auto getShapeImpl(const std::tuple<First, Second> &tup)

template<typename ...Args> static inline auto getShape(const std::tuple<Args...> &args)

Public Static Attributes

static constexpr const char *name = "plus"

static constexpr const char *filename = "arithmetic"

static constexpr const char *kernelName = "addArrays"

static constexpr const char *kernelNameScalarRhs = "addArraysScalarRhs"

static constexpr const char *kernelNameScalarLhs = "addArraysScalarLhs"

template<> struct TypeInfo<::librapid::detail::Minus>

#include <operations.hpp>

Public Static Functions

template<typename T1, typename T2> static inline constexpr const char *getKernelNameImpl(std::tuple<T1, T2> args)

template<typename ...Args> static inline constexpr const char *getKernelName(std::tuple<Args...> args)

template<typename First, typename Second> static inline auto getShapeImpl(const std::tuple<First, Second> &tup)

template<typename ...Args> static inline auto getShape(const std::tuple<Args...> &args)

Public Static Attributes

static constexpr const char *name = "minus"

static constexpr const char *filename = "arithmetic"

static constexpr const char *kernelName = "subArrays"

static constexpr const char *kernelNameScalarRhs = "subArraysScalarRhs"

static constexpr const char *kernelNameScalarLhs = "subArraysScalarLhs"

template<> struct TypeInfo<::librapid::detail::Multiply>

#include <operations.hpp>

Public Static Functions

template<typename T1, typename T2> static inline constexpr const char *getKernelNameImpl(std::tuple<T1, T2> args)

template<typename ...Args> static inline constexpr const char *getKernelName(std::tuple<Args...> args)

template<typename First, typename Second> static inline auto getShapeImpl(const std::tuple<First, Second> &tup)

template<typename ...Args> static inline auto getShape(const std::tuple<Args...> &args)

Public Static Attributes

static constexpr const char *name = "multiply"

static constexpr const char *filename = "arithmetic"

static constexpr const char *kernelName = "mulArrays"

static constexpr const char *kernelNameScalarRhs = "mulArraysScalarRhs"

static constexpr const char *kernelNameScalarLhs = "mulArraysScalarLhs"

template<> struct TypeInfo<::librapid::detail::Divide>

#include <operations.hpp>

Public Static Functions

template<typename T1, typename T2> static inline constexpr const char *getKernelNameImpl(std::tuple<T1, T2> args)

template<typename ...Args> static inline constexpr const char *getKernelName(std::tuple<Args...> args)

template<typename First, typename Second> static inline auto getShapeImpl(const std::tuple<First, Second> &tup)

template<typename ...Args> static inline auto getShape(const std::tuple<Args...> &args)

Public Static Attributes

static constexpr const char *name = "divide"

static constexpr const char *filename = "arithmetic"

static constexpr const char *kernelName = "divArrays"

static constexpr const char *kernelNameScalarRhs = "divArraysScalarRhs"

static constexpr const char *kernelNameScalarLhs = "divArraysScalarLhs"

template<> struct TypeInfo<::librapid::detail::LessThan>

#include <operations.hpp>

Public Static Functions

template<typename T1, typename T2> static inline constexpr const char *getKernelNameImpl(std::tuple<T1, T2> args)

template<typename ...Args> static inline constexpr const char *getKernelName(std::tuple<Args...> args)

template<typename First, typename Second> static inline auto getShapeImpl(const std::tuple<First, Second> &tup)

template<typename ...Args> static inline auto getShape(const std::tuple<Args...> &args)

Public Static Attributes

static constexpr const char *name = "less than"

static constexpr const char *filename = "arithmetic"

static constexpr const char *kernelName = "lessThanArrays"

static constexpr const char *kernelNameScalarRhs = "lessThanArraysScalarRhs"

static constexpr const char *kernelNameScalarLhs = "lessThanArraysScalarLhs"

template<> struct TypeInfo<::librapid::detail::GreaterThan>

#include <operations.hpp>

Public Static Functions

template<typename T1, typename T2> static inline constexpr const char *getKernelNameImpl(std::tuple<T1, T2> args)

template<typename ...Args> static inline constexpr const char *getKernelName(std::tuple<Args...> args)

template<typename First, typename Second> static inline auto getShapeImpl(const std::tuple<First, Second> &tup)

template<typename ...Args> static inline auto getShape(const std::tuple<Args...> &args)

Public Static Attributes

static constexpr const char *name = "greater than"

static constexpr const char *filename = "arithmetic"

static constexpr const char *kernelName = "greaterThanArrays"

static constexpr const char *kernelNameScalarRhs = "greaterThanArraysScalarRhs"

static constexpr const char *kernelNameScalarLhs = "greaterThanArraysScalarLhs"

template<> struct TypeInfo<::librapid::detail::LessThanEqual>

#include <operations.hpp>

Public Static Functions

template<typename T1, typename T2> static inline constexpr const char *getKernelNameImpl(std::tuple<T1, T2> args)

template<typename ...Args> static inline constexpr const char *getKernelName(std::tuple<Args...> args)

template<typename First, typename Second> static inline auto getShapeImpl(const std::tuple<First, Second> &tup)

template<typename ...Args> static inline auto getShape(const std::tuple<Args...> &args)

Public Static Attributes

static constexpr const char *name = "less than or equal"

static constexpr const char *filename = "arithmetic"

static constexpr const char *kernelName = "lessThanEqualArrays"

static constexpr const char *kernelNameScalarRhs = "lessThanEqualArraysScalarRhs"

static constexpr const char *kernelNameScalarLhs = "lessThanEqualArraysScalarLhs"

template<> struct TypeInfo<::librapid::detail::GreaterThanEqual>

#include <operations.hpp>

Public Static Functions

template<typename T1, typename T2> static inline constexpr const char *getKernelNameImpl(std::tuple<T1, T2> args)

template<typename ...Args> static inline constexpr const char *getKernelName(std::tuple<Args...> args)

template<typename First, typename Second> static inline auto getShapeImpl(const std::tuple<First, Second> &tup)

template<typename ...Args> static inline auto getShape(const std::tuple<Args...> &args)

Public Static Attributes

static constexpr const char *name = "greater than or equal"

static constexpr const char *filename = "arithmetic"

static constexpr const char *kernelName = "greaterThanEqualArrays"

static constexpr const char *kernelNameScalarRhs = "greaterThanEqualArraysScalarRhs"

static constexpr const char *kernelNameScalarLhs = "greaterThanEqualArraysScalarLhs"

template<> struct TypeInfo<::librapid::detail::ElementWiseEqual>

#include <operations.hpp>

Public Static Functions

template<typename T1, typename T2> static inline constexpr const char *getKernelNameImpl(std::tuple<T1, T2> args)

template<typename ...Args> static inline constexpr const char *getKernelName(std::tuple<Args...> args)

template<typename First, typename Second> static inline auto getShapeImpl(const std::tuple<First, Second> &tup)

template<typename ...Args> static inline auto getShape(const std::tuple<Args...> &args)

Public Static Attributes

static constexpr const char *name = "element wise equal"

static constexpr const char *filename = "arithmetic"

static constexpr const char *kernelName = "elementWiseEqualArrays"

static constexpr const char *kernelNameScalarRhs = "elementWiseEqualArraysScalarRhs"

static constexpr const char *kernelNameScalarLhs = "elementWiseEqualArraysScalarLhs"

template<> struct TypeInfo<::librapid::detail::ElementWiseNotEqual>

#include <operations.hpp>

Public Static Functions

template<typename T1, typename T2> static inline constexpr const char *getKernelNameImpl(std::tuple<T1, T2> args)

template<typename ...Args> static inline constexpr const char *getKernelName(std::tuple<Args...> args)

template<typename First, typename Second> static inline auto getShapeImpl(const std::tuple<First, Second> &tup)

template<typename ...Args> static inline auto getShape(const std::tuple<Args...> &args)

Public Static Attributes

static constexpr const char *name = "element wise not equal"

static constexpr const char *filename = "arithmetic"

static constexpr const char *kernelName = "elementWiseNotEqualArrays"

static constexpr const char *kernelNameScalarRhs = "elementWiseNotEqualArraysScalarRhs"

static constexpr const char *kernelNameScalarLhs = "elementWiseNotEqualArraysScalarLhs"

template<> struct TypeInfo<::librapid::detail::Neg>

#include <operations.hpp>

Public Static Functions

template<typename ...Args> static inline constexpr const char *getKernelName(std::tuple<Args...> args)

template<typename ...Args> static inline auto getShape(const std::tuple<Args...> &args)

Public Static Attributes

static constexpr const char *name = "negate"

static constexpr const char *filename = "negate"

static constexpr const char *kernelName = "negateArrays"

template<> struct TypeInfo<::librapid::detail::Sin>

#include <operations.hpp>

Public Static Functions

template<typename ...Args> static inline constexpr const char *getKernelName(std::tuple<Args...> args)

template<typename ...Args> static inline auto getShape(const std::tuple<Args...> &args)

Public Static Attributes

static constexpr const char *name = "sin"

static constexpr const char *filename = "trigonometry"

static constexpr const char *kernelName = "sinArrays"

template<> struct TypeInfo<::librapid::detail::Cos>

#include <operations.hpp>

Public Static Functions

template<typename ...Args> static inline constexpr const char *getKernelName(std::tuple<Args...> args)

template<typename ...Args> static inline auto getShape(const std::tuple<Args...> &args)

Public Static Attributes

static constexpr const char *name = "cos"

static constexpr const char *filename = "trigonometry"

static constexpr const char *kernelName = "cosArrays"

template<> struct TypeInfo<::librapid::detail::Tan>

#include <operations.hpp>

Public Static Functions

template<typename ...Args> static inline constexpr const char *getKernelName(std::tuple<Args...> args)

template<typename ...Args> static inline auto getShape(const std::tuple<Args...> &args)

Public Static Attributes

static constexpr const char *name = "tan"

static constexpr const char *filename = "trigonometry"

static constexpr const char *kernelName = "tanArrays"

template<> struct TypeInfo<::librapid::detail::Asin>

#include <operations.hpp>

Public Static Functions

template<typename ...Args> static inline constexpr const char *getKernelName(std::tuple<Args...> args)

template<typename ...Args> static inline auto getShape(const std::tuple<Args...> &args)

Public Static Attributes

static constexpr const char *name = "arcsin"

static constexpr const char *filename = "trigonometry"

static constexpr const char *kernelName = "asinArrays"

template<> struct TypeInfo<::librapid::detail::Acos>

#include <operations.hpp>

Public Static Functions

template<typename ...Args> static inline constexpr const char *getKernelName(std::tuple<Args...> args)

template<typename ...Args> static inline auto getShape(const std::tuple<Args...> &args)

Public Static Attributes

static constexpr const char *name = "arcos"

static constexpr const char *filename = "trigonometry"

static constexpr const char *kernelName = "acosArrays"

template<> struct TypeInfo<::librapid::detail::Atan>

#include <operations.hpp>

Public Static Functions

template<typename ...Args> static inline constexpr const char *getKernelName(std::tuple<Args...> args)

template<typename ...Args> static inline auto getShape(const std::tuple<Args...> &args)

Public Static Attributes

static constexpr const char *name = "arctan"

static constexpr const char *filename = "trigonometry"

static constexpr const char *kernelName = "atanArrays"

template<> struct TypeInfo<::librapid::detail::Sinh>

#include <operations.hpp>

Public Static Functions

template<typename ...Args> static inline constexpr const char *getKernelName(std::tuple<Args...> args)

template<typename ...Args> static inline auto getShape(const std::tuple<Args...> &args)

Public Static Attributes

static constexpr const char *name = "hyperbolic sine"

static constexpr const char *filename = "trigonometry"

static constexpr const char *kernelName = "sinhArrays"

template<> struct TypeInfo<::librapid::detail::Cosh>

#include <operations.hpp>

Public Static Functions

template<typename ...Args> static inline constexpr const char *getKernelName(std::tuple<Args...> args)

template<typename ...Args> static inline auto getShape(const std::tuple<Args...> &args)

Public Static Attributes

static constexpr const char *name = "hyperbolic cosine"

static constexpr const char *filename = "trigonometry"

static constexpr const char *kernelName = "coshArrays"

template<> struct TypeInfo<::librapid::detail::Tanh>

#include <operations.hpp>

Public Static Functions

template<typename ...Args> static inline constexpr const char *getKernelName(std::tuple<Args...> args)

template<typename ...Args> static inline auto getShape(const std::tuple<Args...> &args)

Public Static Attributes

static constexpr const char *name = "hyperbolic tangent"

static constexpr const char *filename = "trigonometry"

static constexpr const char *kernelName = "tanhArrays"

template<> struct TypeInfo<::librapid::detail::Asinh>

#include <operations.hpp>

Public Static Functions

template<typename ...Args> static inline constexpr const char *getKernelName(std::tuple<Args...> args)

template<typename ...Args> static inline auto getShape(const std::tuple<Args...> &args)

Public Static Attributes

static constexpr const char *name = "hyperbolic arcsine"

static constexpr const char *filename = "trigonometry"

static constexpr const char *kernelName = "asinhArrays"

template<> struct TypeInfo<::librapid::detail::Acosh>

#include <operations.hpp>

Public Static Functions

template<typename ...Args> static inline constexpr const char *getKernelName(std::tuple<Args...> args)

template<typename ...Args> static inline auto getShape(const std::tuple<Args...> &args)

Public Static Attributes

static constexpr const char *name = "hyperbolic arccosine"

static constexpr const char *filename = "trigonometry"

static constexpr const char *kernelName = "acoshArrays"

template<> struct TypeInfo<::librapid::detail::Atanh>

#include <operations.hpp>

Public Static Functions

template<typename ...Args> static inline constexpr const char *getKernelName(std::tuple<Args...> args)

template<typename ...Args> static inline auto getShape(const std::tuple<Args...> &args)

Public Static Attributes

static constexpr const char *name = "hyperbolic arctangent"

static constexpr const char *filename = "trigonometry"

static constexpr const char *kernelName = "atanhArrays"

template<> struct TypeInfo<::librapid::detail::Exp>

#include <operations.hpp>

Public Static Functions

template<typename ...Args> static inline constexpr const char *getKernelName(std::tuple<Args...> args)

template<typename ...Args> static inline auto getShape(const std::tuple<Args...> &args)

Public Static Attributes

static constexpr const char *name = "exponent"

static constexpr const char *filename = "expLogPow"

static constexpr const char *kernelName = "expArrays"

template<> struct TypeInfo<::librapid::detail::Exp2>

#include <operations.hpp>

Public Static Functions

template<typename ...Args> static inline constexpr const char *getKernelName(std::tuple<Args...> args)

template<typename ...Args> static inline auto getShape(const std::tuple<Args...> &args)

Public Static Attributes

static constexpr const char *name = "exponent base 2"

static constexpr const char *filename = "expLogPow"

static constexpr const char *kernelName = "exp2Arrays"

template<> struct TypeInfo<::librapid::detail::Exp10>

#include <operations.hpp>

Public Static Functions

template<typename ...Args> static inline constexpr const char *getKernelName(std::tuple<Args...> args)

template<typename ...Args> static inline auto getShape(const std::tuple<Args...> &args)

Public Static Attributes

static constexpr const char *name = "exponent base 10"

static constexpr const char *filename = "expLogPow"

static constexpr const char *kernelName = "exp10Arrays"

template<> struct TypeInfo<::librapid::detail::Log>

#include <operations.hpp>

Public Static Functions

template<typename ...Args> static inline constexpr const char *getKernelName(std::tuple<Args...> args)

template<typename ...Args> static inline auto getShape(const std::tuple<Args...> &args)

Public Static Attributes

static constexpr const char *name = "logarithm"

static constexpr const char *filename = "expLogPow"

static constexpr const char *kernelName = "logArrays"

template<> struct TypeInfo<::librapid::detail::Log2>

#include <operations.hpp>

Public Static Functions

template<typename ...Args> static inline constexpr const char *getKernelName(std::tuple<Args...> args)

template<typename ...Args> static inline auto getShape(const std::tuple<Args...> &args)

Public Static Attributes

static constexpr const char *name = "logarithm base 2"

static constexpr const char *filename = "expLogPow"

static constexpr const char *kernelName = "log2Arrays"

template<> struct TypeInfo<::librapid::detail::Log10>

#include <operations.hpp>

Public Static Functions

template<typename ...Args> static inline constexpr const char *getKernelName(std::tuple<Args...> args)

template<typename ...Args> static inline auto getShape(const std::tuple<Args...> &args)

Public Static Attributes

static constexpr const char *name = "logarithm base 10"

static constexpr const char *filename = "expLogPow"

static constexpr const char *kernelName = "log10Arrays"

template<> struct TypeInfo<::librapid::detail::Sqrt>

#include <operations.hpp>

Public Static Functions

template<typename ...Args> static inline constexpr const char *getKernelName(std::tuple<Args...> args)

template<typename ...Args> static inline auto getShape(const std::tuple<Args...> &args)

Public Static Attributes

static constexpr const char *name = "square root"

static constexpr const char *filename = "expLogPow"

static constexpr const char *kernelName = "sqrtArrays"

template<> struct TypeInfo<::librapid::detail::Cbrt>

#include <operations.hpp>

Public Static Functions

template<typename ...Args> static inline constexpr const char *getKernelName(std::tuple<Args...> args)

template<typename ...Args> static inline auto getShape(const std::tuple<Args...> &args)

Public Static Attributes

static constexpr const char *name = "cube root"

static constexpr const char *filename = "expLogPow"

static constexpr const char *kernelName = "cbrtArrays"

template<> struct TypeInfo<::librapid::detail::Abs>

#include <operations.hpp>

Public Static Functions

template<typename ...Args> static inline constexpr const char *getKernelName(std::tuple<Args...> args)

template<typename ...Args> static inline auto getShape(const std::tuple<Args...> &args)

Public Static Attributes

static constexpr const char *name = "absolute value"

static constexpr const char *filename = "abs"

static constexpr const char *kernelName = "absArrays"

template<> struct TypeInfo<::librapid::detail::Floor>

#include <operations.hpp>

Public Static Functions

template<typename ...Args> static inline constexpr const char *getKernelName(std::tuple<Args...> args)

template<typename ...Args> static inline auto getShape(const std::tuple<Args...> &args)

Public Static Attributes

static constexpr const char *name = "floor"

static constexpr const char *filename = "floorCeilRound"

static constexpr const char *kernelName = "floorArrays"

template<> struct TypeInfo<::librapid::detail::Ceil>

#include <operations.hpp>

Public Static Functions

template<typename ...Args> static inline constexpr const char *getKernelName(std::tuple<Args...> args)

template<typename ...Args> static inline auto getShape(const std::tuple<Args...> &args)

Public Static Attributes

static constexpr const char *name = "ceiling"

static constexpr const char *filename = "floorCeilRound"

static constexpr const char *kernelName = "ceilArrays"

namespace librapid

Functions

template<class VAL> auto sin(VAL &&val) -> detail::Function<typetraits::DescriptorType_t<VAL>, detail::Sin, VAL>

Calculate the sine of each element in the array.

\(R = \{ R_0, R_1, R_2, ... \} \) \text{ where } \(R_i = \sin(A_i)\)

Template Parameters: VAL – Type of the input
Parameters: val – The input array or function
Returns: Sine function object

template<class VAL> auto cos(VAL &&val) -> detail::Function<typetraits::DescriptorType_t<VAL>, detail::Cos, VAL>

Calculate the cosine of each element in the array.

\(R = \{ R_0, R_1, R_2, ... \} \) \text{ where } \(R_i = \cos(A_i)\)

Template Parameters: VAL – Type of the input
Parameters: val – The input array or function
Returns: Cosine function object

template<class VAL> auto tan(VAL &&val) -> detail::Function<typetraits::DescriptorType_t<VAL>, detail::Tan, VAL>

Calculate the tangent of each element in the array.

\(R = \{ R_0, R_1, R_2, ... \} \) \text{ where } \(R_i = \tan(A_i)\)

Template Parameters: VAL – Type of the input
Parameters: val – The input array or function
Returns: Tangent function object

template<class VAL> auto asin(VAL &&val) -> detail::Function<typetraits::DescriptorType_t<VAL>, detail::Asin, VAL>

Calculate the arcsine of each element in the array.

