Halide
maxas
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Halide | maxas | |
---|---|---|
43 | 3 | |
5,700 | 784 | |
1.0% | - | |
9.5 | 0.0 | |
6 days ago | over 1 year ago | |
C++ | Sass | |
GNU General Public License v3.0 or later | MIT License |
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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?
maxas
- With LLVM and MLIR, is manual cuda optimizing still important?
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How to make CUDA libraries more performant?
cuDNN is already very optimized but if you want to read on optimizing, here you go (Maxwell specifix) https://github.com/NervanaSystems/maxas/wiki/SGEMM, there is an accompanying paper or read Nvidia Cutlass.
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Image convolution optimisation strategies.
As explained in https://github.com/NervanaSystems/maxas/wiki/SGEMM you need to do the same on GPUs.
What are some alternatives?
taichi - Productive, portable, and performant GPU programming in Python.
triton - Development repository for the Triton language and compiler
futhark - :boom::computer::boom: A data-parallel functional programming language
blislab - BLISlab: A Sandbox for Optimizing GEMM
Image-Convolutaion-OpenCL
TensorOperations.jl - Julia package for tensor contractions and related operations
cutlass - CUDA Templates for Linear Algebra Subroutines
ponyc - Pony is an open-source, actor-model, capabilities-secure, high performance programming language
qoi - The “Quite OK Image Format” for fast, lossless image compression
png-decoder - A pure-Rust, no_std compatible PNG decoder