ginkgo
Halide
| ginkgo | Halide | |
|---|---|---|
| 2 | 43 | |
| 375 | 5,721 | |
| 1.9% | 0.7% | |
| 9.8 | 9.5 | |
| 2 days ago | 6 days ago | |
| C++ | C++ | |
| BSD 3-clause "New" or "Revised" License | GNU General Public License v3.0 or later |
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ginkgo
- AMD HIP + Cuda in same program
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Incorporating abidiff into CI?
I had exactly the same thought after watching the video (though I wanted to do this for a while anyways) and did exactly that :) I found it interesting to see how different changes impact the ABI of our library, even though we don't promise ABI compatibility or anything. We add the head of the diff part to a PR comment, and store everything else as a build artifact. See the corresponding PR here: https://github.com/ginkgo-project/ginkgo/pull/922
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?
What are some alternatives?
HIP - HIP: C++ Heterogeneous-Compute Interface for Portability
taichi - Productive, portable, and performant GPU programming in Python.
alpaka - Abstraction Library for Parallel Kernel Acceleration :llama:
futhark - :boom::computer::boom: A data-parallel functional programming language
arbor - The Arbor multi-compartment neural network simulation library.
Image-Convolutaion-OpenCL
MatX - An efficient C++17 GPU numerical computing library with Python-like syntax
TensorOperations.jl - Julia package for tensor contractions and related operations
stdgpu - stdgpu: Efficient STL-like Data Structures on the GPU
triton - Development repository for the Triton language and compiler
AdaptiveCpp - Implementation of SYCL and C++ standard parallelism for CPUs and GPUs from all vendors: The independent, community-driven compiler for C++-based heterogeneous programming models. Lets applications adapt themselves to all the hardware in the system - even at runtime!
ponyc - Pony is an open-source, actor-model, capabilities-secure, high performance programming language