\(R = \{ R_0, R_1, R_2, ... \} \) \text{ where } \(R_i = \sin^{-1}(A_i)\)

Template Parameters: VAL – Type of the input
Parameters: val – The input array or function
Returns: Arcsine function object

template<class VAL> auto acos(VAL &&val) -> detail::Function<typetraits::DescriptorType_t<VAL>, detail::Acos, VAL>

Calculate the arccosine of each element in the array.

\(R = \{ R_0, R_1, R_2, ... \} \) \text{ where } \(R_i = \cos^{-1}(A_i)\)

Template Parameters: VAL – Type of the input
Parameters: val – The input array or function
Returns: Arccosine function object

template<class VAL> auto atan(VAL &&val) -> detail::Function<typetraits::DescriptorType_t<VAL>, detail::Atan, VAL>

Calculate the arctangent of each element in the array.

\(R = \{ R_0, R_1, R_2, ... \} \) \text{ where } \(R_i = \tan^{-1}(A_i)\)

Template Parameters: VAL – Type of the input
Parameters: val – The input array or function
Returns: Arctangent function object

template<class VAL> auto sinh(VAL &&val) -> detail::Function<typetraits::DescriptorType_t<VAL>, detail::Sinh, VAL>

Calculate the hyperbolic sine of each element in the array.

\(R = \{ R_0, R_1, R_2, ... \} \) \text{ where } \(R_i = \sinh(A_i)\)

Template Parameters: VAL – Type of the input
Parameters: val – The input array or function
Returns: Hyperbolic sine function object

template<class VAL> auto cosh(VAL &&val) -> detail::Function<typetraits::DescriptorType_t<VAL>, detail::Cosh, VAL>

Calculate the hyperbolic cosine of each element in the array.

\(R = \{ R_0, R_1, R_2, ... \} \) \text{ where } \(R_i = \cosh(A_i)\)

Template Parameters: VAL – Type of the input
Parameters: val – The input array or function
Returns: Hyperbolic cosine function object

template<class VAL> auto tanh(VAL &&val) -> detail::Function<typetraits::DescriptorType_t<VAL>, detail::Tanh, VAL>

Calculate the hyperbolic tangent of each element in the array.

\(R = \{ R_0, R_1, R_2, ... \} \) \text{ where } \(R_i = \tanh(A_i)\)

Template Parameters: VAL – Type of the input
Parameters: val – The input array or function
Returns: Hyperbolic tangent function object

template<class VAL> auto asinh(VAL &&val) -> detail::Function<typetraits::DescriptorType_t<VAL>, detail::Asinh, VAL>

Calculate the hyperbolic arcsine of each element in the array.

\(R = \{ R_0, R_1, R_2, ... \} \) \text{ where } \(R_i = \sinh^{-1}(A_i)\)

Template Parameters: VAL – Type of the input
Parameters: val – The input array or function
Returns: Hyperbolic sine function object

template<class VAL> auto acosh(VAL &&val) -> detail::Function<typetraits::DescriptorType_t<VAL>, detail::Acosh, VAL>

Calculate the hyperbolic arccosine of each element in the array.

\(R = \{ R_0, R_1, R_2, ... \} \) \text{ where } \(R_i = \cosh^{-1}(A_i)\)

Template Parameters: VAL – Type of the input
Parameters: val – The input array or function
Returns: Hyperbolic cosine function object

template<class VAL> auto atanh(VAL &&val) -> detail::Function<typetraits::DescriptorType_t<VAL>, detail::Atanh, VAL>

Calculate the hyperbolic arctangent of each element in the array.

\(R = \{ R_0, R_1, R_2, ... \} \) \text{ where } \(R_i = \tanh^{-1}(A_i)\)

Template Parameters: VAL – Type of the input
Parameters: val – The input array or function
Returns: Hyperbolic tangent function object

template<class VAL> auto exp(VAL &&val) -> detail::Function<typetraits::DescriptorType_t<VAL>, detail::Exp, VAL>

Raise e to the power of each element in the array.

\(R = \{ R_0, R_1, R_2, ... \} \) \text{ where } \(R_i = e^{A_i}\)

Template Parameters: VAL – Type of the input
Parameters: val – The input array or function
Returns: Exponential function object

template<class VAL> auto exp2(VAL &&val) -> detail::Function<typetraits::DescriptorType_t<VAL>, detail::Exp2, VAL>

Raise 2 to the power of each element in the array.

\(R = \{ R_0, R_1, R_2, ... \} \) \text{ where } \(R_i = 2^{A_i}\)

Template Parameters: VAL – Type of the input
Parameters: val – The input array or function
Returns: Exponential function object

template<class VAL> auto exp10(VAL &&val) -> detail::Function<typetraits::DescriptorType_t<VAL>, detail::Exp10, VAL>

Raise 10 to the power of each element in the array.

\(R = \{ R_0, R_1, R_2, ... \} \) \text{ where } \(R_i = 10^{A_i}\)

Template Parameters: VAL – Type of the input
Parameters: val – The input array or function
Returns: Exponential function object

template<class VAL> auto log(VAL &&val) -> detail::Function<typetraits::DescriptorType_t<VAL>, detail::Log, VAL>

\(R = \{ R_0, R_1, R_2, ... \} \) \text{ where } \(R_i = \ln(A_i)\)

Template Parameters: VAL – Type of the input
Parameters: val – The input array or function
Returns: Natural logarithm function object

template<class VAL> auto log10(VAL &&val) -> detail::Function<typetraits::DescriptorType_t<VAL>, detail::Log10, VAL>

Compute the base 10 logarithm of each element in the array.

\(R = \{ R_0, R_1, R_2, ... \} \) \text{ where } \(R_i = \log_{10}(A_i)\)

Template Parameters: VAL – Type of the input
Parameters: val – The input array or function
Returns: Base 10 logarithm function object

template<class VAL> auto log2(VAL &&val) -> detail::Function<typetraits::DescriptorType_t<VAL>, detail::Log2, VAL>

Compute the base 2 logarithm of each element in the array.

\(R = \{ R_0, R_1, R_2, ... \} \) \text{ where } \(R_i = \log_{2}(A_i)\)

Template Parameters: VAL – Type of the input
Parameters: val – The input array or function
Returns: Base 2 logarithm function object

template<class VAL> auto sqrt(VAL &&val) -> detail::Function<typetraits::DescriptorType_t<VAL>, detail::Sqrt, VAL>

Compute the square root of each element in the array.

\(R = \{ R_0, R_1, R_2, ... \} \) \text{ where } \(R_i = \sqrt{A_i}\)

Template Parameters: VAL – Type of the input
Parameters: val – The input array or function
Returns: Square root function object

template<class VAL> auto cbrt(VAL &&val) -> detail::Function<typetraits::DescriptorType_t<VAL>, detail::Cbrt, VAL>

Compute the cube root of each element in the array.

\(R = \{ R_0, R_1, R_2, ... \} \) \text{ where } \(R_i = \sqrt[3]{A_i}\)

Template Parameters: VAL – Type of the input
Parameters: val – The input array or function
Returns: Cube root function object

template<class VAL> auto abs(VAL &&val) -> detail::Function<typetraits::DescriptorType_t<VAL>, detail::Abs, VAL>

Compute the absolute value of each element in the array.

\(R = \{ R_0, R_1, R_2, ... \} \) \text{ where } \(R_i = |A_i|\)

Template Parameters: VAL – Type of the input
Parameters: val – The input array or function
Returns: Absolute value function object

template<class VAL> auto floor(VAL &&val) -> detail::Function<typetraits::DescriptorType_t<VAL>, detail::Floor, VAL>

Compute the floor of each element in the array.

\(R = \{ R_0, R_1, R_2, ... \} \) \text{ where } \(R_i = \lfloor A_i \rfloor\)

Template Parameters: VAL – Type of the input
Parameters: val – The input array or function
Returns: Floor function object

template<class VAL> auto ceil(VAL &&val) -> detail::Function<typetraits::DescriptorType_t<VAL>, detail::Ceil, VAL>

Compute the ceiling of each element in the array.

\(R = \{ R_0, R_1, R_2, ... \} \) \text{ where } \(R_i = \lceil A_i \rceil\)

Template Parameters: VAL – Type of the input
Parameters: val – The input array or function
Returns: Ceiling function object

namespace array

Functions

template<class LHS, class RHS> auto operator+(LHS &&lhs, RHS &&rhs)

Element-wise array addition.

Performs element-wise addition on two arrays. They must both be the same size and of the same data type.

Template Parameters

LHS – Type of the LHS element
RHS – Type of the RHS element

Parameters

lhs – The first array
rhs – The second array

Returns

The element-wise sum of the two arrays

template<class LHS, class RHS> auto operator-(LHS &&lhs, RHS &&rhs)

Element-wise array subtraction.

Performs element-wise subtraction on two arrays. They must both be the same size and of the same data type.

Template Parameters

LHS – Type of the LHS element
RHS – Type of the RHS element

Parameters

lhs – The first array
rhs – The second array

Returns

The element-wise difference of the two arrays

template<class LHS, class RHS> auto operator*(LHS &&lhs, RHS &&rhs)

Element-wise array multiplication.

Performs element-wise multiplication on two arrays. They must both be the same size and of the same data type.

Template Parameters

LHS – Type of the LHS element
RHS – Type of the RHS element

Parameters

lhs – The first array
rhs – The second array

Returns

The element-wise product of the two arrays

template<class LHS, class RHS> auto operator/(LHS &&lhs, RHS &&rhs)

Element-wise array division.

Performs element-wise division on two arrays. They must both be the same size and of the same data type.

Template Parameters

LHS – Type of the LHS element
RHS – Type of the RHS element

Parameters

lhs – The first array
rhs – The second array

Returns

The element-wise division of the two arrays

template<class LHS, class RHS> auto operator<(LHS &&lhs, RHS &&rhs)

Element-wise array comparison, checking whether a < b for all a, b in input arrays.

Performs an element-wise comparison on two arrays, checking if the first value is less than the second. They must both be the same size and of the same data type.

Template Parameters

LHS – Type of the LHS element
RHS – Type of the RHS element

Parameters

lhs – The first array
rhs – The second array

Returns

The element-wise comparison of the two arrays

template<class LHS, class RHS> auto operator>(LHS &&lhs, RHS &&rhs)

Element-wise array comparison, checking whether a > b for all a, b in input arrays.

Performs an element-wise comparison on two arrays, checking if the first value is greater than the second. They must both be the same size and of the same data type.

Template Parameters

LHS – Type of the LHS element
RHS – Type of the RHS element

Parameters

lhs – The first array
rhs – The second array

Returns

The element-wise comparison of the two arrays

template<class LHS, class RHS> auto operator<=(LHS &&lhs, RHS &&rhs)

Element-wise array comparison, checking whether a <= b for all a, b in input arrays.

Performs an element-wise comparison on two arrays, checking if the first value is less than or equal to the second. They must both be the same size and of the same data type.

Template Parameters

LHS – Type of the LHS element
RHS – Type of the RHS element

Parameters

lhs – The first array
rhs – The second array

Returns

The element-wise comparison of the two arrays

template<class LHS, class RHS> auto operator>=(LHS &&lhs, RHS &&rhs)

Element-wise array comparison, checking whether a >= b for all a, b in input arrays.

Performs an element-wise comparison on two arrays, checking if the first value is greater than or equal to the second. They must both be the same size and of the same data type.

Template Parameters

LHS – Type of the LHS element
RHS – Type of the RHS element

Parameters

lhs – The first array
rhs – The second array

Returns

The element-wise comparison of the two arrays

template<class LHS, class RHS> auto operator==(LHS &&lhs, RHS &&rhs)

Element-wise array comparison, checking whether a == b for all a, b in input arrays.

Performs an element-wise comparison on two arrays, checking if the first value is equal to the second. They must both be the same size and of the same data type.

Template Parameters

LHS – Type of the LHS element
RHS – Type of the RHS element

Parameters

lhs – The first array
rhs – The second array

Returns

The element-wise comparison of the two arrays

template<class LHS, class RHS> auto operator!=(LHS &&lhs, RHS &&rhs)

Element-wise array comparison, checking whether a != b for all a, b in input arrays.

Performs an element-wise comparison on two arrays, checking if the first value is not equal to the second. They must both be the same size and of the same data type.

Template Parameters

LHS – Type of the LHS element
RHS – Type of the RHS element

Parameters

lhs – The first array
rhs – The second array

Returns

The element-wise comparison of the two arrays

template<class VAL> auto operator-(VAL &&val)

Negate each element in the array.

Template Parameters: VAL – Type to negate
Parameters: val – The input array or function
Returns: Negation function object

namespace detail

Functions

template<typename desc, typename Functor, typename ...Args> auto makeFunction(Args&&... args)

Construct a new function object with the given functor type and arguments.

Template Parameters

desc – Functor descriptor
Functor – Function type
Args – Argument types

Parameters

args – Arguments passed to the function

Returns

A new Function instance

template<typename T, LibRapidType... ValidTypes> constexpr bool isType()

template<typename VAL> constexpr bool isArrayOp()

template<typename LHS, typename RHS> constexpr bool isArrayOpArray()

template<typename LHS, typename RHS> constexpr bool isArrayOpWithScalar()

Variables

template<typename VAL> IsArrayOp = isArrayOp<VAL>()

template<typename LHS, typename RHS> IsArrayOpArray = isArrayOpArray<LHS, RHS>()

template<typename LHS, typename RHS> IsArrayOpWithScalar = isArrayOpWithScalar<LHS, RHS>()

struct Abs

#include <operations.hpp>

Public Functions

template<typename T> inline auto operator()(const T &arg) const

template<typename Packet> inline auto packet(const Packet &arg) const

struct Acos

#include <operations.hpp>

Public Functions

template<typename T> inline auto operator()(const T &arg) const

template<typename Packet> inline auto packet(const Packet &arg) const

struct Acosh

#include <operations.hpp>

Public Functions

template<typename T> inline auto operator()(const T &arg) const

template<typename Packet> inline auto packet(const Packet &arg) const

struct Asin

#include <operations.hpp>

Public Functions

template<typename T> inline auto operator()(const T &arg) const

template<typename Packet> inline auto packet(const Packet &arg) const

struct Asinh

#include <operations.hpp>

Public Functions

template<typename T> inline auto operator()(const T &arg) const

template<typename Packet> inline auto packet(const Packet &arg) const

struct Atan

#include <operations.hpp>

Public Functions

template<typename T> inline auto operator()(const T &arg) const

template<typename Packet> inline auto packet(const Packet &arg) const

struct Atanh

#include <operations.hpp>

Public Functions

template<typename T> inline auto operator()(const T &arg) const

template<typename Packet> inline auto packet(const Packet &arg) const

struct Cbrt

#include <operations.hpp>

Public Functions

template<typename T> inline auto operator()(const T &arg) const

template<typename Packet> inline auto packet(const Packet &arg) const

struct Ceil

#include <operations.hpp>

Public Functions

template<typename T> inline auto operator()(const T &arg) const

template<typename Packet> inline auto packet(const Packet &arg) const

struct Cos

#include <operations.hpp>

Public Functions

template<typename T> inline auto operator()(const T &arg) const

template<typename Packet> inline auto packet(const Packet &arg) const

struct Cosh

#include <operations.hpp>

Public Functions

template<typename T> inline auto operator()(const T &arg) const

template<typename Packet> inline auto packet(const Packet &arg) const

struct Divide

#include <operations.hpp>

Public Functions

template<typename T, typename V> inline auto operator()(const T &lhs, const V &rhs) const

template<typename Packet> inline auto packet(const Packet &lhs, const Packet &rhs) const

struct ElementWiseEqual

#include <operations.hpp>

Public Functions

template<typename T, typename V> inline auto operator()(const T &lhs, const V &rhs) const

template<typename Packet> inline auto packet(const Packet &lhs, const Packet &rhs) const

struct ElementWiseNotEqual

#include <operations.hpp>

Public Functions

template<typename T, typename V> inline auto operator()(const T &lhs, const V &rhs) const

template<typename Packet> inline auto packet(const Packet &lhs, const Packet &rhs) const

struct Exp

#include <operations.hpp>

Public Functions

template<typename T> inline auto operator()(const T &arg) const

template<typename Packet> inline auto packet(const Packet &arg) const

struct Exp10

#include <operations.hpp>

Public Functions

template<typename T> inline auto operator()(const T &arg) const

template<typename Packet> inline auto packet(const Packet &arg) const

struct Exp2

#include <operations.hpp>

Public Functions

template<typename T> inline auto operator()(const T &arg) const

template<typename Packet> inline auto packet(const Packet &arg) const

struct Floor

#include <operations.hpp>

Public Functions

template<typename T> inline auto operator()(const T &arg) const

template<typename Packet> inline auto packet(const Packet &arg) const

struct GreaterThan

#include <operations.hpp>

Public Functions

template<typename T, typename V> inline auto operator()(const T &lhs, const V &rhs) const

template<typename Packet> inline auto packet(const Packet &lhs, const Packet &rhs) const

struct GreaterThanEqual

#include <operations.hpp>

Public Functions

template<typename T, typename V> inline auto operator()(const T &lhs, const V &rhs) const

template<typename Packet> inline auto packet(const Packet &lhs, const Packet &rhs) const

struct LessThan

#include <operations.hpp>

Public Functions

template<typename T, typename V> inline auto operator()(const T &lhs, const V &rhs) const

template<typename Packet> inline auto packet(const Packet &lhs, const Packet &rhs) const

struct LessThanEqual

#include <operations.hpp>

Public Functions

template<typename T, typename V> inline auto operator()(const T &lhs, const V &rhs) const

template<typename Packet> inline auto packet(const Packet &lhs, const Packet &rhs) const

struct Log

#include <operations.hpp>

Public Functions

template<typename T> inline auto operator()(const T &arg) const

template<typename Packet> inline auto packet(const Packet &arg) const

struct Log10

#include <operations.hpp>

Public Functions

template<typename T> inline auto operator()(const T &arg) const

template<typename Packet> inline auto packet(const Packet &arg) const

struct Log2

#include <operations.hpp>

Public Functions

template<typename T> inline auto operator()(const T &arg) const

template<typename Packet> inline auto packet(const Packet &arg) const

struct Minus

#include <operations.hpp>

Public Functions

template<typename T, typename V> inline auto operator()(const T &lhs, const V &rhs) const

template<typename Packet> inline auto packet(const Packet &lhs, const Packet &rhs) const

struct Multiply

#include <operations.hpp>

Public Functions

template<typename T, typename V> inline auto operator()(const T &lhs, const V &rhs) const

template<typename Packet> inline auto packet(const Packet &lhs, const Packet &rhs) const

struct Neg

#include <operations.hpp>

Public Functions

template<typename T> inline auto operator()(const T &arg) const

template<typename Packet> inline auto packet(const Packet &arg) const

struct Plus

#include <operations.hpp>

Public Functions

template<typename T, typename V> inline auto operator()(const T &lhs, const V &rhs) const

template<typename Packet> inline auto packet(const Packet &lhs, const Packet &rhs) const

struct Sin

#include <operations.hpp>

Public Functions

template<typename T> inline auto operator()(const T &arg) const

template<typename Packet> inline auto packet(const Packet &arg) const

struct Sinh

#include <operations.hpp>

Public Functions

template<typename T> inline auto operator()(const T &arg) const

template<typename Packet> inline auto packet(const Packet &arg) const

struct Sqrt

#include <operations.hpp>

Public Functions

template<typename T> inline auto operator()(const T &arg) const

template<typename Packet> inline auto packet(const Packet &arg) const

struct Tan

#include <operations.hpp>

Public Functions

template<typename T> inline auto operator()(const T &arg) const

template<typename Packet> inline auto packet(const Packet &arg) const

struct Tanh

#include <operations.hpp>

Public Functions

template<typename T> inline auto operator()(const T &arg) const

template<typename Packet> inline auto packet(const Packet &arg) const

namespace typetraits

Typedefs

template<typename ...Args> using DescriptorType_t = typename DescriptorType<Args...>::Type

A simplification of the DescriptorType to reduce code size

See also

DescriptorType

Template Parameters: Args – Input types

template<typename T> struct DescriptorExtractor

#include <operations.hpp>

Extracts the Descriptor type of the provided type.

