# SIMD Everywhere [![All Contributors](https://img.shields.io/badge/all_contributors-58-orange.svg?style=flat-square)](#contributors-) [![Chat](https://badges.gitter.im/gitterHQ/gitter.png)](https://matrix.to/#/#simd-everywhere_community:gitter.im) [![codecov](https://codecov.io/gh/simd-everywhere/simde/branch/master/graph/badge.svg?token=jcMBoRk0ui)](https://codecov.io/gh/simd-everywhere/simde) The SIMDe header-only library provides fast, portable implementations of [SIMD intrinsics](https://en.wikipedia.org/wiki/SIMD) on hardware which doesn't natively support them, such as calling [SSE](https://en.wikipedia.org/wiki/Streaming_SIMD_Extensions) functions on ARM. There is no performance penalty if the hardware supports the native implementation (*e.g.*, SSE/[AVX](https://en.wikipedia.org/wiki/Advanced_Vector_Extensions) runs at full speed on [x86](https://en.wikipedia.org/wiki/X86), [NEON](https://en.wikipedia.org/wiki/ARM_architecture#Advanced_SIMD_(Neon)) on [ARM](https://en.wikipedia.org/wiki/ARM_architecture), *etc.*). This makes porting code to other architectures much easier in a few key ways: First, instead of forcing you to rewrite everything for each architecture, SIMDe lets you get a port up and running almost effortlessly. You can then start working on switching the most performance-critical sections to native intrinsics, improving performance gradually. SIMDe lets (for example) SSE/AVX and NEON code exist side-by-side, in the same implementation. Second, SIMDe makes it easier to write code targeting [ISA](https://en.wikipedia.org/wiki/Instruction_set_architecture) extensions you don't have convenient access to. You can run NEON code on your x86 machine *without an emulator*. Obviously you'll eventually want to test on the actual hardware you're targeting, but for most development, SIMDe can provide a much easier path. SIMDe takes a very different approach from most other SIMD abstraction layers in that it aims to expose the entire functionality of the underlying instruction set. Instead of limiting functionality to the lowest common denominator, SIMDe tries to minimize the amount of effort required to port while still allowing you the space to optimize as needed. The current focus is on writing complete portable implementations, though a large number of functions already have accelerated implementations using one (or more) of the following: * SIMD intrinsics from other ISA extensions (e.g., using NEON to implement SSE). * Compiler-specific vector extensions and built-ins such as [`__builtin_shufflevector`](http://clang.llvm.org/docs/LanguageExtensions.html#langext-builtin-shufflevector) and [`__builtin_convertvector`](http://clang.llvm.org/docs/LanguageExtensions.html#langext-builtin-convertvector) * Compiler auto-vectorization hints, using: * [OpenMP 4 SIMD](http://www.openmp.org/) * [Cilk Plus](https://www.cilkplus.org/) * [GCC loop-specific pragmas](https://gcc.gnu.org/onlinedocs/gcc/Loop-Specific-Pragmas.html) * [clang pragma loop hint directives](http://llvm.org/docs/Vectorizers.html#pragma-loop-hint-directives) You can [try SIMDe online](https://simde.netlify.app/godbolt/demo) using Compiler Explorer and an amalgamated SIMDe header. If you have any questions, please feel free to use the [issue tracker](https://github.com/simd-everywhere/simde/issues) or the [mailing list](https://groups.google.com/forum/#!forum/simde). ## Current Status There are currently complete implementations of the following instruction set extensions: * x86 / x86_64 * [MMX](https://en.wikipedia.org/wiki/MMX_(instruction_set)) * [SSE](https://en.wikipedia.org/wiki/Streaming_SIMD_Extensions) * [SSE2](https://en.wikipedia.org/wiki/SSE2) * [SSE3](https://en.wikipedia.org/wiki/SSE3) * [SSSE3](https://en.wikipedia.org/wiki/SSSE3) * [SSE4.1](https://en.wikipedia.org/wiki/SSE4#SSE4.1) * [AVX](https://en.wikipedia.org/wiki/Advanced_Vector_Extensions) * [AVX2](https://en.wikipedia.org/wiki/Advanced_Vector_Extensions#Advanced_Vector_Extensions_2) * [FMA](https://en.wikipedia.org/wiki/FMA_instruction_set) * [GFNI](https://en.wikipedia.org/wiki/AVX-512#GFNI) * [CLMUL](https://en.wikipedia.org/wiki/CLMUL_instruction_set) * [XOP](https://en.wikipedia.org/wiki/XOP_instruction_set) * [SVML](https://software.intel.com/content/www/us/en/develop/documentation/cpp-compiler-developer-guide-and-reference/top/compiler-reference/intrinsics/intrinsics-for-intel-advanced-vector-extensions-512-intel-avx-512-instructions/intrinsics-for-arithmetic-operations-1/intrinsics-for-short-vector-math-library-svml-operations.html) * WebAssembly * [SIMD128](https://github.com/WebAssembly/simd) As well as partial support for many others, including NEON and SVE in addition to several AVX-512 extensions. See the [instruction-set-support](https://github.com/simd-everywhere/simde/issues?q=is%3Aissue+is%3Aopen+label%3Ainstruction-set-support+sort%3Aupdated-desc) label in the issue tracker for details on progress. If you'd like to be notified when an instruction set is available you may subscribe to the relevant issue. If you have a project you're interested in using with SIMDe but we don't yet support all the functions you need, please file an issue with a list of what's missing so we know what to prioritize. The default branch is protected so commits never reach it unless they have passed extensive CI checks. Status badges don't really make sense since they will always be green, but here are the links: * [GitHub Actions](https://github.com/simd-everywhere/simde/actions) * [Cirrus CI](https://cirrus-ci.com/github/simd-everywhere/simde) * [Semaphore CI](https://nemequ.semaphoreci.com/projects/simde) * [Circle CI](https://app.circleci.com/pipelines/github/simd-everywhere/simde) * [AppVeyor](https://ci.appveyor.com/project/nemequ/simde) * [Azure Pipelines](https://dev.azure.com/simd-everywhere/SIMDe/_build) * [Drone CI](https://cloud.drone.io/simd-everywhere/simde/) If you're adding a new build I suggest Cirrus CI, which is where we currently have the most room given the number of builds currently on the platform and the quotas for free/open-source usage. Alternately, feel free to set up another provider (such as [Codefresh](https://codefresh.io/), [Shippable](https://www.shippable.com/), [Bitrise](https://www.bitrise.io/), [Werkaer](https://app.wercker.com/), etc.). *Notice*: we plan on changing the name of the default branch from "master" to something else soon; we are just trying to wait to see what name git settles on so we can be consistent. ## Contributing First off, if you're reading this: thank you! Even considering contributing to SIMDe is very much appreciated! SIMDe is a fairly large undertaking; there are a *lot* of functions to get through and a lot of opportunities for optimization on different platforms, so we're very happy for any help you can provide. Programmers of all skill levels are welcome, there are lots of tasks which are pretty straightforward and don't require any special expertise. If you're not sure how you'd like to contribute, please consider taking a look at [the issue tracker](https://github.com/simd-everywhere/simde/issues). There is a [good first issue](https://github.com/simd-everywhere/simde/issues?q=is%3Aissue+is%3Aopen+label%3A%22good+first+issue%22) tag if you want to ease into a your first contributions, but if you're interested in something else please get in touch via the issue tracker; we're happy to help you get a handle on whatever you are interested in. If you're interested in implementing currently unimplemented functions, there is [a guide](https://github.com/simd-everywhere/simde/wiki/Implementing-a-New-Function) explaining how to add new functions and how to quickly and easily get a test case in place. It's a bit rough right now, but if anything is unclear please feel free to use the issue tracker to ask about anything you're not clear on. ## Usage First, it is important to note that *you do not need two separate versions* (one using SIMDe, the other native). If the native functions are available SIMDe will use them, and compilers easily optimize away any overhead from SIMDe; all they have to do is some basic inlining. `-O2` should be enough, but we strongly recommend `-O3` (or whatever flag instructs your compiler to aggressizely optimize) since many of the portable fallbacks are substantially faster with aggressive auto-vectorization that isn't enabled at lower optimization levels. Each instruction set has a separate file; `x86/mmx.h` for MMX, `x86/sse.h` for SSE, `x86/sse2.h` for SSE2, and so on. Just include the header for whichever instruction set(s) you want *instead of the native version* (if you include the native version after SIMDe it will result in compile-time errors if native aliases are enabled). SIMDe will provide the fastest implementation it can given which extensions you've enabled in your compiler (i.e., if you want to use NEON to implement SSE, you may need to pass something like `-mfpu=neon` or `-march=armv8-a+simd`. See [GCC ARM-Options](https://gcc.gnu.org/onlinedocs/gcc/ARM-Options.html) for more information). If you