Vc alternatives and similar libraries
Based on the "Math" category

ceressolver
C++ library for modeling and solving large complicated nonlinear least squares problems from google. [BSD] 
Eigen
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LibTomMath
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GMTL
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ExprTK
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muparser
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Boost.Multiprecision
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Wykobi
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Versor
A (fast) Generic C++ library for Geometric Algebras, including Euclidean, Projective, Conformal, Spacetime (etc). 
metamath
metamath is a tiny headeronly library. It can be used for symbolic computations on singlevariable functions, such as dynamic computations of derivatives. 
Xerus
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Mission : Impossible (AutoDiff)
A concise C++17 implementation of automatic differentiation (operator overloading) 
Armadillo
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README
Vc: portable, zerooverhead C++ types for explicitly dataparallel 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 dataparallel 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 (autovectorization). 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 dataparallel variant. Especially larger dataparallel 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 dataparallel 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)
 AVX512 (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 (useI<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 DataParallel 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 ManyCore 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 3clause 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.