Template Parameters: T – The type to extract the descriptor from

Public Types

using Type = ::librapid::detail::descriptor::Trivial

template<typename ShapeType, typename StorageType> ArrayContainer< ShapeType, StorageType > >

#include <operations.hpp>

Extracts the Descriptor type of an ArrayContainer object. In this case, the Descriptor is Trivial

Template Parameters

ShapeType – The shape type of the ArrayContainer
StorageType – The storage type of the ArrayContainer

Public Types

using Type = ::librapid::detail::descriptor::Trivial#

template<typename T, typename S> GeneralArrayView< T, S > >

#include <operations.hpp>

Extracts the Descriptor type of an ArrayView object

Template Parameters: T – The Array type of the ArrayView

Public Types

using Type = ::librapid::detail::descriptor::Trivial

template<typename Descriptor, typename Functor, typename... Args> Function< Descriptor, Functor, Args... > >

#include <operations.hpp>

Extracts the Descriptor type of a Function object

Template Parameters

Descriptor – The descriptor of the Function
Functor – The functor type of the Function
Args – The argument types of the Function

Public Types

using Type = Descriptor

template<typename Descriptor1, typename Descriptor2> struct DescriptorMerger

#include <operations.hpp>

Merge together two Descriptor types. Two trivial operations will result in another trivial operation, while any other combination will result in a Combined operation.

Template Parameters

Descriptor1 – The first descriptor
Descriptor2 – The second descriptor

Public Types

using Type = ::librapid::detail::descriptor::Combined

template<typename Descriptor1> struct DescriptorMerger<Descriptor1, Descriptor1>

#include <operations.hpp>

Public Types

using Type = Descriptor1

template<typename First, typename ...Rest> struct DescriptorType

#include <operations.hpp>

Return the combined Descriptor type of the provided types

Allows a number of Descriptor types to be merged together into a single Descriptor type. The Descriptors used are extracted from the

of the provided types.

Template Parameters

First – The first type to merge
Rest – The remaining types
First – The first type to merge
Rest – The remaining types

Public Types

using FirstType = std::decay_t<First>

using FirstDescriptor = typename DescriptorExtractor<FirstType>::Type

using RestDescriptor = decltype(impl::descriptorExtractor<Rest...>())

using Type = typename DescriptorMerger<FirstDescriptor, RestDescriptor>::Type

template<> Abs >

#include <operations.hpp>

Public Static Functions

template<typename ...Args> static inline constexpr const char *getKernelName(std::tuple<Args...> args)#

template<typename ...Args> static inline auto getShape(const std::tuple<Args...> &args)#

Public Static Attributes

static constexpr const char *name = "absolute value"#

static constexpr const char *filename = "abs"#

static constexpr const char *kernelName = "absArrays"#

template<> Acos >

#include <operations.hpp>

Public Static Functions

template<typename ...Args> static inline constexpr const char *getKernelName(std::tuple<Args...> args)

template<typename ...Args> static inline auto getShape(const std::tuple<Args...> &args)

Public Static Attributes

static constexpr const char *name = "arcos"

static constexpr const char *filename = "trigonometry"

static constexpr const char *kernelName = "acosArrays"

template<> Acosh >

#include <operations.hpp>

Public Static Functions

template<typename ...Args> static inline constexpr const char *getKernelName(std::tuple<Args...> args)

template<typename ...Args> static inline auto getShape(const std::tuple<Args...> &args)

Public Static Attributes

static constexpr const char *name = "hyperbolic arccosine"

static constexpr const char *filename = "trigonometry"

static constexpr const char *kernelName = "acoshArrays"

template<> Asin >

#include <operations.hpp>

Public Static Functions

template<typename ...Args> static inline constexpr const char *getKernelName(std::tuple<Args...> args)

template<typename ...Args> static inline auto getShape(const std::tuple<Args...> &args)

Public Static Attributes

static constexpr const char *name = "arcsin"

static constexpr const char *filename = "trigonometry"

static constexpr const char *kernelName = "asinArrays"

template<> Asinh >

#include <operations.hpp>

Public Static Functions

template<typename ...Args> static inline constexpr const char *getKernelName(std::tuple<Args...> args)

template<typename ...Args> static inline auto getShape(const std::tuple<Args...> &args)

Public Static Attributes

static constexpr const char *name = "hyperbolic arcsine"

static constexpr const char *filename = "trigonometry"

static constexpr const char *kernelName = "asinhArrays"

template<> Atan >

#include <operations.hpp>

Public Static Functions

template<typename ...Args> static inline constexpr const char *getKernelName(std::tuple<Args...> args)

template<typename ...Args> static inline auto getShape(const std::tuple<Args...> &args)

Public Static Attributes

static constexpr const char *name = "arctan"

static constexpr const char *filename = "trigonometry"

static constexpr const char *kernelName = "atanArrays"

template<> Atanh >

#include <operations.hpp>

Public Static Functions

template<typename ...Args> static inline constexpr const char *getKernelName(std::tuple<Args...> args)

template<typename ...Args> static inline auto getShape(const std::tuple<Args...> &args)

Public Static Attributes

static constexpr const char *name = "hyperbolic arctangent"

static constexpr const char *filename = "trigonometry"

static constexpr const char *kernelName = "atanhArrays"

template<> Cbrt >

#include <operations.hpp>

Public Static Functions

template<typename ...Args> static inline constexpr const char *getKernelName(std::tuple<Args...> args)

template<typename ...Args> static inline auto getShape(const std::tuple<Args...> &args)

Public Static Attributes

static constexpr const char *name = "cube root"

static constexpr const char *filename = "expLogPow"

static constexpr const char *kernelName = "cbrtArrays"

template<> Ceil >

#include <operations.hpp>

Public Static Functions

template<typename ...Args> static inline constexpr const char *getKernelName(std::tuple<Args...> args)

template<typename ...Args> static inline auto getShape(const std::tuple<Args...> &args)

Public Static Attributes

static constexpr const char *name = "ceiling"

static constexpr const char *filename = "floorCeilRound"

static constexpr const char *kernelName = "ceilArrays"

template<> Cos >

#include <operations.hpp>

Public Static Functions

template<typename ...Args> static inline constexpr const char *getKernelName(std::tuple<Args...> args)

template<typename ...Args> static inline auto getShape(const std::tuple<Args...> &args)

Public Static Attributes

static constexpr const char *name = "cos"

static constexpr const char *filename = "trigonometry"

static constexpr const char *kernelName = "cosArrays"

template<> Cosh >

#include <operations.hpp>

Public Static Functions

template<typename ...Args> static inline constexpr const char *getKernelName(std::tuple<Args...> args)

template<typename ...Args> static inline auto getShape(const std::tuple<Args...> &args)

Public Static Attributes

static constexpr const char *name = "hyperbolic cosine"

static constexpr const char *filename = "trigonometry"

static constexpr const char *kernelName = "coshArrays"

template<> Divide >

#include <operations.hpp>

Public Static Functions

template<typename T1, typename T2> static inline constexpr const char *getKernelNameImpl(std::tuple<T1, T2> args)#

template<typename ...Args> static inline constexpr const char *getKernelName(std::tuple<Args...> args)

template<typename First, typename Second> static inline auto getShapeImpl(const std::tuple<First, Second> &tup)#

template<typename ...Args> static inline auto getShape(const std::tuple<Args...> &args)

Public Static Attributes

static constexpr const char *name = "divide"

static constexpr const char *filename = "arithmetic"

static constexpr const char *kernelName = "divArrays"

static constexpr const char *kernelNameScalarRhs = "divArraysScalarRhs"#

static constexpr const char *kernelNameScalarLhs = "divArraysScalarLhs"#

template<> ElementWiseEqual >

#include <operations.hpp>

Public Static Functions

template<typename T1, typename T2> static inline constexpr const char *getKernelNameImpl(std::tuple<T1, T2> args)

template<typename ...Args> static inline constexpr const char *getKernelName(std::tuple<Args...> args)

template<typename First, typename Second> static inline auto getShapeImpl(const std::tuple<First, Second> &tup)

template<typename ...Args> static inline auto getShape(const std::tuple<Args...> &args)

Public Static Attributes

static constexpr const char *name = "element wise equal"

static constexpr const char *filename = "arithmetic"

static constexpr const char *kernelName = "elementWiseEqualArrays"

static constexpr const char *kernelNameScalarRhs = "elementWiseEqualArraysScalarRhs"

static constexpr const char *kernelNameScalarLhs = "elementWiseEqualArraysScalarLhs"

template<> ElementWiseNotEqual >

#include <operations.hpp>

Public Static Functions

template<typename T1, typename T2> static inline constexpr const char *getKernelNameImpl(std::tuple<T1, T2> args)

template<typename ...Args> static inline constexpr const char *getKernelName(std::tuple<Args...> args)

template<typename First, typename Second> static inline auto getShapeImpl(const std::tuple<First, Second> &tup)

template<typename ...Args> static inline auto getShape(const std::tuple<Args...> &args)

Public Static Attributes

static constexpr const char *name = "element wise not equal"

static constexpr const char *filename = "arithmetic"

static constexpr const char *kernelName = "elementWiseNotEqualArrays"

static constexpr const char *kernelNameScalarRhs = "elementWiseNotEqualArraysScalarRhs"

static constexpr const char *kernelNameScalarLhs = "elementWiseNotEqualArraysScalarLhs"

template<> Exp >

#include <operations.hpp>

Public Static Functions

template<typename ...Args> static inline constexpr const char *getKernelName(std::tuple<Args...> args)

template<typename ...Args> static inline auto getShape(const std::tuple<Args...> &args)

Public Static Attributes

static constexpr const char *name = "exponent"

static constexpr const char *filename = "expLogPow"

static constexpr const char *kernelName = "expArrays"

template<> Exp10 >

#include <operations.hpp>

Public Static Functions

template<typename ...Args> static inline constexpr const char *getKernelName(std::tuple<Args...> args)

template<typename ...Args> static inline auto getShape(const std::tuple<Args...> &args)

Public Static Attributes

static constexpr const char *name = "exponent base 10"

static constexpr const char *filename = "expLogPow"

static constexpr const char *kernelName = "exp10Arrays"

template<> Exp2 >

#include <operations.hpp>

Public Static Functions

template<typename ...Args> static inline constexpr const char *getKernelName(std::tuple<Args...> args)

template<typename ...Args> static inline auto getShape(const std::tuple<Args...> &args)

Public Static Attributes

static constexpr const char *name = "exponent base 2"

static constexpr const char *filename = "expLogPow"

static constexpr const char *kernelName = "exp2Arrays"

template<> Floor >

#include <operations.hpp>

Public Static Functions

template<typename ...Args> static inline constexpr const char *getKernelName(std::tuple<Args...> args)

template<typename ...Args> static inline auto getShape(const std::tuple<Args...> &args)

Public Static Attributes

static constexpr const char *name = "floor"

static constexpr const char *filename = "floorCeilRound"

static constexpr const char *kernelName = "floorArrays"

template<> GreaterThan >

#include <operations.hpp>

Public Static Functions

template<typename T1, typename T2> static inline constexpr const char *getKernelNameImpl(std::tuple<T1, T2> args)

template<typename ...Args> static inline constexpr const char *getKernelName(std::tuple<Args...> args)

template<typename First, typename Second> static inline auto getShapeImpl(const std::tuple<First, Second> &tup)

template<typename ...Args> static inline auto getShape(const std::tuple<Args...> &args)

Public Static Attributes

static constexpr const char *name = "greater than"

static constexpr const char *filename = "arithmetic"

static constexpr const char *kernelName = "greaterThanArrays"

static constexpr const char *kernelNameScalarRhs = "greaterThanArraysScalarRhs"

static constexpr const char *kernelNameScalarLhs = "greaterThanArraysScalarLhs"

template<> GreaterThanEqual >

#include <operations.hpp>

Public Static Functions

template<typename T1, typename T2> static inline constexpr const char *getKernelNameImpl(std::tuple<T1, T2> args)

template<typename ...Args> static inline constexpr const char *getKernelName(std::tuple<Args...> args)

template<typename First, typename Second> static inline auto getShapeImpl(const std::tuple<First, Second> &tup)

template<typename ...Args> static inline auto getShape(const std::tuple<Args...> &args)

Public Static Attributes

static constexpr const char *name = "greater than or equal"

static constexpr const char *filename = "arithmetic"

static constexpr const char *kernelName = "greaterThanEqualArrays"

static constexpr const char *kernelNameScalarRhs = "greaterThanEqualArraysScalarRhs"

static constexpr const char *kernelNameScalarLhs = "greaterThanEqualArraysScalarLhs"

template<> LessThan >

#include <operations.hpp>

Public Static Functions

template<typename T1, typename T2> static inline constexpr const char *getKernelNameImpl(std::tuple<T1, T2> args)

template<typename ...Args> static inline constexpr const char *getKernelName(std::tuple<Args...> args)

template<typename First, typename Second> static inline auto getShapeImpl(const std::tuple<First, Second> &tup)

template<typename ...Args> static inline auto getShape(const std::tuple<Args...> &args)

Public Static Attributes

static constexpr const char *name = "less than"

static constexpr const char *filename = "arithmetic"

static constexpr const char *kernelName = "lessThanArrays"

static constexpr const char *kernelNameScalarRhs = "lessThanArraysScalarRhs"

static constexpr const char *kernelNameScalarLhs = "lessThanArraysScalarLhs"

template<> LessThanEqual >

#include <operations.hpp>

Public Static Functions

template<typename T1, typename T2> static inline constexpr const char *getKernelNameImpl(std::tuple<T1, T2> args)

template<typename ...Args> static inline constexpr const char *getKernelName(std::tuple<Args...> args)

template<typename First, typename Second> static inline auto getShapeImpl(const std::tuple<First, Second> &tup)

template<typename ...Args> static inline auto getShape(const std::tuple<Args...> &args)

Public Static Attributes

static constexpr const char *name = "less than or equal"

static constexpr const char *filename = "arithmetic"

static constexpr const char *kernelName = "lessThanEqualArrays"

static constexpr const char *kernelNameScalarRhs = "lessThanEqualArraysScalarRhs"

static constexpr const char *kernelNameScalarLhs = "lessThanEqualArraysScalarLhs"

template<> Log >

#include <operations.hpp>

Public Static Functions

template<typename ...Args> static inline constexpr const char *getKernelName(std::tuple<Args...> args)

template<typename ...Args> static inline auto getShape(const std::tuple<Args...> &args)

Public Static Attributes

static constexpr const char *name = "logarithm"

static constexpr const char *filename = "expLogPow"

static constexpr const char *kernelName = "logArrays"

template<> Log10 >

#include <operations.hpp>

Public Static Functions

template<typename ...Args> static inline constexpr const char *getKernelName(std::tuple<Args...> args)

template<typename ...Args> static inline auto getShape(const std::tuple<Args...> &args)

Public Static Attributes

static constexpr const char *name = "logarithm base 10"

static constexpr const char *filename = "expLogPow"

static constexpr const char *kernelName = "log10Arrays"

template<> Log2 >

#include <operations.hpp>

Public Static Functions

template<typename ...Args> static inline constexpr const char *getKernelName(std::tuple<Args...> args)

template<typename ...Args> static inline auto getShape(const std::tuple<Args...> &args)

Public Static Attributes

static constexpr const char *name = "logarithm base 2"

static constexpr const char *filename = "expLogPow"

static constexpr const char *kernelName = "log2Arrays"

template<> Minus >

#include <operations.hpp>

Public Static Functions

template<typename T1, typename T2> static inline constexpr const char *getKernelNameImpl(std::tuple<T1, T2> args)

template<typename ...Args> static inline constexpr const char *getKernelName(std::tuple<Args...> args)

template<typename First, typename Second> static inline auto getShapeImpl(const std::tuple<First, Second> &tup)

template<typename ...Args> static inline auto getShape(const std::tuple<Args...> &args)

Public Static Attributes

static constexpr const char *name = "minus"

static constexpr const char *filename = "arithmetic"

static constexpr const char *kernelName = "subArrays"

static constexpr const char *kernelNameScalarRhs = "subArraysScalarRhs"

static constexpr const char *kernelNameScalarLhs = "subArraysScalarLhs"

template<> Multiply >

#include <operations.hpp>

Public Static Functions

template<typename T1, typename T2> static inline constexpr const char *getKernelNameImpl(std::tuple<T1, T2> args)

template<typename ...Args> static inline constexpr const char *getKernelName(std::tuple<Args...> args)

template<typename First, typename Second> static inline auto getShapeImpl(const std::tuple<First, Second> &tup)

template<typename ...Args> static inline auto getShape(const std::tuple<Args...> &args)

Public Static Attributes

static constexpr const char *name = "multiply"

static constexpr const char *filename = "arithmetic"

static constexpr const char *kernelName = "mulArrays"

static constexpr const char *kernelNameScalarRhs = "mulArraysScalarRhs"

static constexpr const char *kernelNameScalarLhs = "mulArraysScalarLhs"

template<> Neg >

#include <operations.hpp>

Public Static Functions

template<typename ...Args> static inline constexpr const char *getKernelName(std::tuple<Args...> args)

template<typename ...Args> static inline auto getShape(const std::tuple<Args...> &args)

Public Static Attributes

static constexpr const char *name = "negate"

static constexpr const char *filename = "negate"

static constexpr const char *kernelName = "negateArrays"

template<> Plus >

#include <operations.hpp>

Public Static Functions

template<typename T1, typename T2> static inline constexpr const char *getKernelNameImpl(std::tuple<T1, T2> args)

template<typename ...Args> static inline constexpr const char *getKernelName(std::tuple<Args...> args)

template<typename First, typename Second> static inline auto getShapeImpl(const std::tuple<First, Second> &tup)

template<typename ...Args> static inline auto getShape(const std::tuple<Args...> &args)

Public Static Attributes

static constexpr const char *name = "plus"

static constexpr const char *filename = "arithmetic"

static constexpr const char *kernelName = "addArrays"

static constexpr const char *kernelNameScalarRhs = "addArraysScalarRhs"

static constexpr const char *kernelNameScalarLhs = "addArraysScalarLhs"

template<> Sin >

#include <operations.hpp>

Public Static Functions

template<typename ...Args> static inline constexpr const char *getKernelName(std::tuple<Args...> args)

template<typename ...Args> static inline auto getShape(const std::tuple<Args...> &args)

Public Static Attributes

static constexpr const char *name = "sin"

static constexpr const char *filename = "trigonometry"

static constexpr const char *kernelName = "sinArrays"

template<> Sinh >

#include <operations.hpp>

Public Static Functions

template<typename ...Args> static inline constexpr const char *getKernelName(std::tuple<Args...> args)

template<typename ...Args> static inline auto getShape(const std::tuple<Args...> &args)

Public Static Attributes

static constexpr const char *name = "hyperbolic sine"

static constexpr const char *filename = "trigonometry"

static constexpr const char *kernelName = "sinhArrays"

template<> Sqrt >

#include <operations.hpp>

Public Static Functions

template<typename ...Args> static inline constexpr const char *getKernelName(std::tuple<Args...> args)

template<typename ...Args> static inline auto getShape(const std::tuple<Args...> &args)

Public Static Attributes

static constexpr const char *name = "square root"

static constexpr const char *filename = "expLogPow"

static constexpr const char *kernelName = "sqrtArrays"

template<> Tan >

#include <operations.hpp>

Public Static Functions

template<typename ...Args> static inline constexpr const char *getKernelName(std::tuple<Args...> args)

template<typename ...Args> static inline auto getShape(const std::tuple<Args...> &args)

Public Static Attributes

static constexpr const char *name = "tan"

static constexpr const char *filename = "trigonometry"

static constexpr const char *kernelName = "tanArrays"

template<> Tanh >

#include <operations.hpp>

Public Static Functions

template<typename ...Args> static inline constexpr const char *getKernelName(std::tuple<Args...> args)

template<typename ...Args> static inline auto getShape(const std::tuple<Args...> &args)

Public Static Attributes

static constexpr const char *name = "hyperbolic tangent"

static constexpr const char *filename = "trigonometry"

static constexpr const char *kernelName = "tanhArrays"

namespace impl#

Functions

template<typename ...Rest> constexpr auto descriptorExtractor()

A constexpr function which supports the DescriptorType for multi-type inputs

Template Parameters: Rest –
Returns

Size Type#

Warning

doxygenfile: Cannot find file “librapid/include/librapid/array/sizeType.hpp

Stride Tools#

template<typename T> struct formatter<librapid::Stride<T>> : public fmt::formatter<librapid::Shape>

#include <strideTools.hpp>

Public Functions

template<typename FormatContext> inline auto format(const librapid::Stride<T> &stride, FormatContext &ctx)

namespace librapid

template<typename ShapeType_> class Stride

#include <strideTools.hpp>

A Stride is a vector of integers that describes the distance between elements in each dimension of an ArrayContainer object. This can be used to access elements in a non-trivial order, or to access a sub-array of an ArrayContainer object. The Stride class inherits from the Shape class.