define `SIMDE_ENABLE_NATIVE_ALIASES` before including SIMDe you can use the same names as the native functions. Unfortunately, this is somewhat error-prone due to portability issues in the APIs, so it's recommended to only do this for testing. When `SIMDE_ENABLE_NATIVE_ALIASES` is undefined only the versions prefixed with `simde_` will be available; for example, the MMX `_mm_add_pi8` intrinsic becomes `simde_mm_add_pi8`, and `__m64` becomes `simde__m64`. Since SIMDe is meant to be portable, many functions which assume types are of a specific size have been altered to use fixed-width types instead. For example, Intel's APIs use `char` for signed 8-bit integers, but `char` on ARM is generally unsigned. SIMDe uses `int8_t` to make the API portable, but that means your code may require some minor changes (such as using `int8_t` instead of `char`) to work on other platforms. That said, the changes are usually quite minor. It's often enough to just use search and replace, manual changes are required pretty infrequently. ### OpenMP 4 SIMD SIMDe makes extensive use of annotations to help the compiler vectorize code. By far the best annotations use the SIMD support built in to OpenMP 4, so if your compiler supports these annotations we strongly recommend you enable them. If you are already using OpenMP, SIMDe will automatically detect it using the `_OPENMP` macro and no further action is required. Some compilers allow you to enable OpenMP SIMD *without* enabling the full OpenMP. In such cases there is no runtime dependency on OpenMP and no runtime overhead; SIMDe will just be faster. Unfortunately, SIMDe has no way to detect such situations (the `_OPENMP` macro is not defined), so after enabling it in your compiler you'll need to define `SIMDE_ENABLE_OPENMP` (e.g., by passing `-DSIMDE_ENABLE_OPENMP`) to get SIMDe to output the relevant pragmas. Enabling OpenMP SIMD support varies by compiler: * GCC 4.9+ and clang 6+ support a `-fopenmp-simd` command line flag. * ICC supports a `-qopenmp-simd` command line flag. * MCST's LCC enables OpenMP SIMD by default, so no flags are needed (technically you don't even need to pass `-DSIMDE_ENABLE_OPENMP`). We are not currently aware of any other compilers which allow you to enable OpenMP SIMD support without enabling full OpenMP (if you are please file an issue to let us know). You should determine whether you wish to enable full OpenMP support on a case-by-case basis, but it is likely that the overhead of linking to (but not using) the OpenMP runtime library will be dwarfed by the performance improvements from using the OpenMP SIMD annotations in SIMDe. If you choose not to use OpenMP SIMD, SIMDe also supports using [Cilk Plus](https://www.cilkplus.org/), [GCC loop-specific pragmas](https://gcc.gnu.org/onlinedocs/gcc/Loop-Specific-Pragmas.html), or [clang pragma loop hint directives](http://llvm.org/docs/Vectorizers.html#pragma-loop-hint-directives), though these are not nearly as effective as OpenMP SIMD and depending on them will likely result in less efficient code. All of these are detected automatically by SIMDe, so if they are enabled in your compiler nothing more is required. If for some reason you do not wish to enable OpenMP 4 SIMD support even though SIMDe detects it, you should define `SIMDE_DISABLE_OPENMP` prior to including SIMDe. ## Portability ### Compilers SIMDe does depend on some C99 features, though the subset supported by MSVC also works. While we do our best to make sure we provide optimized implementations where they are supported, SIMDe does contain portable fallbacks which are designed to work on any C99 compiler. Every commit is tested in CI on multiple compilers, platforms, and configurations, and our test coverage is extremely extensive. Currently tested compilers include: * GCC versions back to 4.8 * Clang versions back to 3.8 * Microsoft Visual Studio back to 12 (2013) * IBM XL C/C++ * Intel C/C++ Compiler (ICC) I'm generally willing to accept patches to add support for other compilers, as long as they're not too disruptive, *especially* if we can get CI support going. If using one of our existing CI providers isn't an option then other CI platforms can be added. ### Hardware The following architectures are tested in CI for every commit: * x86_64 * x86 * AArch64 * ARMv8 * ARMv7 * PPC64 * MIPS Loongson We would love to add more, so patches are extremely welcome! ## Related Projects * The "builtins" module in [portable-snippets](https://github.com/nemequ/portable-snippets) does much the same thing, but for