sse2neon
simde
sse2neon | simde | |
---|---|---|
7 | 7 | |
1,224 | 2,171 | |
1.2% | 1.5% | |
7.3 | 9.1 | |
14 days ago | 7 days ago | |
C++ | C | |
MIT License | MIT License |
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.
sse2neon
- sse2neon - A C/C++ header file that converts Intel SSE intrinsics to Aarch64 NEON intrinsic
- A C/C++ header file that converts Intel SSE intrinsics to Aarch64 NEON intrinsic
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Porting Architecture Specific C/C++ Intrinsics to Graviton
The sse2neon project is a quick way to get C/C++ applications compiling and running on Graviton. The sse2neon header file provides NEON implementations for x64 intrinsics so no source code changes are needed. Each function call (intrinsic) is simply replaced with NEON instructions and will just work on Graviton.
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An AWS Community Builder Story
To continue our collaboration I contributed some small changes to KasmVNC on GitHub to use sse2neon for a performance critical part of the application which uses SSE intrinsics and needed to be changed to NEON intrinsics.
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Deserializing JSON Fast
I think the talk is very clearly laid out as an incremental journey, and each stepping stone involves contextual decision-making. I don't think Andreas is saying "you must end up with the SSE2 implementation at the end". Using machine-specific intrinsics is another dependency decision very similar to deciding to use a given library. I would have loved the talk and probably still thought of it and posted it, even if it ended before the intrinsics (but I think he does an excellent job at that part too).
And porting SSE2 to Neon is actually pretty easy -- if you use https://github.com/DLTcollab/sse2neon, IME it's very easy to do incrementally (or avoid or postpone indefinitely, depending on your needs).
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PortableGL: An MIT licensed implementation of OpenGL 3.x-ish in clean C
I have a private cross-platform port, I’m waiting on the resolution of his latest GitHub issue to submit my changes. sse2neon (https://github.com/DLTcollab/sse2neon) was a big help - I also wrote a very primitive sse2scalar for raspbian builds where neon is unavailable. Honestly SIMD doesn’t help much, as you’re usually memory bound under SWGL. The biggest perf win is any amount of asynchronous execution - running off the main thread is good enough and could be applied to your library externally through a command buffer without any changes to your code.
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Success porting VCV into aarch64 linux! (Usable on Android Devices)
You should go to /include/simd and download sse2neon.h into the folder. Replace appearing in any source files in that directory with "sse2neon.h". You will still encounter errors; remove the lines causing problems, typically containing the phrase ZERO_MODE. ARM processors does not require it.
simde
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The Case of the Missing SIMD Code
I was curious about these libraries a few weeks ago and did some searching. Is there one that's got a clearly dominating set of users or contributors?
I don't know what a good way to compare these might be, other than perhaps activity/contributor count.
[1] https://github.com/simd-everywhere/simde
[2] https://github.com/ermig1979/Simd
[3] https://github.com/google/highway
[4] https://gitlab.com/libeigen/eigen
[5] https://github.com/shibatch/sleef
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Rise: Accelerate the Development of Open Source Software for RISC-V
I note that SIMDe doesn't have RISC-V support yet (but it does support Loongson LoongArch):
https://github.com/simd-everywhere/simde/
There are still a ton of things to do to get the Debian riscv64 port going too:
https://wiki.debian.org/PortsDocs/New
- SIMD intrinsics and the possibility of a standard library solution
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Portable SIMD library
SIMDe is everything you're after: https://github.com/simd-everywhere/simde
- SIMD Everywhere – SIMD intrinsics on hardware which doesn't support them
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Making Your Own Tools
> low level code that can run on multiple hardware architectures
I thought SIMD Everywhere was a pretty interesting project for that, lets you write x86 SSE/AVX code and run it on non-x86 architectures:
https://github.com/simd-everywhere/simde
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Adobe Photoshop Ships on Macs Apple Silicon/M1 – 50% Faster
> architecture-specific features such as SSE/AVX which is not portable.
I don’t have hands-on experience, but somewhere on HN I saw this: https://github.com/simd-everywhere/simde If starting a new cross-platform project today, I would try that library first, before doing the usual intrinsics.
What are some alternatives?
yenten-arm-miner-yespowerr16 - ARM 64 CPU miner for Yespower variant algorithms
nsimd - Agenium Scale vectorization library for CPUs and GPUs
KasmVNC - Modern VNC Server and client, web based and secure
android-inline-hook - :fire: ShadowHook is an Android inline hook library which supports thumb, arm32 and arm64.
Tow-Boot - An opinionated distribution of U-Boot. — https://matrix.to/#/#Tow-Boot:matrix.org?via=matrix.org
libsimdpp - Portable header-only C++ low level SIMD library
libsamplerate - An audio Sample Rate Conversion library
Sparkle - A software update framework for macOS
cglm - 📽 Highly Optimized 2D / 3D Graphics Math (glm) for C
picoRTOS - Very small, lightning fast, yet portable RTOS with SMP suppport
simdutf - Unicode routines (UTF8, UTF16, UTF32) and Base64: billions of characters per second using SSE2, AVX2, NEON, AVX-512, RISC-V Vector Extension. Part of Node.js and Bun.