See also

Shape

Template Parameters

T – The type of the Stride. Must be an integer type.
N – The number of dimensions in the Stride.

Public Types

using ShapeType = ShapeType_

using IndexType = typename std::decay_t<decltype(std::declval<ShapeType>()[0])>

Public Functions

Stride() = default

Stride(const ShapeType &shape)

Stride(const Stride &other) = default

Stride(Stride &&other) noexcept = default

Stride &operator=(const Stride &other) = default

Stride &operator=(Stride &&other) noexcept = default

auto operator[](size_t index) const -> IndexType

auto operator[](size_t index) -> IndexType&

inline auto ndim() const

auto substride(size_t start, size_t end) const -> Stride<Shape>

auto data() const -> const ShapeType&

auto data() -> ShapeType&

template<typename T_, typename Char, typename Ctx> void str(const fmt::formatter<T_, Char> &format, Ctx &ctx) const

Public Static Attributes

static constexpr size_t MaxDimensions = ShapeType::MaxDimensions

Protected Attributes

ShapeType m_data

namespace typetraits

Functions

LIBRAPID_DEFINE_AS_TYPE(typename ShapeType, Stride<ShapeType>)#

Storage#

namespace librapid

template<typename Scalar_, size_t... Size_> class FixedStorage

#include <storage.hpp>

Public Types

using Scalar = Scalar_

using Pointer = Scalar*

using ConstPointer = const Scalar*

using Reference = Scalar&

using ConstReference = const Scalar&

using SizeType = size_t

using DifferenceType = ptrdiff_t

using Iterator = typename std::array<Scalar, product<Size_...>()>::iterator

using ConstIterator = typename std::array<Scalar, product<Size_...>()>::const_iterator

using ReverseIterator = std::reverse_iterator<Iterator>

using ConstReverseIterator = std::reverse_iterator<ConstIterator>

Public Functions

FixedStorage(): Default constructor.

explicit FixedStorage(const Scalar &value)

Create a FixedStorage object filled with value

Parameters: value – Value to fill the FixedStorage object with

FixedStorage(const FixedStorage &other)

Create a FixedStorage object from another FixedStorage object

Parameters: other – FixedStorage object to copy

FixedStorage(FixedStorage &&other) noexcept

Move constructor for a FixedStorage object

Parameters: other – FixedStorage object to move

explicit FixedStorage(const std::initializer_list<Scalar> &list)

Create a FixedStorage object from a std::initializer_list

Template Parameters: V – Type of the elements in the initializer list
Parameters: list – Initializer list to copy

explicit FixedStorage(const std::vector<Scalar> &vec)

Create a FixedStorage object from a std::vector

Template Parameters: V – Type of the elements in the vector
Parameters: vec – Vector to copy

FixedStorage &operator=(const FixedStorage &other) noexcept

Assignment operator for a FixedStorage object

Parameters: other – FixedStorage object to copy
Returns: *this

FixedStorage &operator=(FixedStorage &&other) noexcept = default

Move assignment operator for a FixedStorage object

Parameters: other – FixedStorage object to move
Returns: *this

~FixedStorage() = default: Free a FixedStorage object.

void resize(SizeType newSize)

Resize a Storage object to size elements. Existing elements are preserved.

Parameters: size – New size of the Storage object

void resize(SizeType newSize, int)

Resize a Storage object to size elements. Existing elements are not preserved

Parameters: size – New size of the Storage object

SizeType size() const noexcept

Return the number of elements in the FixedStorage object

Returns: Number of elements in the FixedStorage object

FixedStorage copy() const

Create a copy of the FixedStorage object.

Returns: Copy of the FixedStorage object

ConstReference operator[](SizeType index) const

Const access to the element at index index

Parameters: index – Index of the element to access
Returns: Const reference to the element at index index

Reference operator[](SizeType index)

Access to the element at index index

Parameters: index – Index of the element to access
Returns: Reference to the element at index index

Pointer data() const noexcept

Iterator begin() noexcept

Iterator end() noexcept

ConstIterator begin() const noexcept

ConstIterator end() const noexcept

ConstIterator cbegin() const noexcept

ConstIterator cend() const noexcept

ReverseIterator rbegin() noexcept

ReverseIterator rend() noexcept

ConstReverseIterator rbegin() const noexcept

ConstReverseIterator rend() const noexcept

ConstReverseIterator crbegin() const noexcept

ConstReverseIterator crend() const noexcept

template<typename ShapeType> auto defaultShape() -> ShapeType

Public Static Functions

template<typename ShapeType> static ShapeType defaultShape()

Public Static Attributes

static constexpr SizeType Size = product<Size_...>()

Private Members

std::array<Scalar, Size> m_data#

template<typename Scalar_> class Storage

#include <storage.hpp>

Public Types

using Scalar = Scalar_

using Packet = typename typetraits::TypeInfo<Scalar>::Packet

using Pointer = Scalar*

using ConstPointer = const Scalar*

using Reference = Scalar&

using ConstReference = const Scalar&

using SizeType = size_t

using DifferenceType = ptrdiff_t

using Iterator = Pointer

using ConstIterator = ConstPointer

using ReverseIterator = std::reverse_iterator<Iterator>

using ConstReverseIterator = std::reverse_iterator<ConstIterator>

Public Functions

Storage() = default: Default constructor.

explicit Storage(SizeType size)

Create a Storage object with size elements

Parameters: size – Number of elements to allocate

explicit Storage(Scalar *begin, Scalar *end, bool ownsData)

Storage(SizeType size, ConstReference value)

Create a Storage object with size elements, each initialized to value.

Parameters

size – Number of elements to allocate
value – Value to initialize each element to

Storage(const Storage &other)

Create a Storage object from another Storage object. Additionally a custom allocator can be used. The data is NOT copied — it is referenced by the new Storage object. For a deep copy, use the copy() method.

Parameters: other – Storage object to copy

Storage(Storage &&other) noexcept

Move a Storage object into this object.

Parameters: other – Storage object to move

template<typename V> Storage(const std::initializer_list<V> &list)

Create a Storage object from an std::initializer_list

Template Parameters

V – Type of the elements in the initializer list

Parameters

list – Initializer list to copy
alloc – Allocator to use

template<typename V> explicit Storage(const std::vector<V> &vec)

Create a Storage object from a std::vector

Template Parameters: V – Type of the elements in the vector
Parameters: vec – Vector to copy

Storage &operator=(const Storage &other)

Assignment operator for a Storage object

Parameters: other – Storage object to copy
Returns: *this

Storage &operator=(Storage &&other) noexcept

Move assignment operator for a Storage object

Parameters: other – Storage object to move
Returns: *this

~Storage(): Free a Storage object.

Storage toHostStorage() const

Return a Storage object on the host with the same data as this Storage object (mainly for use with CUDA or OpenCL)

Returns

Storage toHostStorageUnsafe() const

Same as toHostStorage() but does not necessarily copy the data.

Returns: Storage object on the host

Storage copy() const

Create a deep copy of this Storage object.

Returns: Deep copy of this Storage object

void resize(SizeType newSize)

Resize a Storage object to size elements. Existing elements are preserved.

Parameters: size – New size of the Storage object

void resize(SizeType newSize, int)

Resize a Storage object to size elements. Existing elements are not preserved

Parameters: size – New size of the Storage object

SizeType size() const noexcept

Return the number of elements in the Storage object

Returns

ConstReference operator[](SizeType index) const

Const access to the element at index index

Parameters: index – Index of the element to access
Returns: Const reference to the element at index index

Reference operator[](SizeType index)

Access to the element at index index

Parameters: index – Index of the element to access
Returns: Reference to the element at index index

Pointer data() const noexcept

Pointer begin() noexcept

Pointer end() noexcept

ConstPointer begin() const noexcept

ConstPointer end() const noexcept

ConstIterator cbegin() const noexcept

ConstIterator cend() const noexcept

ReverseIterator rbegin() noexcept

ReverseIterator rend() noexcept

ConstReverseIterator rbegin() const noexcept

ConstReverseIterator rend() const noexcept

ConstReverseIterator crbegin() const noexcept

ConstReverseIterator crend() const noexcept

template<typename V> auto fromData(const std::initializer_list<V> &list) -> Storage

template<typename V> auto fromData(const std::vector<V> &vec) -> Storage

template<typename ShapeType> auto defaultShape() -> ShapeType

Public Static Functions

template<typename V> static Storage fromData(const std::initializer_list<V> &vec)

template<typename V> static Storage fromData(const std::vector<V> &vec)

template<typename ShapeType> static ShapeType defaultShape()

Public Static Attributes

static constexpr uint64_t packetWidth = typetraits::TypeInfo<Scalar>::packetWidth

Private Functions

template<typename P> void initData(P begin, P end)#

Copy data from begin to end into this Storage object

Template Parameters

P – Pointer type

Parameters

begin – Beginning of data to copy
end – End of data to copy

template<typename P> void initData(P begin, SizeType size)#

Private Members

Pointer m_begin = nullptr#

SizeType m_size = 0#

bool m_ownsData = true#

namespace detail

Functions

template<typename T> void safeDeallocate(T *ptr, size_t size)

Safely deallocate memory for size elements, using an std::allocator alloc. If the object cannot be trivially destroyed, the destructor will be called on each element of the data, ensuring that it is safe to free the allocated memory.

Template Parameters

A – The allocator type

Parameters

alloc – The allocator object
ptr – The pointer to free
size – The number of elements of type in the memory block

template<typename T> T *safeAllocate(size_t size)

Safely allocate memory for size elements using the allocator alloc. If the data can be trivially default constructed, then the constructor is not called and no data is initialized. Otherwise, the correct default constructor will be called for each element in the data, making sure the returned pointer is safe to use.

See also

safeDeallocate

Template Parameters

A – The allocator type to use

Parameters

alloc – The allocator object to use
size – Number of elements to allocate

Returns

Pointer to the first element

template<typename T, typename V> void fastCopy (T *__restrict dst, const V *__restrict src, size_t size)

namespace typetraits

Functions

LIBRAPID_DEFINE_AS_TYPE(typename Scalar, Storage<Scalar>)#

template<typename T> struct IsFixedStorage : public std::false_type

template<typename Scalar, size_t... Size> struct IsFixedStorage<FixedStorage<Scalar, Size...>> : public std::true_type

template<typename T> struct IsStorage : public std::false_type

template<typename Scalar> struct IsStorage<Storage<Scalar>> : public std::true_type

template<typename Scalar_, size_t... Dims> struct TypeInfo<FixedStorage<Scalar_, Dims...>>

#include <storage.hpp>

Public Types

using Scalar = Scalar_

using Backend = backend::CPU

Public Static Attributes

static constexpr bool isLibRapidType = true

template<typename Scalar_> struct TypeInfo<Storage<Scalar_>>

#include <storage.hpp>

Public Types

using Scalar = Scalar_

using Backend = backend::CPU

Public Static Attributes

static constexpr bool isLibRapidType = true

OpenCL Storage#

CUDA Storage#

Defines

CUDA_REF_OPERATOR(OP)

CUDA_REF_OPERATOR_NO_ASSIGN(OP)

namespace librapid

template<typename Scalar_> class CudaStorage

#include <cudaStorage.hpp>

Public Types

using Scalar = Scalar_

using Pointer = Scalar*

using ConstPointer = const Scalar*

using Reference = Scalar&

using ConstReference = const Scalar&

using DifferenceType = std::ptrdiff_t

using SizeType = std::size_t

Public Functions

CudaStorage() = default: Default constructor — initializes with nullptr.

explicit CudaStorage(SizeType size)

Create a CudaStorage object with elements. The data is not initialized.

Parameters: size – Number of elements

CudaStorage(SizeType size, ConstReference value)

Create a CudaStorage object with elements. The data is initialized to value.

Parameters

size – Number of elements
value – Value to fill with

CudaStorage(Scalar *begin, SizeType size, bool ownsData)

CudaStorage(const CudaStorage &other)

Create a new CudaStorage object from an existing one.

Parameters: other – The CudaStorage to copy

CudaStorage(CudaStorage &&other) noexcept

Create a new CudaStorage object from a temporary one, moving the data

Parameters: other – The array to move

CudaStorage(const std::initializer_list<Scalar> &list)

Create a CudaStorage object from an std::initializer_list

Parameters: list – Initializer list of elements

explicit CudaStorage(const std::vector<Scalar> &vec)

Create a CudaStorage object from an std::vector of values

Parameters: vec – The vector to fill with

CudaStorage &operator=(const CudaStorage &other)

Assignment operator for a CudaStorage object

Parameters: other – CudaStorage object to copy
Returns: *this

CudaStorage &operator=(CudaStorage &&other) noexcept

Move assignment operator for a CudaStorage object

Parameters: other – CudaStorage object to move
Returns: *this

~CudaStorage(): Free a CudaStorage object.

CudaStorage copy() const

Create a deep copy of this CudaStorage object.

Returns: Deep copy of this CudaStorage object

void resize(SizeType newSize)

Resize a CudaStorage object to size elements. Existing elements are preserved where possible.

See also

resize(SizeType, int)

Parameters: size – Number of elements

void resize(SizeType newSize, int)

Resize a CudaStorage object to size elements. Existing elements are not preserved. This method of resizing is faster and more efficient than the version which preserves the original data, but of course, this has the drawback that data will be lost.

Parameters: size – Number of elements

SizeType size() const noexcept

Return the number of elements in the CudaStorage object.

Returns: The number of elements

detail::CudaRef<Scalar> operator[](SizeType index) const

detail::CudaRef<Scalar> operator[](SizeType index)

Pointer data() const noexcept

Return the underlying pointer to the data

Returns: The underlying pointer to the data

Pointer begin() const noexcept

Returns the pointer to the first element of the CudaStorage object

Returns: Pointer to the first element of the CudaStorage object

Pointer end() const noexcept

Returns the pointer to the last element of the CudaStorage object

Returns: A pointer to the last element of the CudaStorage

Public Static Functions

template<typename ShapeType> static ShapeType defaultShape()

static CudaStorage fromData(const std::initializer_list<Scalar> &vec)

static CudaStorage fromData(const std::vector<Scalar> &vec)

Private Functions

template<typename P> void initData(P begin, P end)#

Template Parameters

P – Pointer type

Parameters

begin – Beginning of data to copy
end – End of data to copy

Private Members

Pointer m_begin = nullptr#

size_t m_size#

bool m_ownsData = true#

namespace detail

Functions

template<typename LHS, typename RHS> auto operator+(const CudaRef<LHS> &lhs, const RHS &rhs)

template<typename LHS, typename RHS> auto operator+(const LHS &lhs, const CudaRef<RHS> &rhs)

template<typename LHS, typename RHS> auto operator+(const CudaRef<LHS> &lhs, const CudaRef<RHS> &rhs)

template<typename LHS, typename RHS> auto operator+=(CudaRef<LHS> &lhs, const RHS &rhs)

template<typename LHS, typename RHS> auto operator+=(CudaRef<LHS> &lhs, const CudaRef<RHS> &rhs)

template<typename LHS, typename RHS> auto operator-(const CudaRef<LHS> &lhs, const RHS &rhs)

template<typename LHS, typename RHS> auto operator-(const LHS &lhs, const CudaRef<RHS> &rhs)

template<typename LHS, typename RHS> auto operator-(const CudaRef<LHS> &lhs, const CudaRef<RHS> &rhs)

template<typename LHS, typename RHS> auto operator-=(CudaRef<LHS> &lhs, const RHS &rhs)

template<typename LHS, typename RHS> auto operator-=(CudaRef<LHS> &lhs, const CudaRef<RHS> &rhs)

template<typename LHS, typename RHS> auto operator*(const CudaRef<LHS> &lhs, const RHS &rhs)

template<typename LHS, typename RHS> auto operator*(const LHS &lhs, const CudaRef<RHS> &rhs)

template<typename LHS, typename RHS> auto operator*(const CudaRef<LHS> &lhs, const CudaRef<RHS> &rhs)

template<typename LHS, typename RHS> auto operator*=(CudaRef<LHS> &lhs, const RHS &rhs)

template<typename LHS, typename RHS> auto operator*=(CudaRef<LHS> &lhs, const CudaRef<RHS> &rhs)

template<typename LHS, typename RHS> auto operator/(const CudaRef<LHS> &lhs, const RHS &rhs)

template<typename LHS, typename RHS> auto operator/(const LHS &lhs, const CudaRef<RHS> &rhs)

template<typename LHS, typename RHS> auto operator/(const CudaRef<LHS> &lhs, const CudaRef<RHS> &rhs)

template<typename LHS, typename RHS> auto operator/=(CudaRef<LHS> &lhs, const RHS &rhs)

template<typename LHS, typename RHS> auto operator/=(CudaRef<LHS> &lhs, const CudaRef<RHS> &rhs)

template<typename LHS, typename RHS> auto operator%(const CudaRef<LHS> &lhs, const RHS &rhs)

template<typename LHS, typename RHS> auto operator%(const LHS &lhs, const CudaRef<RHS> &rhs)

template<typename LHS, typename RHS> auto operator%(const CudaRef<LHS> &lhs, const CudaRef<RHS> &rhs)

template<typename LHS, typename RHS> auto operator%=(CudaRef<LHS> &lhs, const RHS &rhs)

template<typename LHS, typename RHS> auto operator%=(CudaRef<LHS> &lhs, const CudaRef<RHS> &rhs)

template<typename LHS, typename RHS> auto operator^(const CudaRef<LHS> &lhs, const RHS &rhs)

template<typename LHS, typename RHS> auto operator^(const LHS &lhs, const CudaRef<RHS> &rhs)

template<typename LHS, typename RHS> auto operator^(const CudaRef<LHS> &lhs, const CudaRef<RHS> &rhs)

template<typename LHS, typename RHS> auto operator^=(CudaRef<LHS> &lhs, const RHS &rhs)

template<typename LHS, typename RHS> auto operator^=(CudaRef<LHS> &lhs, const CudaRef<RHS> &rhs)

template<typename LHS, typename RHS> auto operator&(const CudaRef<LHS> &lhs, const RHS &rhs)

template<typename LHS, typename RHS> auto operator&(const LHS &lhs, const CudaRef<RHS> &rhs)

template<typename LHS, typename RHS> auto operator&(const CudaRef<LHS> &lhs, const CudaRef<RHS> &rhs)

template<typename LHS, typename RHS> auto operator&=(CudaRef<LHS> &lhs, const RHS &rhs)

template<typename LHS, typename RHS> auto operator&=(CudaRef<LHS> &lhs, const CudaRef<RHS> &rhs)

template<typename LHS, typename RHS> auto operator|(const CudaRef<LHS> &lhs, const RHS &rhs)

template<typename LHS, typename RHS> auto operator|(const LHS &lhs, const CudaRef<RHS> &rhs)

template<typename LHS, typename RHS> auto operator|(const CudaRef<LHS> &lhs, const CudaRef<RHS> &rhs)

template<typename LHS, typename RHS> auto operator|=(CudaRef<LHS> &lhs, const RHS &rhs)

template<typename LHS, typename RHS> auto operator|=(CudaRef<LHS> &lhs, const CudaRef<RHS> &rhs)

template<typename LHS, typename RHS> auto operator<<(const CudaRef<LHS> &lhs, const RHS &rhs)

template<typename LHS, typename RHS> auto operator<<(const LHS &lhs, const CudaRef<RHS> &rhs)

template<typename LHS, typename RHS> auto operator<<(const CudaRef<LHS> &lhs, const CudaRef<RHS> &rhs)

template<typename LHS, typename RHS> auto operator<<=(CudaRef<LHS> &lhs, const RHS &rhs)

template<typename LHS, typename RHS> auto operator<<=(CudaRef<LHS> &lhs, const CudaRef<RHS> &rhs)

template<typename LHS, typename RHS> auto operator>>(const CudaRef<LHS> &lhs, const RHS &rhs)

template<typename LHS, typename RHS> auto operator>>(const LHS &lhs, const CudaRef<RHS> &rhs)

template<typename LHS, typename RHS> auto operator>>(const CudaRef<LHS> &lhs, const CudaRef<RHS> &rhs)

template<typename LHS, typename RHS> auto operator>>=(CudaRef<LHS> &lhs, const RHS &rhs)

template<typename LHS, typename RHS> auto operator>>=(CudaRef<LHS> &lhs, const CudaRef<RHS> &rhs)

template<typename LHS, typename RHS> auto operator==(const CudaRef<LHS> &lhs, const RHS &rhs)

template<typename LHS, typename RHS> auto operator==(const LHS &lhs, const CudaRef<RHS> &rhs)

template<typename LHS, typename RHS> auto operator==(const CudaRef<LHS> &lhs, const CudaRef<RHS> &rhs)

template<typename LHS, typename RHS> auto operator!=(const CudaRef<LHS> &lhs, const RHS &rhs)

template<typename LHS, typename RHS> auto operator!=(const LHS &lhs, const CudaRef<RHS> &rhs)

template<typename LHS, typename RHS> auto operator!=(const CudaRef<LHS> &lhs, const CudaRef<RHS> &rhs)

template<typename LHS, typename RHS> auto operator<(const CudaRef<LHS> &lhs, const RHS &rhs)

template<typename LHS, typename RHS> auto operator<(const LHS &lhs, const CudaRef<RHS> &rhs)

template<typename LHS, typename RHS> auto operator<(const CudaRef<LHS> &lhs, const CudaRef<RHS> &rhs)

template<typename LHS, typename RHS> auto operator>(const CudaRef<LHS> &lhs, const RHS &rhs)

template<typename LHS, typename RHS> auto operator>(const LHS &lhs, const CudaRef<RHS> &rhs)

template<typename LHS, typename RHS> auto operator>(const CudaRef<LHS> &lhs, const CudaRef<RHS> &rhs)

template<typename LHS, typename RHS> auto operator<=(const CudaRef<LHS> &lhs, const RHS &rhs)

template<typename LHS, typename RHS> auto operator<=(const LHS &lhs, const CudaRef<RHS> &rhs)

template<typename LHS, typename RHS> auto operator<=(const CudaRef<LHS> &lhs, const CudaRef<RHS> &rhs)

template<typename LHS, typename RHS> auto operator>=(const CudaRef<LHS> &lhs, const RHS &rhs)

template<typename LHS, typename RHS> auto operator>=(const LHS &lhs, const CudaRef<RHS> &rhs)

template<typename LHS, typename RHS> auto operator>=(const CudaRef<LHS> &lhs, const CudaRef<RHS> &rhs)

template<typename T> T *__restrict cudaSafeAllocate (size_t size)

template<typename T> void cudaSafeDeallocate (T *__restrict data)

template<typename T> class CudaRef

#include <cudaStorage.hpp>

Public Types

using PtrType = T*

Public Functions

inline CudaRef(PtrType ptr, size_t offset)

inline CudaRef &operator=(const T &val)

inline T get() const

template<typename CAST> inline operator CAST() const

template<typename T_, typename Char, typename Ctx> inline void str(const fmt::formatter<T_, Char> &format, Ctx &ctx) const

