oneDNN
highway
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oneDNN | highway | |
---|---|---|
5 | 66 | |
3,456 | 3,623 | |
2.5% | 3.3% | |
10.0 | 9.8 | |
1 day ago | 5 days ago | |
C++ | C++ | |
Apache License 2.0 | Apache License 2.0 |
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.
oneDNN
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Blaze: A High Performance C++ Math library
If you are talking about non-small matrix multiplication in MKL, is now in opensource as a part of oneDNN. It literally has exactly the same code, as in MKL (you can see this by inspecting constants or doing high-precision benchmarks).
For small matmul there is libxsmm. It may take tremendous efforts make something faster than oneDNN and libxsmm, as jit-based approach of https://github.com/oneapi-src/oneDNN/blob/main/src/gpu/jit/g... is too flexible: if someone finds a better sequence, oneDNN can reuse it without major change of design.
But MKL is not limited to matmul, I understand it...
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Arc & Deep Learning Frameworks
For completeness, it looks like this question was posted to the oneDNN GitHub repo and the response was to stay tune for updates.
- Keeping POWER relevant in the open source world
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Intel oneDNN 2.5 released with experimental RISC-V support
From the release note of oneDNN v2.5:
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Is gpu hardware tied to cpu ISA ?
Intel are trying to support their oneAPI compute framework on Arm and IBM POWER and z/Architecture (s390x) but since they ever released only a single discrete GPU with the Xe architecture it's unclear whether they'll support Xe GPU compute on e.g. ARM https://github.com/oneapi-src/oneDNN
highway
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Llamafile 0.7 Brings AVX-512 Support: 10x Faster Prompt Eval Times for AMD Zen 4
The bf16 dot instruction replaces 6 instructions: https://github.com/google/highway/blob/master/hwy/ops/x86_12...
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JPEG XL and the Pareto Front
[0] for those interested in Highway.
It's also mentioned in [1], which starts off
> Today we're sharing open source code that can sort arrays of numbers about ten times as fast as the C++ std::sort, and outperforms state of the art architecture-specific algorithms, while being portable across all modern CPU architectures. Below we discuss how we achieved this.
[0] https://github.com/google/highway
[1] https://opensource.googleblog.com/2022/06/Vectorized%20and%2..., which has an associated paper at https://arxiv.org/pdf/2205.05982.pdf.
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Gemma.cpp: lightweight, standalone C++ inference engine for Gemma models
Thanks so much!
Everyone working on this self-selected into contributing, so I think of it less as my team than ... a team?
Specifically want to call out: Jan Wassenberg (author of https://github.com/google/highway) and I started gemma.cpp as a small project just a few months ago + Phil Culliton, Dan Zheng, and Paul Chang + of course the GDM Gemma team.
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From slow to SIMD: A Go optimization story
C++ users can enjoy Highway [1].
[1] https://github.com/google/highway/
- GDlog: A GPU-Accelerated Deductive Engine
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Designing a SIMD Algorithm from Scratch
At that point it is better to have some kind of DSL that should not be in the main language, because it would target a much lower level than a typical program. The best effort I've seen in this scene was Google's Highway [1] (not to be confused with HighwayHash) and I even once attempted to recreate it in Rust, but it is still distanced from my ideal.
[1] https://github.com/google/highway
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SIMD Everywhere Optimization from ARM Neon to RISC-V Vector Extensions
Interesting, thanks for sharing :)
At the time we open-sourced Highway, the standardization process had already started and there were some discussions.
I'm curious why stdlib is the only path you see to default? Compare the activity level of https://github.com/VcDevel/std-simd vs https://github.com/google/highway. As to open-source usage, after years of std::experimental, I see <200 search hits [1], vs >400 for Highway [2], even after excluding several library users.
But that aside, I'm not convinced standardization is the best path for a SIMD library. We and external users extend Highway on a weekly basis as new use cases arise. What if we deferred those changes to 3-monthly meetings, or had to wait for one meeting per WD, CD, (FCD), DIS, (FDIS) stage before it's standardized? Standardization seems more useful for rarely-changing things.
1: https://sourcegraph.com/search?q=context:global+std::experim...
2: https://sourcegraph.com/search?q=context:global+HWY_NAMESPAC...
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Permuting Bits with GF2P8AFFINEQB
Thanks for the link. We were previously using GFNI for bit reversal and 8-bit shifts, and I just extended that to our 8-bit BroadcastSignBit (https://github.com/google/highway/pull/1784).
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Six times faster than C
You could study Google's Highway library [1].
[1] https://github.com/google/highway
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AMD EPYC 97x4 “Bergamo” CPUs: 128 Zen 4c CPU Cores for Servers, Shipping Now
Runtime feature detection need not be rare nor hard, it's a few dozen lines of boilerplate. You can even write your code just once: see https://github.com/google/highway#examples.
What are some alternatives?
oneMKL - oneAPI Math Kernel Library (oneMKL) Interfaces
xsimd - C++ wrappers for SIMD intrinsics and parallelized, optimized mathematical functions (SSE, AVX, AVX512, NEON, SVE))
CTranslate2 - Fast inference engine for Transformer models
Vc - SIMD Vector Classes for C++
oneDPL - oneAPI DPC++ Library (oneDPL) https://software.intel.com/content/www/us/en/develop/tools/oneapi/components/dpc-library.html
swup - Versatile and extensible page transition library for server-rendered websites 🎉
highway - Highway - A Modern Javascript Transitions Manager
DirectXMath - DirectXMath is an all inline SIMD C++ linear algebra library for use in games and graphics apps
asmjit - Low-latency machine code generation
riscv-v-spec - Working draft of the proposed RISC-V V vector extension
librealsense - Intel® RealSense™ SDK
jpeg-xl