Halide
highway
Halide | highway | |
---|---|---|
43 | 66 | |
5,714 | 3,656 | |
0.5% | 2.1% | |
9.5 | 9.8 | |
3 days ago | 2 days ago | |
C++ | C++ | |
GNU General Public License v3.0 or later | 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.
Halide
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Show HN: Flash Attention in ~100 lines of CUDA
If CPU/GPU execution speed is the goal while simultaneously code golfing the source size, https://halide-lang.org/ might have come in handy.
- Halide v17.0.0
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From slow to SIMD: A Go optimization story
This is a task where Halide https://halide-lang.org/ could really shine! It disconnects logic from scheduling (unrolling, vectorizing, tiling, caching intermediates etc), so every step the author describes in the article is a tunable in halide. halide doesn't appear to have bindings for golang so calling C++ from go might be the only viable option.
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Implementing Mario's Stack Blur 15 times in C++ (with tests and benchmarks)
Probably would have been much easier to do 15 times in https://halide-lang.org/
The idea behind Halide is that scheduling memory access patterns is critical to performance. But, access patterns being interwoven into arithmetic algorithms makes them difficult to modify separately.
So, in Halide you specify the arithmetic and the schedule separately so you can rapidly iterate on either.
- Making Hard Things Easy
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Deepmind Alphadev: Faster sorting algorithms discovered using deep RL
It is not the sorting per-se which was improved here, but sorting (particularly short sequences) on modern CPUs with really the complexity being on the difficulty of predicting what will work quickly on these modern CPUs.
Doing an empirical algorithm search to find which algorithms fit well on modern CPUs/memory systems is pretty common, see e.g. FFTW, ATLAS, https://halide-lang.org/
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Two-tier programming language
Halide https://halide-lang.org/
- Best book on writing an optimizing compiler (inlining, types, abstract interpretation)?
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Blog Post: Can You Trust a Compiler to Optimize Your Code?
It doesn’t apply in this case, but in general if you really want the best vectorization I would suggest using https://halide-lang.org instead of trying to coerce your compiler.
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What would make you try a new language?
If we drop the "APL" requirement, wouldn't Halide fit your criteria for the third?
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?
taichi - Productive, portable, and performant GPU programming in Python.
xsimd - C++ wrappers for SIMD intrinsics and parallelized, optimized mathematical functions (SSE, AVX, AVX512, NEON, SVE))
futhark - :boom::computer::boom: A data-parallel functional programming language
Vc - SIMD Vector Classes for C++
Image-Convolutaion-OpenCL
swup - Versatile and extensible page transition library for server-rendered websites 🎉
TensorOperations.jl - Julia package for tensor contractions and related operations
DirectXMath - DirectXMath is an all inline SIMD C++ linear algebra library for use in games and graphics apps
triton - Development repository for the Triton language and compiler
riscv-v-spec - Working draft of the proposed RISC-V V vector extension
ponyc - Pony is an open-source, actor-model, capabilities-secure, high performance programming language
jpeg-xl