Private Members

T *m_ptr#

size_t m_offset#

namespace typetraits

Functions

LIBRAPID_DEFINE_AS_TYPE(typename Scalar_, CudaStorage<Scalar_>)#

template<typename T> struct IsCudaStorage : public std::false_type: #include <cudaStorage.hpp>

template<typename Scalar> struct IsCudaStorage<CudaStorage<Scalar>> : public std::true_type: #include <cudaStorage.hpp>

template<typename Scalar_> struct TypeInfo<CudaStorage<Scalar_>>

#include <cudaStorage.hpp>

Public Types

using Scalar = Scalar_

using Backend = backend::CUDA

Public Static Attributes

static constexpr bool isLibRapidType = true

Vectors#

LibRapid provides a highly optimised fixed-size vector library which supports all primitive types as well as user-defined ones (assuming they implement the required operations).

Vector Listing#

Warning

doxygenclass: Cannot find class “librapid::GenericVector” in doxygen xml output for project “librapid” from directory: ../xml

Complex Numbers#

Documentation

View the API and documentation for complex numbers.

Examples

See some examples of LibRapid’s complex number library in action

Implementation Details

Learn about the implementation of complex numbers in LibRapid

Complex Number Listing#

Defines

IS_SCALAR(T)

namespace librapid

Functions

template<typename T> auto operator-(const Complex<T> &other) -> Complex<T>#

Negate a complex number.

Template Parameters: T – Scalar type of the complex number
Parameters: other – Complex number to negate
Returns: Negated complex number

template<typename L, typename R> auto operator+(const Complex<L> &left, const Complex<R> &right)#

Add two complex numbers.

Add two complex numbers together, returning the result

Template Parameters

L – Scalar type of LHS
R – Scalar type of RHS

Parameters

left – LHS complex number
right – RHS complex number

Returns

Sum of LHS and RHS

template<typename T, typename R> auto operator+(const Complex<T> &left, const R &right)#

Add a complex number and a scalar.

Add a real number to the real component of a complex number, returning the result

Template Parameters

T – Scalar type of the complex number
R – Type of the real number

Parameters

left – LHS complex number
right – RHS scalar

Returns

Sum of LHS and RHS

template<typename R, typename T> auto operator+(const R &real, const Complex<T> &right)#

Add a scalar to a complex number.

Add a real number to the real component of a complex number, returning the result

Template Parameters

R – Type of the real number
T – Scalar type of the complex number

Parameters

real – LHS scalar
right – RHS complex number

Returns

Sum of LHS and RHS

template<typename L, typename R> auto operator-(const Complex<L> &left, const Complex<R> &right)#

Subtract a complex number from another complex number.

Subtract the real and imaginary components of the RHS complex number from the corresponding components of the LHS complex number, returning the result

Template Parameters

L – Scalar type of the LHS complex number
R – Scalar type of the RHS complex number

Parameters

left – LHS complex number
right – RHS complex number

Returns

Difference of LHS and RHS

template<typename T, typename R> auto operator-(const Complex<T> &left, const R &real)#

Subtract a scalar from a complex number.

Subtract a real number from the real component of a complex number, returning the result

Template Parameters

T – Scalar type of the complex number
R – Type of the real number

Parameters

left – LHS complex number
real – RHS scalar

Returns

Difference of LHS and RHS

template<typename R, typename T> auto operator-(const R &real, const Complex<T> &right)#

Subtract a complex number from a scalar.

Subtract the real and imaginary components of the RHS complex number from a real number, returning the result

Template Parameters

T – Scalar type of the complex number
R – Type of the real number

Parameters

real – LHS scalar
right – RHS complex number

Returns

Difference of LHS and RHS

template<typename L, typename R> auto operator*(const Complex<L> &left, const Complex<R> &right)#

Multiply two complex numbers.

Multiply the LHS and RHS complex numbers, returning the result

Template Parameters

L – Scalar type of the LHS complex number
R – Scalar type of the RHS complex number

Parameters

left – LHS complex number
right – RHS complex number

Returns

Product of LHS and RHS

template<typename T, typename R> auto operator*(const Complex<T> &left, const R &real)#

Multiply a complex number by a scalar.

Multiply the real and imaginary components of a complex number by a real number, returning the result

Template Parameters

T – Scalar type of the complex number
R – Type of the real number

Parameters

left – LHS complex number
real – RHS scalar

Returns

Product of LHS and RHS

template<typename R, typename T> auto operator*(const R &real, const Complex<T> &right)#

Multiply a scalar by a complex number.

Multiply a real number by the real and imaginary components of a complex number, returning the result

Template Parameters

R – Type of the real number
T – Scalar type of the complex number

Parameters

real – LHS scalar
right – RHS complex number

Returns

Product of LHS and RHS

template<typename L, typename R> auto operator/(const Complex<L> &left, const Complex<R> &right)#

Divide two complex numbers.

Divide the LHS complex number by the RHS complex number, returning the result

Template Parameters

L – Scalar type of the LHS complex number
R – Scalar type of the RHS complex number

Parameters

left – LHS complex number
right – RHS complex number

Returns

Quotient of LHS and RHS

template<typename T, typename R> auto operator/(const Complex<T> &left, const R &real)#

Divide a complex number by a scalar.

Divide the real and imaginary components of a complex number by a real number, returning the result

Template Parameters

T – Scalar type of the complex number
R – Type of the real number

Parameters

left – LHS complex number
real – RHS scalar

Returns

Quotient of LHS and RHS

template<typename R, typename T> auto operator/(const R &real, const Complex<T> &right)#

Divide a scalar by a complex number.

Divide a real number by the real and imaginary components of a complex number, returning the result

Template Parameters

T – Scalar type of the complex number
R – Type of the real number

Parameters

real – LHS scalar
right – RHS complex number

Returns

Quotient of LHS and RHS

template<typename L, typename R> constexpr bool operator==(const Complex<L> &left, const Complex<R> &right)

Equality comparison of two complex numbers.

Template Parameters

L – Scalar type of LHS complex number
R – Scalar type of RHS complex number

Parameters

left – LHS complex number
right – RHS complex number

Returns

true if equal, false otherwise

template<typename T> constexpr bool operator==(const Complex<T> &left, T &right)

Equality comparison of complex number and scalar.

Compares the real component of the complex number to the scalar, and the imaginary component to zero. Returns true if and only if both comparisons are true.

Template Parameters

T – Scalar type of complex number

Parameters

left – LHS complex number
right – RHS scalar

Returns

true if equal, false otherwise

template<typename T> constexpr bool operator==(const T &left, const Complex<T> &right)

Equality comparison of scalar and complex number.

Compares the real component of the complex number to the scalar, and the imaginary component to zero. Returns true if and only if both comparisons are true.

Template Parameters

T – Scalar type of complex number

Parameters

left – LHS scalar
right – RHS complex number

Returns

true if equal, false otherwise

template<typename T> constexpr bool operator!=(const Complex<T> &left, const Complex<T> &right)

Inequality comparison of two complex numbers.

Template Parameters

T – Scalar type of complex number

Parameters

left – LHS complex number
right – RHS complex number

Returns

true if equal, false otherwise

template<typename T> constexpr bool operator!=(const Complex<T> &left, T &right)

Inequality comparison of complex number and scalar.

See also

operator==(const Complex<T> &, T &)

Template Parameters

T – Scalar type of complex number

Parameters

left – LHS complex number
right – RHS scalar

Returns

true if equal, false otherwise

template<typename T> constexpr bool operator!=(const T &left, const Complex<T> &right)

Inequality comparison of scalar and complex number.

Template Parameters

T – Scalar type of complex number

Parameters

left – LHS scalar
right – RHS complex number

Returns

true if equal, false otherwise

template<typename T> T real(const Complex<T> &val)

Return \( \mathrm{Re}(z) \).

Template Parameters: T – Scalar type of the complex number
Parameters: val – Complex number
Returns: Real component of the complex number

template<typename T> T imag(const Complex<T> &val)

Return \( \mathrm{Im}(z) \).

Template Parameters: T – Scalar type of the complex number
Parameters: val – Complex number
Returns: Imaginary component of the complex number

template<typename T> Complex<T> sqrt(const Complex<T> &val)

Return \( \sqrt{z} \).

Template Parameters: T – Scalar type of the complex number
Parameters: val – Complex number
Returns: Square root of the complex number

template<typename T> T abs(const Complex<T> &val)

Return \( \sqrt{\mathrm{Re}(z)^2 + \mathrm{Im}(z)^2} \).

Template Parameters: T – Scalar type of the complex number
Parameters: val – Complex number
Returns: Absolute value of the complex number

template<typename T> Complex<T> conj(const Complex<T> &val)

Returns \(z^{*}\).

Template Parameters: T – Scalar type of the complex number
Parameters: val – Complex number
Returns: Complex conjugate of the complex number

template<typename T> Complex<T> acos(const Complex<T> &other)

Compute the complex arc cosine of a complex number.

This function computes the complex arc cosine of the input complex number, \(\text{acos}(z)\)

The algorithm handles NaN and infinity values, and avoids overflow.

Template Parameters: T – Scalar type of the complex number
Parameters: other – Input complex number
Returns: Complex arc cosine of the input complex number

template<typename T> Complex<T> acosh(const Complex<T> &other)

Compute the complex hyperbolic arc cosine of a complex number.

This function computes the complex area hyperbolic cosine of the input complex number, \( \text{acosh}(z) \)

The algorithm handles NaN and infinity values, and avoids overflow.

Template Parameters: T – Scalar type of the complex number
Parameters: other – Input complex number
Returns: Complex area hyperbolic cosine of the input complex number

template<typename T> Complex<T> asinh(const Complex<T> &other)

Compute the complex arc hyperbolic sine of a complex number.

This function computes the complex arc hyperbolic sine of the input complex number, \( \text{asinh}(z) \)

The algorithm handles NaN and infinity values, and avoids overflow.

Template Parameters: T – Scalar type of the complex number
Parameters: other – Input complex number
Returns: Complex arc hyperbolic sine of the input complex number

template<typename T> Complex<T> asin(const Complex<T> &other)

Compute the complex arc sine of a complex number.

This function computes the complex arc sine of the input complex number, \( \text{asin}(z) \)

It calculates the complex arc sine by using the complex hyperbolic sine function.

See also

asinh

Template Parameters: T – Scalar type of the complex number
Parameters: other – Input complex number
Returns: Complex arc sine of the input complex number

template<typename T> Complex<T> atanh(const Complex<T> &other)

Compute the complex arc hyperbolic tangent of a complex number.

This function computes the complex arc hyperbolic tangent of the input complex number, \( \text{atanh}(z) \)

This function performs error checking and supports NaNs and Infs.

Template Parameters: T – Scalar type of the complex number
Parameters: other – Input complex number
Returns: Complex arc hyperbolic tangent of the input complex number

template<typename T> Complex<T> atan(const Complex<T> &other)

Compute the complex arc tangent of a complex number.

This function computes the complex arc tangent of the input complex number, \( \text{atan}(z) \)

The algorithm handles NaN and infinity values, and avoids overflow.

Template Parameters: T – Scalar type of the complex number
Parameters: other – Input complex number
Returns: Complex arc tangent of the input complex number

template<typename T> Complex<T> cosh(const Complex<T> &other)

Compute the complex hyperbolic cosine of a complex number.

This function computes the complex hyperbolic cosine of the input complex number, \( \text{cosh}(z) \)

Template Parameters: T – Scalar type of the complex number
Parameters: other – Input complex number
Returns: Complex hyperbolic cosine of the input complex number

template<typename T> Complex<T> polarPositiveNanInfZeroRho(const T &rho, const T &theta)

template<typename T> Complex<T> exp(const Complex<T> &other)

Compute the complex exponential of a complex number.

This function computes the complex exponential of the input complex number, \( e^z \)

The algorithm handles NaN and infinity values.

Template Parameters: T – Scalar type of the complex number
Parameters: other – Input complex number
Returns: Complex exponential of the input complex number

template<typename T> Complex<T> exp2(const Complex<T> &other)

Compute the complex exponential base 2 of a complex number.

See also

exp

Template Parameters: T – Scalar type of the complex number
Parameters: other – Input complex number
Returns: Complex exponential base 2 of the input complex number

template<typename T> Complex<T> exp10(const Complex<T> &other)

Compute the complex exponential base 10 of a complex number.

See also

exp

Template Parameters: T – Scalar type of the complex number
Parameters: other – Input complex number
Returns: Complex exponential base 10 of the input complex number

template<typename T> T _fabs(const Complex<T> &other, int64_t *exp)

template<typename T> T _logAbs(const Complex<T> &other) noexcept

template<> mpfr _logAbs(const Complex<mpfr> &other) noexcept

template<> float _logAbs(const Complex<float> &other) noexcept

template<typename T> Complex<T> log(const Complex<T> &other)

Calculates the natural logarithm of a complex number.

Template Parameters: T – Scalar type
Parameters: other – Complex number
Returns: Natural logarithm of the complex number

template<typename T, typename B> Complex<T> log(const Complex<T> &other, const Complex<T> &base)

Calculates the logarithm of a complex number with a complex base.

\( \log_{\mathrm{base}}(z) = \log(z) / \log(\mathrm{base}) \)

See also

log

Template Parameters

T – Scalar type
B – Base type

Parameters

other – Complex number
base – Base of the logarithm

Returns

Logarithm of the complex number with the given base

template<typename T, typename B> Complex<T> log(const Complex<T> &other, const B &base)

Calculates the logarithm of a complex number with a real base.

\( \log_{\mathrm{base}}(z) = \log(z) / \log(\mathrm{base}) \)

See also

log

Template Parameters

T – Scalar type of the complex number
B – Scalar type of the base

Parameters

other – Complex number
base – Base of the logarithm (real)

Returns

Logarithm of the complex number with the given base

template<typename T> Complex<T> _pow(const T &left, const T &right)

template<typename T, typename V> Complex<T> pow(const Complex<T> &left, const V &right)

Calculate \( \text{left}^{\text{right}} \) for a complex-valued left-hand side.

Template Parameters

T – Value type for the left-hand side
V – Value type for the right-hand side

Parameters

left – Complex base
right – Real exponent

Returns

\( \text{left}^{\text{right}} \)

template<typename T, typename V> Complex<T> pow(const V &left, const Complex<T> &right)

Calculate \( \text{left}^{\text{right}} \) for a complex-valued right-hand side.

Template Parameters

T – Value type for the left-hand side
V – Value type for the right-hand side

Parameters

left – Real base
right – Complex exponent

Returns

\( \text{left}^{\text{right}} \)

template<typename T> Complex<T> pow(const Complex<T> &left, const Complex<T> &right)

Calculate \( \text{left}^{\text{right}} \) for complex numbers.

Template Parameters

T – Complex number component type

Parameters

left – Complex base
right – Complex exponent

Returns

\( \text{left}^{\text{right}} \)

template<typename T> Complex<T> sinh(const Complex<T> &other)

Calculate the hyperbolic sine of a complex number.

Template Parameters: T – Scalar type
Parameters: other – Complex number
Returns: \( \sinh(z) \)

template<typename T> Complex<T> cbrt(const Complex<T> &other)

template<typename T> Complex<T> tanh(const Complex<T> &other)

Calculate the hyperbolic tangent of a complex number.

This function supports propagation of NaNs and Infs.

Template Parameters: T – Scalar type
Parameters: other – Complex number
Returns: \( \tanh(z) \)

template<typename T> T arg(const Complex<T> &other)

Return the phase angle of a complex value as a real.

This function calls \( \text{atan2}(\text{imag}(z), \text{real}(z)) \).

See also

atan2

Template Parameters: T – Scalar type
Parameters: other – Complex number
Returns: \( \arg(z) \)

template<typename T> Complex<T> proj(const Complex<T> &other)

Project a complex number onto the Riemann sphere.

Template Parameters: T – Scalar type
Parameters: other – Complex number
Returns: \( \text{proj}(z) \)

template<typename T> Complex<T> cos(const Complex<T> &other)

Calculate the cosine of a complex number.

Template Parameters: T – Scalar type
Parameters: other – Complex number
Returns: \( \cos(z) \)

template<typename T> Complex<T> csc(const Complex<T> &other)

Calculate the cosecant of a complex number.

Template Parameters: T – Scalar type
Parameters: other – Complex number
Returns: \( \csc(z) \)

template<typename T> Complex<T> sec(const Complex<T> &other)

Calculate the secant of a complex number.

Template Parameters: T – Scalar type
Parameters: other – Complex number
Returns: \( \sec(z) \)

template<typename T> Complex<T> cot(const Complex<T> &other)

Calculate the cotangent of a complex number.

Template Parameters: T – Scalar type
Parameters: other – Complex number
Returns: \( \cot(z) \)

template<typename T> Complex<T> acsc(const Complex<T> &other)

Calculate the arc cosecant of a complex number.

Template Parameters: T – Scalar type
Parameters: other – Complex number
Returns: \( \operatorname{arccsc}(z) \)

template<typename T> Complex<T> asec(const Complex<T> &other)

Calculate the arc secant of a complex number.

Template Parameters: T – Scalar type
Parameters: other – Complex number
Returns: \( \operatorname{arcsec}(z) \)

template<typename T> Complex<T> acot(const Complex<T> &other)

Calculate the arc cotangent of a complex number.

Template Parameters: T – Scalar type
Parameters: other – Complex number
Returns: \( \operatorname{arccot}(z) \)

template<typename T> Complex<T> log2(const Complex<T> &other)

Calculate the logarithm base 2 of a complex number.

Template Parameters: T – Scalar type
Parameters: other – Complex number
Returns: \( \log_2(z) \)

template<typename T> Complex<T> log10(const Complex<T> &other)

Calculate the logarithm base 10 of a complex number.

