SIMD Everywhere

The SIMDe header-only library provides fast, portable implementations of SIMD intrinsics on hardware which doesn't natively support them, such as calling SSE functions on ARM. There is no performance penalty if the hardware supports the native implementation (e.g., SSE/AVX runs at full speed on x86, NEON on ARM, 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 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 and __builtin_convertvector
Compiler auto-vectorization hints, using:

For an example of a project using SIMDe, see LZSSE-SIMDe.

You can try SIMDe online using Compiler Explorer and an amalgamated SIMDe header.

If you have any questions, please feel free to use the issue tracker or the mailing list.

Current Status

There are currently complete implementations of the following instruction sets:

As well as partial support for many others; see the instruction-set-support 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 master 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:

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. There is a good first issue 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 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, and 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 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.

For best performance, in addition to -O3 (or whatever your compiler's equivalent is), you should enable OpenMP 4 SIMD support by defining SIMDE_ENABLE_OPENMP before including any SIMDe headers, and enabling OpenMP support in your compiler. GCC and ICC both support a flag to enable only OpenMP SIMD support instead of full OpenMP (the OpenMP SIMD support doesn't require the OpenMP run-time library); for GCC the flag is -fopenmp-simd, for ICC -qopenmp-simd. SIMDe also supports using Cilk Plus, GCC loop-specific pragmas, or clang 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.

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 7
Microsoft Visual Studio back to 12 (2013)
IBM XL C/C++
Intel C/C++ Compiler (ICC)
PGI C Compiler

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. We currently use Travis CI, AppVeyor, and Microsoft Azure Pipelines, but other CI platforms can be added as necessary.

Hardware

The following architectures are tested in CI for every commit:

x86_64
x86
AArch64
ARMv8
ARMv7
PPC64
MIPS

We would love to add more, so patches are extremely welcome!

The tests currently contain some assumptions that they are running on a little-endian CPU. We're working on this, but for now big endian CPUs should work, but we can't promise.

Related Projects

The "builtins" module in 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 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 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 — implementing NEON using SSE. Quite extensive, Apache 2.0 license.
- sse2neon — implementing SSE using NEON. This code has already been merged into SIMDe.
- veclib — implementing SSE2 using AltiVec/VMX, using a non-free IBM library called powerveclib
- SSE-to-NEON — implementing SSE with NEON. Non-free, C++.
arm-neon-tests contains tests te verify NEON implementations.

If you know of any other related projects, please let us know!

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 <math.h> and/or <fenv.h>. 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:

GitHub — hosts our source repository, issue tracker, etc.
Travis CI — provides CI testing on numerous platforms.
AppVeyor — provides CI testing on Windows.
Drone CI — provides CI testing on ARM 32 bits platform, etc.
IntegriCloud — provides access to a very fast POWER9 server for developing AltiVec/VMX support.
GCC Compile Farm — provides access to a wide range of machines with different architectures for developing support for various ISA extensions.
CodeCov.io — provides code coverage analysis for our test cases.

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 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):

_{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 📖