compiler-specific intrinsics (think `__builtin_clz` and `_BitScanForward`), **not** SIMD intrinsics. * Intel offers an emulator, the [Intel® Software Development Emulator](https://software.intel.com/en-us/articles/intel-software-development-emulator/) which can be used to develop software which uses Intel intrinsics without having to own hardware which supports them, though it doesn't help for deployment. * [Iris](https://github.com/AlexYaruki/iris) is the only other project I'm aware of which is attempting to create portable implementations like SIMDe. SIMDe is much further along on the Intel side, but Iris looks to be in better shape on ARM. C++-only, Apache 2.0 license. AFAICT there are no accelerated fallbacks, nor is there a good way to add them since it relies extensively on templates. * There are a few projects trying to implement one set with another: * [ARM_NEON_2_x86_SSE](https://github.com/intel/ARM_NEON_2_x86_SSE) — implementing NEON using SSE. Quite extensive, Apache 2.0 license. * [sse2neon](https://github.com/jratcliff63367/sse2neon) — implementing SSE using NEON. This code has already been merged into SIMDe. * [veclib](https://github.com/IvantheDugtrio/veclib) — implementing SSE2 using AltiVec/VMX, using a non-free IBM library called [powerveclib](https://www.ibm.com/developerworks/community/groups/community/powerveclib/) * [SSE-to-NEON](https://github.com/otim/SSE-to-NEON) — implementing SSE with NEON. Non-free, C++. * [arm-neon-tests](https://github.com/christophe-lyon/arm-neon-tests) contains tests to verify NEON implementations. If you know of any other related projects, please [let us know](https://github.com/simd-everywhere/simde/issues/new)! ## Caveats Sometime features can't be emulated. If SIMDe is operating in native mode the functions will work as expected, but if there is no native support some caveats apply: * Many functions require and/or . SIMDe will still work without those headers, but the results of those functions are undefined. * x86 / x86_64 * SSE * `SIMDE_MM_SET_ROUNDING_MODE()` will use `fesetround()`, altering the global rounding mode. * `simde_mm_getcsr` and `simde_mm_setcsr` only implement bits 13 and 14 (rounding mode). * AVX * `simde_mm256_test*` do not set the CF/ZF registers as there is no portable way to implement that functionality. * `simde_mm256_zeroall` and `simde_mm256_zeroupper` are not implemented as there is no portable way to implement that functionality. Additionally, there are some known limitations which apply when using native aliases (`SIMDE_ENABLE_NATIVE_ALIASES`): * On Windows x86 (but not x86_64), some MMX functions and SSE/SSE2 functions which use MMX types (__m64) other than for pointers may return incorrect results. Also, as mentioned earlier, while some APIs make assumptions about basic types (*e.g.*, `int` is 32 bits), SIMDe does not, so many types have been altered to use portable fixed-width versions such as `int32_t`. If you find any other differences, please file an issue so we can either fix it or add it to the list above. ## Benefactors SIMDe uses resources provided for free by a number of organizations. While this shouldn't be taken to imply endorsement of SIMDe, we're tremendously grateful for their support: * [IntegriCloud](https://integricloud.com/) — provides access to a very fast POWER9 server for developing AltiVec/VMX support. * [GCC Compile Farm](https://gcc.gnu.org/wiki/CompileFarm) — provides access to a wide range of machines with different architectures for developing support for various ISA extensions. * [CodeCov.io](https://codecov.io/) — provides code coverage analysis for our test cases. * [Google](https://www.google.com/) ­— financing [Summer of Code](https://summerofcode.withgoogle.com/), substantial amounts of code (Sean Maher's contributions), and an [Open Source Peer Bonus](https://opensource.google/docs/growing/peer-bonus/). Without such organizations donating resources, SIMDe wouldn't be nearly as useful or usable as it is today. We would also like to thank anyone who has helped develop the myriad of software on which SIMDe relies, including compilers and analysis tools. Finally, a special thank you to [anyone who has contributed](https://github.com/simd-everywhere/simde/graphs/contributors) to SIMDe, filed bugs, provided suggestions, or helped with SIMDe development in any way. ## License SIMDe is distributed under an MIT-style license; see COPYING for details. ## Contributors ✨ Thanks goes to these wonderful people ([emoji key](https://allcontributors.org/docs/en/emoji-key)):