Template Parameters: T – Scalar type
Parameters: other – Complex number
Returns: \( \log_{10}(z) \)

template<typename T> T norm(const Complex<T> &other)

Calculate the magnitude squared of a complex number.

Template Parameters: T – Scalar type
Parameters: other – Complex number
Returns: \( |z|^2 \)

template<typename T> Complex<T> polar(const T &rho, const T &theta)

Return a complex number from polar coordinates.

Given a radius, rho, and an angle, theta, this function returns the complex number \( \rho e^{i\theta} \).

The function returns NaN, infinity or zero based on the input values of rho.

Template Parameters

T – Scalar type of the complex number

Parameters

rho – Radius of the polar coordinate system
theta – Angle of the polar coordinate system

Returns

Complex number in polar form.

template<typename T> Complex<T> sin(const Complex<T> &other)

Compute the sine of a complex number.

Template Parameters: T – Scalar type
Parameters: other – Complex number
Returns: \( \sin(z) \)

template<typename T> Complex<T> tan(const Complex<T> &other)

Compute the tangent of a complex number.

Template Parameters: T – Scalar type
Parameters: other – Complex number
Returns: \( \tan(z) \)

template<typename T> Complex<T> floor(const Complex<T> &other)

Round the real and imaginary parts of a complex number towards \( -\infty \).

Template Parameters: T – Scalar type
Parameters: other – Complex number
Returns: \( (\lfloor\operatorname{real}(z)\rfloor,\lfloor\operatorname{imag}(z)\rfloor )\)

template<typename T> Complex<T> ceil(const Complex<T> &other)

Round the real and imaginary parts of a complex number towards \( +\infty \).

Template Parameters: T – Scalar type
Parameters: other – Complex number
Returns: \((\lceil\operatorname{real}(z)\rceil,\lceil\operatorname{imag}(z)\rceil )\)

template<typename T> auto random(const Complex<T> &min, const Complex<T> &max, uint64_t seed = -1) -> Complex<T>

Generate a random complex number between two given complex numbers.

This function generates a random complex number in the range [min, max], where min and max are given as input. The function uses a default seed if none is provided.

Template Parameters

T – Scalar type of the complex number

Parameters

min – Minimum complex number
max – Maximum complex number
seed – Seed for the random number generator

Returns

Random complex number between min and max

template<typename T = double> class Complex

#include <complex.hpp>

A class representing a complex number of the form \(a + bi\), where \(a\) and \(b\) are real numbers.

This class represents a complex number of the form \(a + bi\), where \(a\) and \(b\) are real numbers. The class is templated, allowing the user to specify the type of the real and imaginary components. The default type is double.

Template Parameters: T – The type of the real and imaginary components

Public Types

using Scalar = typename typetraits::TypeInfo<T>::Scalar

Public Functions

inline Complex()

Default constructor.

Create a new complex number. Both the real and imaginary components are set to zero

template<typename R> inline explicit Complex(const R &realVal)

Construct a complex number from a real number.

Create a complex number, setting only the real component. The imaginary component is initialized to zero

Template Parameters: R – The type of the real component
Parameters: realVal – The real component

template<typename R, typename I> inline Complex(const R &realVal, const I &imagVal)

Construct a complex number from real and imaginary components.

Create a new complex number where both the real and imaginary parts are set from the passed parameters

Template Parameters

R – The type of the real component
I – The type of the imaginary component

Parameters

realVal – The real component
imagVal – The imaginary component

inline Complex(const Complex<T> &other)

Complex number copy constructor.

Parameters: other – The complex number to copy

inline Complex(Complex<T> &&other) noexcept

Complex number move constructor.

Parameters: other – The complex number to move

template<typename Other> inline Complex(const Complex<Other> &other)

Construct a complex number from another complex number with a different type.

Template Parameters: Other – Type of the components of the other complex number
Parameters: other – The complex number to copy

inline explicit Complex(const std::complex<T> &other)

Construct a complex number from a std::complex.

Parameters: other – The std::complex value to copy

inline auto operator=(const Complex<T> &other) -> Complex<T>&

Complex number assignment operator.

Parameters: other – The value to assign
Returns: *this

inline void real(const T &val)

Assign to the real component.

Set the real component of this complex number to val

Parameters: val – The value to assign

inline void imag(const T &val)

Assign to the imaginary component.

Set the imaginary component of this complex number to val

Parameters: val – The value to assign

inline auto real() const -> const T&

Access the real component.

Returns a const reference to the real component of this complex number

Returns: Real component

inline auto imag() const -> const T&

Access the imaginary component.

Returns a const reference to the imaginary component of this complex number

Returns: Imaginary component

inline auto real() -> T&

Access the real component.

Returns a reference to the real component of this complex number. Since this is a reference type, it can be assigned to

Returns: Real component

inline auto imag() -> T&

Access the imaginary component.

Returns a reference to the imaginary component of this complex number. Since this is a reference type, it can be assigned to

Returns: imaginary component

inline auto operator=(const T &other) -> Complex&

Complex number assigment operator.

Set the real component of this complex number to other, and the imaginary component to 0

Parameters: other –
Returns: *this

template<typename Other> inline auto operator=(const Complex<Other> &other) -> Complex&

Complex number assigment operator.

Assign another complex number to this one, copying the real and imaginary components

Template Parameters: Other – The type of the other complex number
Parameters: other – Complex number to assign
Returns: *this

inline auto operator+=(const T &other) -> Complex&

Inplace addition.

Add a scalar value to the real component of this imaginary number

Parameters: other – Scalar value to add
Returns: *this

inline auto operator-=(const T &other) -> Complex&

Inplace subtraction.

Subtract a scalar value from the real component of this imaginary number

Parameters: other – Scalar value to subtract
Returns: *this

inline auto operator*=(const T &other) -> Complex&

Inplace multiplication.

Multiply both the real and imaginary components of this complex number by a scalar

Parameters: other – Scalar value to multiply by
Returns: *this

inline auto operator/=(const T &other) -> Complex&

Inplace division.

Divide both the real and imaginary components of this complex number by a scalar

Parameters: other – Scalar value to divide by
Returns: *this

inline auto operator+=(const Complex &other) -> Complex&

Inplace addition.

Add a complex number to this one

Parameters: other – Complex number to add
Returns: *this

inline auto operator-=(const Complex &other) -> Complex&

Inplace subtraction.

Subtract a complex number from this one

Parameters: other – Complex number to subtract
Returns: *this

inline auto operator*=(const Complex &other) -> Complex&

Inplace multiplication.

Multiply this complex number by another one

Parameters: other – Complex number to multiply by
Returns: *this

inline auto operator/=(const Complex &other) -> Complex&

Inplace division.

Divide this complex number by another one

Parameters: other – Complex number to divide by
Returns: *this

template<typename To> inline explicit operator To() const

Cast to scalar types.

Cast this complex number to a scalar type. This will extract only the real component.

Template Parameters: To – Type to cast to
Returns: Scalar

template<typename To> inline explicit operator Complex<To>() const

Cast to a complex number with a different scalar type.

Cast the real and imaginary components of this complex number to a different type and return the result as a new complex number

Template Parameters: To – Scalar type to cast to
Returns: Complex number

template<typename T_, typename Char, typename Ctx> inline void str(const fmt::formatter<T_, Char> &format, Ctx &ctx) const

Public Static Functions

static inline constexpr auto size() -> size_t

Protected Functions

template<typename Other> inline void _add(const Complex<Other> &other)

Add a complex number to this one.

Template Parameters: Other – Scalar type of the other complex number
Parameters: other – Other complex number

template<typename Other> inline void _sub(const Complex<Other> &other)

Subtract a complex number from this one.

Template Parameters: Other – Scalar type of the other complex number
Parameters: other – Other complex number

template<typename Other> inline void _mul(const Complex<Other> &other)

Multiply this complex number by another one.

Template Parameters: Other – Scalar type of the other complex number
Parameters: other – Other complex number

template<typename Other> inline void _div(const Complex<Other> &other)

Divide this complex number by another one.

Template Parameters: Other – Scalar type of the other complex number
Parameters: other – Other complex number

Private Members

T m_val[2]#

Private Static Attributes

static constexpr size_t RE = 0#

static constexpr size_t IM = 1#

namespace detail

namespace algorithm#

Functions

template<typename T> auto normMinusOne(const T x, const T y) noexcept -> T

Calculates \( x^2 + y^2 - 1 \) for \( |x| \geq |y| \) and \( 0.5 \leq |x| < 2^{12} \).

Template Parameters

T – Template type

Parameters

x – First value
y – Second value

Returns

x * x + y * y - 1

template<bool safe = true, typename T> auto logP1(const T x) -> T

Calculates \( \log(1 + x) \).

May be inaccurate for small inputs

Template Parameters

safe – If true, will check for NaNs and overflow
T – Template type

Parameters

x – Input value

Returns

\( \log(1 + x) \)

template<bool safe = true, typename T> auto logHypot(const T x, const T y) noexcept -> T

Calculates \( \log(\sqrt{x^2 + y^2}) \).

Template Parameters

safe – If true, will check for NaNs and overflow
T – Template type

Parameters

x – Horizontal component
y – Vertical component

Returns

\( \log(\sqrt{x^2 + y^2}) \)

template<typename T> auto expMul(T *pleft, T right, short exponent) -> short

Compute \(e^{\text{pleft}} \times \text{right} \times 2^{\text{exponent}}\).

Template Parameters

T – Template type

Parameters

pleft – Pointer to the value to be exponentiated
right – Multiplier for the exponentiated value
exponent – Exponent for the power of 2 multiplication

Returns

1 if the result is NaN or Inf, -1 otherwise

Variables

template<typename T> static T HypotLegHuge = HypotLegHugeHelper<T>::val

template<typename T> static T HypotLegTiny = HypotLegTinyHelper<T>::val

template<typename T> struct HypotLegHugeHelper

#include <complex.hpp>

Public Static Attributes

static T val =(std::is_integral_v<T>)? (::librapid::sqrt(typetraits::NumericInfo<T>::max()) / T(2)): (T(0.5) * ::librapid::sqrt(typetraits::NumericInfo<T>::max()))

template<> struct HypotLegHugeHelper<double>

#include <complex.hpp>

Public Static Attributes

static constexpr double val = 6.703903964971298e+153

template<> struct HypotLegHugeHelper<float>

#include <complex.hpp>

Public Static Attributes

static constexpr double val = 9.2233715e+18f

template<typename T> struct HypotLegTinyHelper

#include <complex.hpp>

Public Static Attributes

static T val = ::librapid::sqrt(T(2) * typetraits::NumericInfo<T>::min() / typetraits::NumericInfo<T>::epsilon())

template<> struct HypotLegTinyHelper<double>

#include <complex.hpp>

Public Static Attributes

static constexpr double val = 1.4156865331029228e-146

template<> struct HypotLegTinyHelper<float>

#include <complex.hpp>

Public Static Attributes

static constexpr double val = 4.440892e-16f

namespace multiprec#

Functions

template<typename T> constexpr auto addX2(const T &x, const T &y) noexcept -> Fmp<T>

Summarizes two 1x precision values combined into a 2x precision result.

This function is exact when:

The result doesn’t overflow
Either underflow is gradual, or no internal underflow occurs
Intermediate precision is either the same as T, or greater than twice the precision of T
Parameters and local variables do not retain extra intermediate precision
Rounding mode is rounding to nearest.

Violation of condition 3 or 5 could lead to relative error on the order of epsilon^2.

Violation of other conditions could lead to worse results

Template Parameters

T – Template type

Parameters

x – First value
y – Second value

Returns

Sum of x and y

template<typename T> constexpr auto addSmallX2(const T x, const T y) noexcept -> Fmp<T>

Combines two 1x precision values into a 2x precision result with the requirement of specific exponent relationship.

Requires: exponent(x) + countr_zero(significand(x)) >= exponent(y) or x == 0

The result is exact when:

The requirement above is satisfied
No internal overflow occurs
Either underflow is gradual, or no internal underflow occurs
Intermediate precision is either the same as T, or greater than twice the precision of T
Parameters and local variables do not retain extra intermediate precision
Rounding mode is rounding to nearest

Violation of condition 3 or 5 could lead to relative error on the order of epsilon^2.

Violation of other conditions could lead to worse results

Template Parameters

T – Template type

Parameters

x – First value
y – Second value

Returns

Sum of x and y

template<typename T> constexpr auto addSmallX2(const T &x, const Fmp<T> &y) noexcept -> Fmp<T>

Combines a 1x precision value with a 2x precision value.

Requires: exponent(x) + countr_zero(significand(x)) >= exponent(y.val0) or x == 0

Template Parameters

T – Template type

Parameters

x – First value
y – Second value

Returns

Sum of x and y

template<typename T> constexpr auto addX1(const Fmp<T> &x, const Fmp<T> &y) noexcept -> T

Combines two 2x precision values into a 1x precision result.

Template Parameters

T – Template type

Parameters

x – First value
y – Second value

Returns

Sum of x and y

constexpr auto highHalf(const double x) noexcept -> double

Rounds a 2x precision value to 26 significant bits.

Parameters: x – Value to round
Returns: Rounded value

constexpr double sqrError(const double x, const double prod0) noexcept: Fallback method for sqrError(const double, const double) when SIMD is not available.

template<typename T> auto sqrError(const T x, const T prod0) noexcept -> T

Type-agnostic version of sqrError(const double, const double)

Template Parameters

T – Template type

Parameters

x – Input value
prod0 – Faithfully rounded product of x^2

auto sqrX2(const double x) noexcept -> Fmp<double>

Calculates the square of a 1x precision value and returns a 2x precision result.

The result is exact when no internal overflow or underflow occurs.

Parameters: x – Input value
Returns: 2x precision square of x

template<typename T> auto sqrX2(const T x) noexcept -> Fmp<T>

Type-agnostic version of sqrX2(const double)

Template Parameters: T – Template type
Parameters: x – Input value
Returns: 2x precision square of x

template<typename Scalar> struct Fmp

#include <complex.hpp>

Public Members

Scalar val0

Scalar val1

namespace typetraits

template<typename T> struct NumericInfo<Complex<T>>

Public Types

using Scalar = typename TypeInfo<Complex<T>>::Scalar

Public Functions

inline LIMIT_IMPL(min)

inline LIMIT_IMPL(max)

inline LIMIT_IMPL(epsilon)

inline LIMIT_IMPL(roundError)

inline LIMIT_IMPL(denormMin)

inline LIMIT_IMPL(infinity)

inline LIMIT_IMPL(quietNaN)

inline LIMIT_IMPL(signalingNaN)

template<typename T> struct TypeInfo<Complex<T>>

Public Types

using Scalar = Complex<T>

using Backend = typename TypeInfo<T>::Backend

using ShapeType = std::false_type

using Packet = std::false_type

Public Static Attributes

static constexpr detail::LibRapidType type = detail::LibRapidType::Scalar

static constexpr int64_t packetWidth = 0

static constexpr char name[] = "Complex"

static constexpr bool supportsArithmetic = true

static constexpr bool supportsLogical = true

static constexpr bool supportsBinary = false

static constexpr bool allowVectorisation = false

static constexpr cudaDataType_t CudaType = cudaDataType_t::CUDA_C_64F

static constexpr bool canAlign = TypeInfo<T>::canAlign

static constexpr bool canMemcpy = TypeInfo<T>::canMemcpy

namespace cxxblas#

Functions

template<typename T> librapid::Complex<T> conjugate(const librapid::Complex<T> &val)

Complex Number Examples#

To do

Complex Number Implementation Details#

To do

Set#

Documentation

View the API and documentation for LibRapid Sets.

Examples

See some examples of LibRapid’s Set library in action

Implementation Details

Learn about how LibRapid’s Set library is implemented

Set Listing#

Functions

template<typename ElementType> std::ostream &operator<<(std::ostream &os, const librapid::Set<ElementType> &set)#

template<typename ElementType, typename Char> struct formatter<librapid::Set<ElementType>, Char>

#include <set.hpp>

Public Functions

template<typename ParseContext> inline FMT_CONSTEXPR auto parse(ParseContext &ctx) -> const Char*

template<typename FormatContext> inline FMT_CONSTEXPR auto format(const Type &val, FormatContext &ctx) const -> decltype(ctx.out())

Private Types

using Type = librapid::Set<ElementType>#

using Base = fmt::formatter<ElementType, Char>#

Private Members

Base m_base#

namespace librapid

template<typename ElementType_> class Set

#include <set.hpp>

An unordered set of distinct elements of the same type. Elements are stored in ascending order and duplicates are removed.

For example, consider creating a set from the following array:

myArr = { 4, 4, 3, 7, 5, 2, 5, 6, 7, 1, 8, 9 }
mySet = Set(myArr)
// mySet -> Set(1, 2, 3, 4, 5, 6, 7, 8, 9)

Template Parameters: ElementType_ – The type of the elements in the set

Public Types

using ElementType = ElementType_: The type of the elements in the set.

using VectorType = std::vector<ElementType>: The type of the underlying vector.

using VectorIterator = typename VectorType::iterator: The type of the iterator for the underlying vector.

using VectorConstIterator = typename VectorType::const_iterator: The type of the const iterator for the underlying vector.

Public Functions

Set() = default: Default constructor.

Set(const Set &other) = default: Copy constructor.

Set(Set &&other) = default: Move constructor.

template<typename ShapeType, typename StorageType> inline Set(const array::ArrayContainer<ShapeType, StorageType> &arr)

Construct a set from an array.

In the case of multidimensional arrays, the elements are flattened. This means a 2D array will still result in a 1D set. Create a Set<Set<...>> if needed.

Template Parameters

ShapeType – Shape type of the array
StorageType – Storage type of the array

Parameters

arr – The array to construct the set from

inline Set(const std::vector<ElementType> &data)

Construct a set from a vector.

Parameters: data – The vector to construct the set from

inline Set(const std::initializer_list<ElementType> &data)

Construct a set from an initializer list.

Parameters: data – The initializer list to construct the set from

Set &operator=(const Set &other) = default: Copy assignment operator.

Set &operator=(Set &&other) = default: Move assignment operator.

inline int64_t size() const

Returns: Return the cardinality of the set \( |S| \)

inline const ElementType &operator[](int64_t index) const

Access the n-th element of the set in ascending order (i.e. \( S_n \))

index must be in the range \( [0, |S|) \)

Parameters: index – The index of the element to access
Returns: Return the n-th element of the set

inline bool contains(const ElementType &val) const

Check if the set contains a value ( \( \text{val} \in S \))

Parameters: val – The value to check for
Returns: True if the value is present, false otherwise

inline Set &insert(const ElementType &val)

Insert a value into the set ( \( S \cup \{\text{val}\} \))

Parameters: val – The value to insert
Returns: Return a reference to the set

inline Set &insert(const std::vector<ElementType> &data)

Insert an std::vector of values into the set ( \( S \leftarrow S \cup \text{data} \))

Each element of the vector is inserted into the set.

Parameters: data –
Returns: Reference to the set

inline Set &insert(const std::initializer_list<ElementType> &data)

Insert an initializer list of values into the set ( \( S \leftarrow S \cup \text{data} \))

Parameters: data –
Returns: Reference to the set

inline Set &operator+=(const ElementType &val)

Insert an element into the set ( \( S \leftarrow S \cup \{\text{val}\} \))

See also

insert(const ElementType &val)

Parameters: val – The value to insert
Returns: Return a reference to the set

inline Set &operator+=(const std::vector<ElementType> &data)

Insert an std::vector of values into the set ( \( S \leftarrow S \cup \text{data} \))

Parameters: data –
Returns: Reference to the set

inline Set &operator+=(const std::initializer_list<ElementType> &data)

Insert an initializer list of values into the set ( \( S \leftarrow S \cup \text{data} \))

Parameters: data –
Returns: Reference to the set

inline Set operator+(const ElementType &val)

Insert an element into the set and return the result ( \( R = S \cup \{\text{val}\} \))

Parameters: val – The value to insert
Returns: A new set \( R = S \cup \{\text{val}\} \)

inline Set operator+(const std::vector<ElementType> &data)

Insert an std::vector of values into the set and return the result ( \( R = S \cup \text{data} \))

Parameters: data – The vector of values to insert
Returns: A new set \( R = S \cup \text{data} \)

inline Set operator+(const std::initializer_list<ElementType> &data)

Insert an initializer list of values into the set and return the result ( \( R = S \cup \text{data} \))

Parameters: data – The initializer list of values to insert
Returns: A new set \( R = S \cup \text{data} \)

inline Set &discard(const ElementType &val)

Discard val from the set if it exists ( \( S \setminus \{\text{val}\} \))

If val is not contained within the set, nothing happens.

