Vc alternatives and similar libraries
Based on the "Math" category
-
Eigen
A high-level C++ library of template headers for linear algebra, matrix and vector operations, numerical solvers and related algorithms. [MPL2] -
ceres-solver
C++ library for modeling and solving large complicated nonlinear least squares problems from google. [BSD] -
TinyExpr
tiny recursive descent expression parser, compiler, and evaluation engine for math expressions -
LibTomMath
A free open source portable number theoretic multiple-precision integer library written entirely in C. [PublicDomain & WTFPL] website -
GMTL
Graphics Math Template Library is a collection of tools implementing Graphics primitives in generalized ways. [GPL2] -
ExprTK
The C++ Mathematical Expression Toolkit Library (ExprTk) is a simple to use, easy to integrate and extremely efficient run-time mathematical expression parser and evaluation engine. [CPL] -
muparser
muParser is an extensible high performance math expression parser library written in C++. [MIT] -
Boost.Multiprecision
provides higher-range/precision integer, rational and floating-point types in C++, header-only or with GMP/MPFR/LibTomMath backends. [Boost] -
Wykobi
A C++ library of efficient, robust and simple to use C++ 2D/3D oriented computational geometry routines. [MIT] -
Versor
A (fast) Generic C++ library for Geometric Algebras, including Euclidean, Projective, Conformal, Spacetime (etc). -
metamath
metamath is a tiny header-only library. It can be used for symbolic computations on single-variable functions, such as dynamic computations of derivatives. -
Xerus
A general purpose library for numerical calculations with higher order tensors, Tensor-Train Decompositions / Matrix Product States and other Tensor Networks -
Armadillo
A high quality C++ linear algebra library, aiming towards a good balance between speed and ease of use. The syntax (API) is deliberately similar to Matlab. [MPL2]
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README
Vc: portable, zero-overhead C++ types for explicitly data-parallel programming
NOTE: This is the development version, implementing https://wg21.link/p0214. For production use consider the latest release or the 1.3 branch. This implementation requires GCC 7.1 or newer. Support for Clang, ICC, and MSVC is available with the 1.3 branch.
Introduction
Recent generations of CPUs, and GPUs in particular, require data-parallel codes for full efficiency. Data parallelism requires that the same sequence of operations is applied to different input data. CPUs and GPUs can thus reduce the necessary hardware for instruction decoding and scheduling in favor of more arithmetic and logic units, which execute the same instructions synchronously. On CPU architectures this is implemented via SIMD registers and instructions. A single SIMD register can store N values and a single SIMD instruction can execute N operations on those values. On GPU architectures N threads run in perfect sync, fed by a single instruction decoder/scheduler. Each thread has local memory and a given index to calculate the offsets in memory for loads and stores.
Current C++ compilers can do automatic transformation of scalar codes to SIMD instructions (auto-vectorization). However, the compiler must reconstruct an intrinsic property of the algorithm that was lost when the developer wrote a purely scalar implementation in C++. Consequently, C++ compilers cannot vectorize any given code to its most efficient data-parallel variant. Especially larger data-parallel loops, spanning over multiple functions or even translation units, will often not be transformed into efficient SIMD code.
The Vc library provides the missing link. Its types enable explicitly stating data-parallel operations on multiple values. The parallelism is therefore added via the type system. Competing approaches state the parallelism via new control structures and consequently new semantics inside the body of these control structures.
Vc is a free software library to ease explicit vectorization of C++ code. It has an intuitive API and provides portability between different compilers and compiler versions as well as portability between different vector instruction sets. Thus an application written with Vc can be compiled for:
- AVX and AVX2
- SSE2 up to SSE4.2 or SSE4a
- Scalar
- MIC (only before Vc 2.0)
- AVX-512 (since Vc 2.0)
- NEON (in development)
- NVIDIA GPUs / CUDA (research)
Examples
Scalar Product
Let's start from the code for calculating a 3D scalar product using builtin floats:
using Vec3D = std::array<float, 3>;
float scalar_product(Vec3D a, Vec3D b) {
return a[0] * b[0] + a[1] * b[1] + a[2] * b[2];
}
Using Vc, we can easily vectorize the code using the native_simd<float> type:
using Vc::native_simd;
using Vec3D = std::array<native_simd<float>, 3>;
float_v scalar_product(Vec3D a, Vec3D b) {
return a[0] * b[0] + a[1] * b[1] + a[2] * b[2];
}
The above will scale to 1, 4, 8, 16, etc. scalar products calculated in parallel, depending on the target hardware's capabilities.
For comparison, the same vectorization using Intel SSE intrinsics is more verbose and uses prefix notation (i.e. function calls):
using Vec3D = std::array<__m128, 3>;
__m128 scalar_product(Vec3D a, Vec3D b) {
return _mm_add_ps(_mm_add_ps(_mm_mul_ps(a[0], b[0]), _mm_mul_ps(a[1], b[1])),
_mm_mul_ps(a[2], b[2]));
}
The above will neither scale to AVX, MIC, etc. nor is it portable to other SIMD ISAs.
Build Requirements
cmake >= 3.0
C++17 Compiler:
- GCC >= 7.1
Building and Installing Vc
- Create a build directory:
$ mkdir build
$ cd build
- Call
cmake; the following options may be interesting:-DCMAKE_INSTALL_PREFIX=<path>: Select a different install prefix. Note that installing is not required (use-I<path to Vc src>) and currently not supported.-DENABLE_UBSAN=ON: Build tests with the “undefined behavior sanitizer” enabled.-DTARGET_ARCHITECTURE=<target>: Select a target architecture, different from the one you are building on.-DUSE_CCACHE=ON: Useccache(when found) to speed up recurring builds.
$ cmake <srcdir>
- Build and run tests:
$ make -j8
$ ctest -j8
Documentation
The documentation of the master branch is currently out of date. Please refer to https://wg21.link/p0214 for the specification.
Documentation for older releases is available at:
Publications
- M. Kretz, "Extending C++ for Explicit Data-Parallel Programming via SIMD Vector Types", Goethe University Frankfurt, Dissertation, 2015.
- M. Kretz and V. Lindenstruth, "Vc: A C++ library for explicit vectorization", Software: Practice and Experience, 2011.
- M. Kretz, "Efficient Use of Multi- and Many-Core Systems with Vectorization and Multithreading", University of Heidelberg, 2009.
Work on integrating the functionality of Vc in the C++ standard library.
Communication
A channel on the freenode IRC network is reserved for discussions on Vc: [##vc on freenode](irc://chat.freenode.net:6667/##vc) ([via SSL](ircs://chat.freenode.net:6697/##vc))
Feel free to use the GitHub issue tracker for questions. Alternatively, there's a mailinglist for users of Vc
License
Vc is released under the terms of the 3-clause BSD license.
*Note that all licence references and agreements mentioned in the Vc README section above
are relevant to that project's source code only.