Evan Nemerson
💻 🖋 📖 💡 🤔 💬 👀 ⚠️ ✅ 📢 🐛 🚇 🚧 📆
Michael R. Crusoe
🐛 💻 📋 🔍 🤔 🚇 📦 ⚠️
HIMANSHI MATHUR
💻 ⚠️
Hidayat Khan
💻 ⚠️
rosbif
💻 ⚠️ 🐛 🤔 📖
Jun Aruga
💻 🤔 📦 🚇 🚧 ⚠️ 🐛
Élie ROUDNINSKI
💻 ⚠️

Jesper Storm Bache
💻
Jeff Daily
💻 🚇
Pavel
💻
Sabarish Bollapragada
💻
Gavin Li
💻
Yining Karl Li
💻
Anirban Dey
📖

Darren Ng
📖
FaresSalem
📖
Pradnyesh Gore
💻
Sean Maher
💻
Mingye Wang
📖
Ng Zhi An
💻 📖
Atharva Nimbalkar
💻 ⚠️

simba611
💻 ⚠️
Ashleigh Newman-Jones
💻 ⚠️
Willy R. Vasquez
💻 🚧 ⚠️
Keith Winstein
💻 🚧 ⚠️
David Seifert
🚧
Milot Mirdita
💻 🚧 ⚠️
aqrit
💻 🚧

Décio Luiz Gazzoni Filho
💻 🚧 ⚠️
Igor Molchanov
💻 🚧 📦
Andrew Rodriguez
💻 🚧 ⚠️
Changqing Jing
🚧
JP Cimalando
💻 🚇
Jiaxun Yang
💻 📦
Masahiro Kitagawa
💻 ⚠️

Pavel Iatchenii
💻 ⚠️
Tommy Vercetti
🚧
Robert Cohn
🚧
Adam Novak
📖
boris-kuz
🚧
Dimo Markov
🚧
dblue
🚧

zekehul
💻 🚧
Laurent Thomas
💻
Max Bachmann
📖
psaab
🚧
Sam Clegg
🚧
Thomas Lively
🐛 🤔 🚧
coderzh
💻 ⚠️

Dominik Kutra
💻 ⚠️
Lithrein
🚧
Nick
🚧
thomasdwu
🚧
Stephen
🐛
John Platts
🐛
Steven Noonan
🐛

p0nce
🐛
Paul Wise
🐛
This project follows the [all-contributors](https://github.com/all-contributors/all-contributors) specification. Contributions of any kind are welcome!