Parameters: val – The value to discard
Returns: A reference to the set

inline Set &discard(const std::vector<ElementType> &data)

Discard an std::vector of values from the set ( \( S \setminus \text{data} \))

If an element in data is not contained within the set, nothing happens.

Parameters: data – The vector of values to Discard
Returns: A reference to the set

inline Set &discard(const std::initializer_list<ElementType> &data)

Discard an initializer list of values from the set ( \( S \setminus \text{data} \))

If an element in data is not contained within the set, nothing happens.

Parameters: data – The initializer list of values to Discard
Returns: A reference to the set

inline Set &remove(const ElementType &val)

Remove val from the set ( \( S \setminus \{\text{val}\} \))

If val is not contained within the set, an exception is thrown.

Parameters: val – The value to remove
Returns: A reference to the set

inline Set &remove(const std::vector<ElementType> &data)

Remove an std::vector of values from the set ( \( S \setminus \text{data} \))

If an element in data is not contained within the set, an exception is thrown.

Parameters: data – The vector of values to remove
Returns: A reference to the set

inline Set &remove(const std::initializer_list<ElementType> &data)

Remove an initializer list of values from the set ( \( S \setminus \text{data} \))

If an element in data is not contained within the set, an exception is thrown.

Parameters: data – The initializer list of values to remove
Returns: A reference to the set

inline Set &operator-=(const ElementType &val)

Discard val from the set if it exists.

See also

discard(const ElementType &val)

Parameters: val – The value to discard
Returns: A reference to the set

inline Set &operator-=(const std::vector<ElementType> &data)

Discard an std::vector of values from the set.

Parameters: data – The vector of values to discard
Returns: A reference to the set

inline Set &operator-=(const std::initializer_list<ElementType> &data)

Discard an initializer list of values from the set.

Parameters: data – The initializer list of values to discard
Returns: A reference to the set

inline Set operator-(const ElementType &val)

Discard val from the set if it exists and return the result.

See also

discard(const ElementType &val)

Parameters: val – The value to discard
Returns: A new set \( R = S \setminus \{\text{val}\} \)

inline Set operator-(const std::vector<ElementType> &data)

Discard an std::vector of values from the set and return the result.

Parameters: data – The vector of values to discard
Returns: A new set \( R = S \setminus \text{data} \)

inline Set operator-(const std::initializer_list<ElementType> &data)

Discard an initializer list of values from the set and return the result.

Parameters: data – The initializer list of values to discard
Returns: A new set \( R = S \setminus \text{data} \)

inline Set operator|(const Set &other) const

Return the union of two sets ( \( R = S_1 \cup S_2 \))

\( \{ x : x \in S_1 \lor x \in S_2 \} \)

Parameters: other – \( S_2 \)
Returns: A new set \( R = S_1 \cup S_2 \)

inline Set operator&(const Set &other) const

Return the intersection of two sets ( \( R = S_1 \cap S_2 \))

\( \{ x : x \in S_1 \land x \in S_2 \} \)

Parameters: other – \( S_2 \)
Returns: A new set \( R = S_1 \cap S_2 \)

inline Set operator^(const Set &other) const

Return the symmetric difference of two sets ( \( R = S_1 \oplus S_2 \))

\( \{ x : x \in S_1 \oplus x \in S_2 \} \)

Parameters: other – \( S_2 \)
Returns: A new set \( R = S_1 \oplus S_2 \)

inline Set operator-(const Set &other) const

Return the set difference of two sets ( \( R = S_1 \setminus S_2 \))

\( \{ x : x \in S_1 \land x \notin S_2 \} \)

Parameters: other – \( S_2 \)
Returns: A new set \( R = S_1 \setminus S_2 \)

auto operator<=>(const Set &other) const = default

inline auto begin() const

Returns: Iterator to the beginning of the set

inline auto end() const

Returns: Iterator to the end of the set

template<typename T, typename Char, typename Ctx> inline void str(const fmt::formatter<T, Char> &format, Ctx &ctx) const

Used for formatting the set with {fmt}.

Template Parameters

T – Formatter type
Char – Character type
Ctx – Context type

Parameters

format – formatter instance
ctx – format_context instance

Protected Functions

inline void reserve(size_t elements)

Reserve space in the underlying vector for elements elements.

Parameters: elements – The number of elements to reserve space for

inline void sort(): Sort the underlying vector.

inline void prune(): Remove duplicates from the underlying vector.

inline void pushBack(const ElementType &val)

Add a value to the end of the set if it is known to be the largest element.

Parameters: val –

inline void insert(VectorConstIterator insertLocation, VectorConstIterator begin, VectorConstIterator end)

Insert a vector of values into a location in the set if they are known to be valid in that position.

Parameters

insertLocation –
begin –
end –

Private Members

std::vector<ElementType> m_data#: The underlying vector.

Complex Number Examples#

To do

Complex Number Implementation Details#

To do

Bitset#

Documentation

View the API and documentation for LibRapid Sets.

Examples

See some examples of LibRapid’s Set library in action

Implementation Details

Learn about how LibRapid’s Bitset library is implemented

Bitset Listing#

Functions

LIBRAPID_SIMPLE_IO_NORANGE (uint64_t numBits COMMA bool stackAlloc, librapid::BitSet< numBits COMMA stackAlloc >) template< uint64_t numElements

bool stackAlloc std::ostream & operator<< (std::ostream &os, const librapid::BitSet< numElements, stackAlloc > &bitset)

template<uint64_t numBits, bool stackAlloc, typename Char> struct formatter<librapid::BitSet<numBits, stackAlloc>, Char>

#include <bitset.hpp>

Public Functions

template<typename ParseContext> inline FMT_CONSTEXPR auto parse(ParseContext &ctx) -> const Char*

template<typename FormatContext> inline FMT_CONSTEXPR auto format(const Type &val, FormatContext &ctx) const -> decltype(ctx.out())

Private Types

using Type = librapid::BitSet<numBits, stackAlloc>#

using Base = fmt::formatter<int, Char>#

Private Members

Base m_base#

namespace librapid

Functions

template<typename T> uint64_t popCount(const T &value)

template<uint64_t numBits, bool stackAlloc> uint64_t popCount(const BitSet<numBits, stackAlloc> &bitset)

template<uint64_t numBits_ = 64, bool stackAlloc_ = true> class BitSet

#include <bitset.hpp>

Public Types

template<uint64_t otherBits, bool otherStackAlloc> using BitSetMerger = BitSet

using ElementType = uint64_t

using StorageType = std::conditional_t<stackAlloc, std::array<ElementType, numElements>, ElementType*>

Public Functions

inline BitSet()

inline BitSet(const BitSet &other)

BitSet(BitSet &&other) = default

inline constexpr BitSet(uint64_t value)

inline constexpr BitSet(const std::string &str, char zero = '0', char one = '1')

BitSet &operator=(const BitSet &other) = default

BitSet &operator=(BitSet &&other) = default

inline ~BitSet()

inline BitSet &set(uint64_t index, bool value)

inline BitSet &set(uint64_t start, uint64_t end, bool value)

inline bool get(uint64_t index) const

inline bool any() const

inline bool all() const

inline bool none() const

inline uint64_t first() const

inline uint64_t last() const

inline uint64_t popCount() const

template<uint64_t otherBits = numBits, bool otherStackAlloc = stackAlloc> inline BitSet &operator|=(const BitSet<otherBits, otherStackAlloc> &other)

template<uint64_t otherBits = numBits, bool otherStackAlloc = stackAlloc> inline BitSet &operator&=(const BitSet<otherBits, otherStackAlloc> &other)

template<uint64_t otherBits = numBits, bool otherStackAlloc = stackAlloc> inline BitSet &operator^=(const BitSet<otherBits, otherStackAlloc> &other)

inline BitSet &operator<<=(int64_t shift)

inline BitSet &operator>>=(int64_t shift)

template<uint64_t otherBits = numBits, bool otherStackAlloc = stackAlloc> inline auto operator|(const BitSet<otherBits, otherStackAlloc> &other) const -> BitSetMerger<otherBits, otherStackAlloc>

template<uint64_t otherBits = numBits, bool otherStackAlloc = stackAlloc> inline auto operator&(const BitSet<otherBits, otherStackAlloc> &other) const -> BitSetMerger<otherBits, otherStackAlloc>

template<uint64_t otherBits = numBits, bool otherStackAlloc = stackAlloc> inline auto operator^(const BitSet<otherBits, otherStackAlloc> &other) const -> BitSetMerger<otherBits, otherStackAlloc>

inline BitSet operator<<(int64_t shift) const

inline BitSet operator>>(int64_t shift) const

inline BitSet operator~() const

inline bool operator==(const BitSet &other) const

inline const auto &data() const

inline auto &data()

template<typename Integer = ElementType> inline int toInt() const

template<typename T, typename Char, typename Ctx> inline void str(const fmt::formatter<T, Char> &format, Ctx &ctx) const

Public Static Attributes

static constexpr bool stackAlloc = stackAlloc_

static constexpr uint64_t bitsPerElement = sizeof(ElementType) * 8

static constexpr uint64_t numBits = numBits_

static constexpr uint64_t numElements = (numBits + bitsPerElement - 1) / bitsPerElement

Protected Functions

inline constexpr uint64_t highMask() const

inline void zero()

inline void init()

inline void emptyInit()

Private Members

StorageType m_data#

Bitset Examples#

To do

Bitset Implementation Details#

To do

Map and Unordered Map#

Both of these classes inherit from the std::map and std::unordered_map classes respectively, implementing a few additional features to make them easier to use and more versatile.

For the full documentation on the std::map and std::unordered_map classes, see the map (C++ reference) and unordered map (C++ reference) pages.

namespace librapid

template<typename Key, typename Value> class Map : public std::map<Key, Value>

#include <map.hpp>

Public Functions

inline bool contains(const Key &key) const

Check if a key exists in the map.

Parameters: key – Key to search for
Returns: Boolean

inline bool contains(const Key &key, Value &value) const

Check if a key exists in the map and, if it does, set value to the value of the key. The function returns true if the key exists, false otherwise. (If the function returns false, value will not be modified/initialized, so make sure you check the return value!)

Parameters

key – Key to search for
value – Value of the key, if it exists (output)

Returns

True if the key exists, false otherwise

inline auto get(const Key &key) const

Get the value of a key.

Parameters: key – Key to search for
Returns: Value of the key

inline auto get(const Key &key, const Value &defaultValue) const

Get the value of a key, or a default value if the key does not exist.

Parameters

key – Key to search for
defaultValue – Default value to return if the key does not exist

Returns

Value of the key, or defaultValue if the key does not exist

inline std::string str(const std::string &keyFormat = "{}", const std::string &valueFormat = "{}") const

template<typename Key, typename Value> class UnorderedMap : public std::unordered_map<Key, Value>

#include <map.hpp>

Public Functions

inline bool contains(const Key &key) const

Check if a key exists in the map.

Parameters: key – Key to search for
Returns: Boolean

inline bool contains(const Key &key, Value &value) const

Parameters

key – Key to search for
value – Value of the key, if it exists (output)

Returns

True if the key exists, false otherwise

inline auto get(const Key &key) const

Get the value of a key.

Parameters: key – Key to search for
Returns: Value of the key

inline auto get(const Key &key, const Value &defaultValue) const

Get the value of a key, or a default value if the key does not exist.

Parameters

key – Key to search for
defaultValue – Default value to return if the key does not exist

Returns

Value of the key, or defaultValue if the key does not exist

inline std::string str(const std::string &keyFormat = "{}", const std::string &valueFormat = "{}") const

Mathematics#

Multi-Precision Arithmetic#

LibRapid has support for MPIR and MPFR, which support arbitrary-precision integers, floating points and rationals.

We provide a simple wrapper around these libraries, enabling all mathematical operations to be performed on these data types – you don’t even need to use a different function name!

Multi-Precision Listing#

Warning

doxygenclass: Cannot find class “librapid::mpz” in doxygen xml output for project “librapid” from directory: ../xml

Warning

doxygenclass: Cannot find class “librapid::mpq” in doxygen xml output for project “librapid” from directory: ../xml

Warning

doxygenclass: Cannot find class “librapid::mpf” in doxygen xml output for project “librapid” from directory: ../xml

Warning

doxygenclass: Cannot find class “librapid::mpfr” in doxygen xml output for project “librapid” from directory: ../xml

Tutorials#

Performance and Benchmarks#

LibRapid is high-performance library and is fast by default, but there are still ways to make your code even faster.

Lazy Evaluation#

Operations performed on Arrays are evaluated only when needed, meaning functions can be chained together and evaluated in one go. In many cases, the compiler can optimise these chained calls into a single loop, resulting in much faster code.

Look at the example below:

lrc::Array<float> A, B, C, D:
A = lrc::fromData({{1, 2}, {3, 4}});
B = lrc::fromData({{5, 6}, {7, 8}});
C = lrc::fromData({{9, 10}, {11, 12}});
D = A + B * C;

Without lazy-evaluation, the operation A+B*C must be performed in multiple stages:

auto tmp1 = B * C;    // First operation and temporary object
auto tmp2 = A + tmp1; // Second operation and ANOTHER temporary object
D = tmp2;             // Unnecessary copy

This is clearly suboptimal.

With lazy-evaluation, however, the compiler can generate a loop similar to the pseudocode below:

FOR index IN A.size DO
    D[i] = A[i] + B[i] * C[i]
ENDFOR 

This has no unnecessary copies, no temporary variables, no additional memory allocation, etc. and is substantially quicker.

Making Use of LibRapid’s Lazy Evaluation#

To make use of LibRapid’s lazy evaluation, try to avoid creating temporary objects and always assign results directly to an existing array object, instead of creating a new one. This means no heap allocations are performed, which is a very costly operation.

Warning

Be very careful not to reference invalid memory. This is, unfortunately, an unavoidable side effect of returning lazy-objects. See Caution for more information.

Note that, sometimes, it is faster to evaluate intermediate results than to use the combined operation. To do this, you can call eval() on the result of any operation to generate an Array object directly from it.

Linear Algebra#

Linear algebra methods in LibRapid also return temporary objects, meaning they are not evaluated fully until they are needed. One implication of this is that expressions involving more than one operation will be evaluated very slowly.

Danger

Be careful when calling eval on the result of a linear algebra operation. Sometimes, LibRapid will be able to combine multiple operations into a single function call, which can lead to much better performance. Check the documentation for that specific function to see what further optimisations it supports.

Solution#

To get around this issue, it’ll often be quicker to simply evaluate (myExpression.eval()) the result of any linear algebra operations inside the larger expression.

auto slowExpression = a + b * c.dot(d);
auto fastExpression = a + b * c.dot(d).eval();

Explanation#

Since c.dot(d) is a lazy object, the lazy evaluator will calculate each element of the resulting array independently as and when it is required by the rest of the expression. This means it is not possible to make use of the extremely fast BLAS and LAPACK functions.

By forcing the result to be evaluated independently of the rest of the expression, LibRapid can call gemm, for example, making the program significantly faster.

LibRapid Benchmarks#

Run the Benchmarks Yourself#

You can run the benchmarks yourself by cloning the Benchmark repository and running the necessary CMake commands.

git clone --recursive https://github.com/LibRapid/BenchmarksCPP.git

cd BenchmarksCPP
mkdir build
cd build
cmake .. -DCMAKE_BUILD_TYPE=Release
cmake --build . --config Release

Warning

Make sure to run the benchmarks in Release mode. Otherwise, the compiler will not produce optimized code and the results will not be accurate.

Warning#

The benchmarks included in the documentation were run on free GitHub Actions runners. These machines have a limited number of virtual CPU cores, a small amount of RAM and are not designed for intensive workloads.

Running the benchmarks on my personal machine produces fairly different results from those shown in documentation.

As a result, the benchmarks do not necessarily represent the true performance characteristics of each library.

It is highly recommended that you run the benchmarks yourself for the best results.

Note

LibRapid’s developers are looking into getting more powerful servers to run the benchmarks on, but we do not currently have the funding or resources to do so.

Strange Results#

Occasionally, the benchmarks can produce some strange results where one library is disproportionately faster than the others. I’m not sure exactly why this happens, but my current theory is that it’s to do with memory alignment of the code and fluctuations in the server’s performance.

Having more powerful runners may help to reduce the impact of these fluctuations, but I’m not sure if it will completely eliminate them. If you have any ideas, please let me know!

Benchmark Results#

Ubuntu#

GCC#

OPTIMISE_SMALL_ARRAYS=OFF#

Matrix Transpose#

1 thread#

Warning

I strongly recommend running these benchmarks on your own machine to ensure that the results you see are as relevant as possible.

Benchmark Result | Matrix Transpose | 1 threads

2 threads#

Warning

I strongly recommend running these benchmarks on your own machine to ensure that the results you see are as relevant as possible.

Benchmark Result | Matrix Transpose | 2 threads

Combined Array Operations#

1 thread#

Warning

I strongly recommend running these benchmarks on your own machine to ensure that the results you see are as relevant as possible.

Benchmark Result | Combined Array Operations | 1 threads

2 threads#

Warning

I strongly recommend running these benchmarks on your own machine to ensure that the results you see are as relevant as possible.

Benchmark Result | Combined Array Operations | 2 threads

Array Addition#

1 thread#

Warning

I strongly recommend running these benchmarks on your own machine to ensure that the results you see are as relevant as possible.

Benchmark Result | Array Addition | 1 threads

2 threads#

Warning

I strongly recommend running these benchmarks on your own machine to ensure that the results you see are as relevant as possible.

Benchmark Result | Array Addition | 2 threads

OPTIMISE_SMALL_ARRAYS=ON#

Matrix Transpose#

(Optimised for Small Arrays)#

Warning

I strongly recommend running these benchmarks on your own machine to ensure that the results you see are as relevant as possible.

Benchmark Result | Matrix Transpose | 1 threads

Combined Array Operations#

(Optimised for Small Arrays)#

Warning

I strongly recommend running these benchmarks on your own machine to ensure that the results you see are as relevant as possible.

Benchmark Result | Combined Array Operations | 1 threads

Array Addition#

(Optimised for Small Arrays)#

Warning

I strongly recommend running these benchmarks on your own machine to ensure that the results you see are as relevant as possible.

Benchmark Result | Array Addition | 1 threads

Clang#

OPTIMISE_SMALL_ARRAYS=OFF#

Matrix Transpose#

1 thread#

Warning

I strongly recommend running these benchmarks on your own machine to ensure that the results you see are as relevant as possible.

Benchmark Result | Matrix Transpose | 1 threads

2 threads#

Warning

I strongly recommend running these benchmarks on your own machine to ensure that the results you see are as relevant as possible.

Benchmark Result | Matrix Transpose | 2 threads

3 threads#

Warning

I strongly recommend running these benchmarks on your own machine to ensure that the results you see are as relevant as possible.

Benchmark Result | Matrix Transpose | 3 threads

4 threads#

Warning

I strongly recommend running these benchmarks on your own machine to ensure that the results you see are as relevant as possible.

Benchmark Result | Matrix Transpose | 4 threads

Combined Array Operations#

1 thread#

Warning

I strongly recommend running these benchmarks on your own machine to ensure that the results you see are as relevant as possible.

Benchmark Result | Combined Array Operations | 1 threads

2 threads#

Warning

I strongly recommend running these benchmarks on your own machine to ensure that the results you see are as relevant as possible.

Benchmark Result | Combined Array Operations | 2 threads

3 threads#

Warning

I strongly recommend running these benchmarks on your own machine to ensure that the results you see are as relevant as possible.

Benchmark Result | Combined Array Operations | 3 threads

4 threads#

Warning

I strongly recommend running these benchmarks on your own machine to ensure that the results you see are as relevant as possible.

Benchmark Result | Combined Array Operations | 4 threads

Array Addition#

1 thread#

Warning

I strongly recommend running these benchmarks on your own machine to ensure that the results you see are as relevant as possible.

Benchmark Result | Array Addition | 1 threads

2 threads#

Warning

I strongly recommend running these benchmarks on your own machine to ensure that the results you see are as relevant as possible.

Benchmark Result | Array Addition | 2 threads

3 threads#

Warning

I strongly recommend running these benchmarks on your own machine to ensure that the results you see are as relevant as possible.

Benchmark Result | Array Addition | 3 threads

4 threads#

Warning

I strongly recommend running these benchmarks on your own machine to ensure that the results you see are as relevant as possible.

Benchmark Result | Array Addition | 4 threads

OPTIMISE_SMALL_ARRAYS=ON#

Matrix Transpose#

(Optimised for Small Arrays)#

Warning

I strongly recommend running these benchmarks on your own machine to ensure that the results you see are as relevant as possible.

Benchmark Result | Matrix Transpose | 1 threads

Combined Array Operations#

(Optimised for Small Arrays)#

Warning

I strongly recommend running these benchmarks on your own machine to ensure that the results you see are as relevant as possible.

Benchmark Result | Combined Array Operations | 1 threads

Array Addition#

(Optimised for Small Arrays)#

Warning

I strongly recommend running these benchmarks on your own machine to ensure that the results you see are as relevant as possible.

Benchmark Result | Array Addition | 1 threads

MacOS#

GCC#

OPTIMISE_SMALL_ARRAYS=OFF#

Matrix Transpose#

1 thread#

Warning

I strongly recommend running these benchmarks on your own machine to ensure that the results you see are as relevant as possible.

Benchmark Result | Matrix Transpose | 1 threads

2 threads#

Warning

I strongly recommend running these benchmarks on your own machine to ensure that the results you see are as relevant as possible.

Benchmark Result | Matrix Transpose | 2 threads

3 threads#

Warning

I strongly recommend running these benchmarks on your own machine to ensure that the results you see are as relevant as possible.

Benchmark Result | Matrix Transpose | 3 threads

4 threads#

Warning

I strongly recommend running these benchmarks on your own machine to ensure that the results you see are as relevant as possible.

Benchmark Result | Matrix Transpose | 4 threads

Combined Array Operations#

1 thread#

Warning

I strongly recommend running these benchmarks on your own machine to ensure that the results you see are as relevant as possible.

Benchmark Result | Combined Array Operations | 1 threads

2 threads#

Warning

I strongly recommend running these benchmarks on your own machine to ensure that the results you see are as relevant as possible.

Benchmark Result | Combined Array Operations | 2 threads

3 threads#

Warning

I strongly recommend running these benchmarks on your own machine to ensure that the results you see are as relevant as possible.

Benchmark Result | Combined Array Operations | 3 threads

4 threads#

Warning

I strongly recommend running these benchmarks on your own machine to ensure that the results you see are as relevant as possible.

Benchmark Result | Combined Array Operations | 4 threads

Array Addition#

1 thread#

Warning

I strongly recommend running these benchmarks on your own machine to ensure that the results you see are as relevant as possible.

Benchmark Result | Array Addition | 1 threads

2 threads#

Warning

I strongly recommend running these benchmarks on your own machine to ensure that the results you see are as relevant as possible.

Benchmark Result | Array Addition | 2 threads

3 threads#

Warning

I strongly recommend running these benchmarks on your own machine to ensure that the results you see are as relevant as possible.

Benchmark Result | Array Addition | 3 threads

4 threads#

Warning

I strongly recommend running these benchmarks on your own machine to ensure that the results you see are as relevant as possible.

Benchmark Result | Array Addition | 4 threads

OPTIMISE_SMALL_ARRAYS=ON#

Matrix Transpose#

(Optimised for Small Arrays)#

Warning

I strongly recommend running these benchmarks on your own machine to ensure that the results you see are as relevant as possible.

Benchmark Result | Matrix Transpose | 1 threads

Combined Array Operations#

(Optimised for Small Arrays)#

Warning

I strongly recommend running these benchmarks on your own machine to ensure that the results you see are as relevant as possible.

Benchmark Result | Combined Array Operations | 1 threads

Array Addition#

(Optimised for Small Arrays)#

Warning

I strongly recommend running these benchmarks on your own machine to ensure that the results you see are as relevant as possible.

Benchmark Result | Array Addition | 1 threads

Clang#

OPTIMISE_SMALL_ARRAYS=OFF#

Matrix Transpose#

1 thread#

Warning

I strongly recommend running these benchmarks on your own machine to ensure that the results you see are as relevant as possible.

Benchmark Result | Matrix Transpose | 1 threads

2 threads#

Warning

I strongly recommend running these benchmarks on your own machine to ensure that the results you see are as relevant as possible.

Benchmark Result | Matrix Transpose | 2 threads

3 threads#

Warning

I strongly recommend running these benchmarks on your own machine to ensure that the results you see are as relevant as possible.

Benchmark Result | Matrix Transpose | 3 threads

4 threads#

Warning

I strongly recommend running these benchmarks on your own machine to ensure that the results you see are as relevant as possible.

Benchmark Result | Matrix Transpose | 4 threads

Combined Array Operations#

1 thread#

Warning

I strongly recommend running these benchmarks on your own machine to ensure that the results you see are as relevant as possible.

Benchmark Result | Combined Array Operations | 1 threads

2 threads#

Warning

I strongly recommend running these benchmarks on your own machine to ensure that the results you see are as relevant as possible.

Benchmark Result | Combined Array Operations | 2 threads

3 threads#

Warning

I strongly recommend running these benchmarks on your own machine to ensure that the results you see are as relevant as possible.

Benchmark Result | Combined Array Operations | 3 threads

4 threads#

Warning

I strongly recommend running these benchmarks on your own machine to ensure that the results you see are as relevant as possible.

Benchmark Result | Combined Array Operations | 4 threads

Array Addition#

1 thread#

Warning

I strongly recommend running these benchmarks on your own machine to ensure that the results you see are as relevant as possible.

Benchmark Result | Array Addition | 1 threads

2 threads#

Warning

I strongly recommend running these benchmarks on your own machine to ensure that the results you see are as relevant as possible.

Benchmark Result | Array Addition | 2 threads

3 threads#

Warning

I strongly recommend running these benchmarks on your own machine to ensure that the results you see are as relevant as possible.

Benchmark Result | Array Addition | 3 threads

4 threads#

Warning

I strongly recommend running these benchmarks on your own machine to ensure that the results you see are as relevant as possible.

Benchmark Result | Array Addition | 4 threads

OPTIMISE_SMALL_ARRAYS=ON#

Matrix Transpose#

(Optimised for Small Arrays)#

Warning

I strongly recommend running these benchmarks on your own machine to ensure that the results you see are as relevant as possible.

Benchmark Result | Matrix Transpose | 1 threads

Combined Array Operations#

(Optimised for Small Arrays)#

Warning

I strongly recommend running these benchmarks on your own machine to ensure that the results you see are as relevant as possible.

Benchmark Result | Combined Array Operations | 1 threads

Array Addition#

(Optimised for Small Arrays)#

Warning

I strongly recommend running these benchmarks on your own machine to ensure that the results you see are as relevant as possible.

Benchmark Result | Array Addition | 1 threads

Windows#

Clang#

OPTIMISE_SMALL_ARRAYS=OFF#

Matrix Transpose#

1 thread#

Warning

I strongly recommend running these benchmarks on your own machine to ensure that the results you see are as relevant as possible.

Benchmark Result | Matrix Transpose | 1 threads

2 threads#

Warning

I strongly recommend running these benchmarks on your own machine to ensure that the results you see are as relevant as possible.

Benchmark Result | Matrix Transpose | 2 threads

Combined Array Operations#

1 thread#

Warning

I strongly recommend running these benchmarks on your own machine to ensure that the results you see are as relevant as possible.

Benchmark Result | Combined Array Operations | 1 threads

2 threads#

Warning

I strongly recommend running these benchmarks on your own machine to ensure that the results you see are as relevant as possible.

Benchmark Result | Combined Array Operations | 2 threads

Array Addition#

1 thread#

Warning

I strongly recommend running these benchmarks on your own machine to ensure that the results you see are as relevant as possible.

Benchmark Result | Array Addition | 1 threads

2 threads#

Warning

I strongly recommend running these benchmarks on your own machine to ensure that the results you see are as relevant as possible.

Benchmark Result | Array Addition | 2 threads

OPTIMISE_SMALL_ARRAYS=ON#

Matrix Transpose#

(Optimised for Small Arrays)#

Warning

I strongly recommend running these benchmarks on your own machine to ensure that the results you see are as relevant as possible.

Benchmark Result | Matrix Transpose | 1 threads

Combined Array Operations#

(Optimised for Small Arrays)#

Warning

I strongly recommend running these benchmarks on your own machine to ensure that the results you see are as relevant as possible.

Benchmark Result | Combined Array Operations | 1 threads

Array Addition#

(Optimised for Small Arrays)#

Warning

I strongly recommend running these benchmarks on your own machine to ensure that the results you see are as relevant as possible.

Benchmark Result | Array Addition | 1 threads

MSVC#

OPTIMISE_SMALL_ARRAYS=OFF#

Matrix Transpose#

1 thread#

Warning

I strongly recommend running these benchmarks on your own machine to ensure that the results you see are as relevant as possible.

Benchmark Result | Matrix Transpose | 1 threads

2 threads#

Warning

I strongly recommend running these benchmarks on your own machine to ensure that the results you see are as relevant as possible.

Benchmark Result | Matrix Transpose | 2 threads

3 threads#

Warning

I strongly recommend running these benchmarks on your own machine to ensure that the results you see are as relevant as possible.

Benchmark Result | Matrix Transpose | 3 threads

4 threads#

Warning

I strongly recommend running these benchmarks on your own machine to ensure that the results you see are as relevant as possible.

Benchmark Result | Matrix Transpose | 4 threads

Combined Array Operations#

1 thread#

Warning

I strongly recommend running these benchmarks on your own machine to ensure that the results you see are as relevant as possible.

Benchmark Result | Combined Array Operations | 1 threads

2 threads#

Warning

I strongly recommend running these benchmarks on your own machine to ensure that the results you see are as relevant as possible.

Benchmark Result | Combined Array Operations | 2 threads

3 threads#

Warning

I strongly recommend running these benchmarks on your own machine to ensure that the results you see are as relevant as possible.

Benchmark Result | Combined Array Operations | 3 threads

4 threads#

Warning

I strongly recommend running these benchmarks on your own machine to ensure that the results you see are as relevant as possible.

Benchmark Result | Combined Array Operations | 4 threads

Array Addition#

1 thread#

Warning

I strongly recommend running these benchmarks on your own machine to ensure that the results you see are as relevant as possible.

Benchmark Result | Array Addition | 1 threads

2 threads#

Warning

I strongly recommend running these benchmarks on your own machine to ensure that the results you see are as relevant as possible.

Benchmark Result | Array Addition | 2 threads

3 threads#

Warning

I strongly recommend running these benchmarks on your own machine to ensure that the results you see are as relevant as possible.

Benchmark Result | Array Addition | 3 threads

4 threads#

Warning

I strongly recommend running these benchmarks on your own machine to ensure that the results you see are as relevant as possible.

Benchmark Result | Array Addition | 4 threads

OPTIMISE_SMALL_ARRAYS=ON#

Matrix Transpose#

(Optimised for Small Arrays)#

Warning

I strongly recommend running these benchmarks on your own machine to ensure that the results you see are as relevant as possible.

Benchmark Result | Matrix Transpose | 1 threads

Combined Array Operations#

(Optimised for Small Arrays)#

Warning

I strongly recommend running these benchmarks on your own machine to ensure that the results you see are as relevant as possible.

Benchmark Result | Combined Array Operations | 1 threads

Array Addition#

(Optimised for Small Arrays)#

Warning

I strongly recommend running these benchmarks on your own machine to ensure that the results you see are as relevant as possible.

Benchmark Result | Array Addition | 1 threads

sphinx#

design#

OPTIMISE_SMALL_ARRAYS=ON#

Caution#

Warning

LibRapid developers had to make certain decisions regarding the underlying data layout used by the library. We made these decisions with the best interests of the library in mind, and while they may improve performance or usability, they may also incur adverse side effects.

While the developers of LibRapid may not be aware of all the side effects of their design choices, we have done our best to identify and justify those we know of.

Array Referencing Issues#

LibRapid uses lazy evaluation to reduce the number of intermediate variables and copies required for any given operation, significantly improving performance. A side effect of this is that combined operations store references to Array objects.

As a result, if any of the referenced Array instances go out of scope before the lazy object is evaluated, an invalid memory location will be accessed, incurring a segmentation fault.

The easiest fix for this is to make sure you evaluate temporary results in time, though this is easier said than done. LibRapid aims to identify when a lazy object is using an invalid value and notify the user, but this will not work in all cases.

The code below will cause a segmentation fault since testArray will go out of scope upon returning from the function while the returned object contains two references to the array.

/* References invalid memory
vvvv */
auto doesThisBreak() {
    lrc::Array<float> testArray(lrc::Shape({3, 3}));
    testArray << 1, 2, 3, 4, 5, 6, 7, 8, 9;
    return testArray + testArray;
}

/*   Changed
-------vvv------- */
lrc::Array<float> doesThisBreak() {
    lrc::Array<float> testArray(lrc::Shape({3, 3}));
    testArray << 1, 2, 3, 4, 5, 6, 7, 8, 9;
    return testArray + testArray;
}

Why use LibRapid?#

LibRapid aims to provide a cohesive ecosystem of functions that interoperate with each other, allowing for faster development and faster code execution.

For example, LibRapid implements a wide range of mathematical functions which can operate on primitive types, multi-precision types, vectors, and arrays. Due to the way these functions are implemented, a single function call can be used to operate on all of these types, reducing code duplication.

A Small Example#

To prove the point made above, let’s take a look at a simple example. Here, we have a function that maps a value from one range to another:

// Standard "double" implementation
double map(double val, double start1, double stop1, double start2, double stop2) {
    return start2 + (stop2 - start2) * ((val - start1) / (stop1 - start1));
}

// map(0.5, 0, 1, 0, 10) = 5
// map(10, 0, 100, 0, 1) = 0.1
// map(5, 0, 10, 0, 100) = 50

This function will accept integers, floats and doubles, but nothing else can be used, limiting its functionality.

Of course, this could be templated to accept other types, but if you passed a std::vector<double> to this function, for example, you’d have to create an edge case to support it. This is where LibRapid comes in.

Look at the function below:

// An extremely versatile mapping function (used within LibRapid!)
template<typename V, typename B1, typename E1, typename B2, typename E2>
V map(V val, B1 start1, E1 stop1, B2 start2, E2 stop2) {
    return start2 + (stop2 - start2) * ((val - start1) / (stop1 - start1));
}

This may look excessively complicated with that many template parameters, but you don’t actually need all of those! This just gives the greatest flexibility. This function can be called with almost any LibRapid type!.

map(0.5, 0, 1, 0, 100); //  . . . . . . . . . . . . . . . | 50
map(lrc::Vec2d(0.2, 0.8), 0, 1, 0, 100); // . . . . . . . | (20, 80)
map(0.5, 0, 1, 0, lrc::Vec2d(100, 200)); // . . . . . . . | (50, 100)
map(lrc::Vec2d(-1, -2), 1, 0, lrc::Vec2d(100, 300)); // . | (75, 250)

// ---------------------------------------------------------------------

using namespace lrc::literals; // To use "_f" suffix
                               // (also requires multiprecision to be enabled)
// "0.5"_f in this case creates a multiprecision float :)
map("0.5"_f, "0"_f, "1"_f, "0"_f, "100"_f); //  . . . . . | 50.00000000000000

// ---------------------------------------------------------------------

auto val    = lrc::fromData<float>({{1, 2}, {3, 4}});
auto start1 = lrc::fromData<float>({{0, 0}, {0, 0}});
auto end1   = lrc::fromData<float>({{10, 10}, {10, 10}});
auto start2 = lrc::fromData<float>({{0, 0}, {0, 0}});
auto end2   = lrc::fromData<float>({{100, 100}, {100, 100}});

fmt::print("{}\n", lrc::map(val, start1, end1, start2, end2));
// [[10 20]
//  [30 40]]

Note: LibRapid’s built-in map function has even more functionality! See the Map Function details.

This is just one example of how LibRapid’s functions can be used to make your code more concise and more efficient, and hopefully it’s clear to see how powerful this could be when working with more complex functions and types.

Current Development Stage#

At the current point in time, LibRapid C++ is under rapid development by me (Pencilcaseman).

I am currently doing my A-Levels and do not have time to work on the library as much as I would like, so if you or someone you know might be willing to support the development of the library, feel free to create a pull request or chat to us on Discord. Any help is greatly appreciated!

Roadmap#

The Roadmap is a rough outline of what I want to get implemented in the library and by what point, but please don’t count on features being implemented quickly – I can’t promise I’ll have the time to implement everything as soon as I’d like… (I’ll try my best though!)

If you have any feature requests or suggestions, feel free to create an issue describing it. I’ll try to get it working as soon as possible. If you really need something implemented quickly, a small donation would be appreciated, and would allow me to bump it to the top of my list of features.

Licencing#

LibRapid is produced under the MIT License, so you are free to use the library how you like for personal and commercial purposes, though this is subject to some conditions, which can be found in full here: LibRapid License

LibRapid#

What is LibRapid?#

Getting Started#

Installation#

Your First Program#

Your First Program: Explained#

Troubleshooting#

Linux with CUDA#

CMake Integration#

Installation#

CMake Options#

LIBRAPID_BUILD_EXAMPLES#

LIBRAPID_BUILD_TESTS#

LIBRAPID_CODE_COV#

LIBRAPID_STRICT#

LIBRAPID_QUIET#

LIBRAPID_USE_PRECOMPILED_HEADER#

LIBRAPID_GET_FFTW#

LIBRAPID_GET_BLAS#

LIBRAPID_USE_OMP#

LIBRAPID_USE_OPENCL#

LIBRAPID_USE_CUDA#

LIBRAPID_USE_MULTIPREC#

LIBRAPID_OPTIMISE_SMALL_ARRAYS#

LIBRAPID_FAST_MATH#

LIBRAPID_NATIVE_ARCH#

LIBRAPID_CUDA_FLOAT_VECTOR_WIDTH and LIBRAPID_CUDA_DOUBLE_VECTOR_WIDTH#

LIBRAPID_NO_WINDOWS_H#

LIBRAPID_MKL_CONFIG_PATH#

API Reference#

Topics and Usage Examples#

Array Iterators#

Implicit Iteration#

Subscript Iteration#

Direct Iteration#

Direct Storage Access#

Benchmarks#

Arrays, Matrices and Linear Algebra#

Linear Algebra#

Level 1 (Vector-Vector)#

Level 2 (Matrix-Vector)#

Level 3 (Matrix-Matrix)#

Array Class Listing#

Array From Data#

Pseudoconstructors#

Array View#

Array Operations#

Size Type#

Stride Tools#

Storage#

OpenCL Storage#

CUDA Storage#

Vectors#

Vector Listing#

Complex Numbers#

Complex Number Listing#

Complex Number Examples#

Complex Number Implementation Details#

Set#

Set Listing#

Complex Number Examples#

Complex Number Implementation Details#

Bitset#

Bitset Listing#

Bitset Examples#

Bitset Implementation Details#

Map and Unordered Map#

Mathematics#

Multi-Precision Arithmetic#

Multi-Precision Listing#

Tutorials#

Performance and Benchmarks#

Lazy Evaluation#

Making Use of LibRapid’s Lazy Evaluation#

Linear Algebra#

Solution#

Explanation#

LibRapid Benchmarks#

Run the Benchmarks Yourself#

Warning#

`LIBRAPID_BUILD_EXAMPLES`#

`LIBRAPID_BUILD_TESTS`#

`LIBRAPID_CODE_COV`#

`LIBRAPID_STRICT`#

`LIBRAPID_QUIET`#

`LIBRAPID_USE_PRECOMPILED_HEADER`#

`LIBRAPID_GET_FFTW`#

`LIBRAPID_GET_BLAS`#

`LIBRAPID_USE_OMP`#

`LIBRAPID_USE_OPENCL`#

`LIBRAPID_USE_CUDA`#

`LIBRAPID_USE_MULTIPREC`#

`LIBRAPID_OPTIMISE_SMALL_ARRAYS`#

`LIBRAPID_FAST_MATH`#

`LIBRAPID_NATIVE_ARCH`#

`LIBRAPID_CUDA_FLOAT_VECTOR_WIDTH` and `LIBRAPID_CUDA_DOUBLE_VECTOR_WIDTH`#

`LIBRAPID_NO_WINDOWS_H`#

`LIBRAPID_MKL_CONFIG_PATH`#