weave
array
weave | array | |
---|---|---|
7 | 5 | |
524 | 189 | |
- | - | |
3.0 | 6.9 | |
5 months ago | 5 months ago | |
Nim | C++ | |
GNU General Public License v3.0 or later | Apache License 2.0 |
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weave
- The GIL can now be disabled in Python's main branch
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Maybe Everything Is a Coroutine
GPU drivers provide an event system:
- Cuda: https://github.com/mratsim/weave/issues/133
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Benchmarking 20 programming languages on N-queens and matrix multiplication
```
Note: the Theoretical peak limit is hardcoded and used my previous machine i9-9980XE.
It maybe that your BLAS library is not named libopenblas.so, you can change that here: https://github.com/mratsim/laser/blob/master/benchmarks/thir...
Implementation is in this folder: https://github.com/mratsim/laser/tree/master/laser/primitive...
in particular, tiling, cache and register optimization: https://github.com/mratsim/laser/blob/master/laser/primitive...
AVX512 code generator: https://github.com/mratsim/laser/blob/master/laser/primitive...
And generic Scalar/SSE/AVX/AVX2/AVX512 microkernel generator (this is Nim macros to generate code at compile-time): https://github.com/mratsim/laser/blob/master/laser/primitive...
I'll come back later with details on how to use my custom HPC threadpool Weave instead of OpenMP (https://github.com/mratsim/weave/tree/master/benchmarks/matm...)
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Nim vs Rust Benchmarks
In my benchmarks, Nim is faster than Rust:
- multithreading runtime (i.e Rayon vs Weave https://github.com/mratsim/weave)
- Cryptography: https://hackmd.io/@gnark/eccbench#Pairing
- Scientific computing / matrix multiplication: https://github.com/bluss/matrixmultiply/issues/34#issuecomme...
There is no inherent reason why a Nim program would be slower than Rust.
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Aren't green threads just better than async/await?
If you're interested into diving into this I have reviewed solutions to cactus stacks / split stacks here https://github.com/mratsim/weave/blob/master/weave/memory/multithreaded_memory_management.md
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Nim 2.0 – Thoughts
[4] https://github.com/mratsim/weave
array
-
Einsum in 40 Lines of Python
I wrote a library in C++ (I know, probably a non-starter for most reading this) that I think does most of what you want, as well as some other requests in this thread (generalized to more than just multiply-add): https://github.com/dsharlet/array?tab=readme-ov-file#einstei....
A matrix multiply written with this looks like this:
enum { i = 2, j = 0, k = 1 };
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Benchmarking 20 programming languages on N-queens and matrix multiplication
I should have mentioned somewhere, I disabled threading for OpenBLAS, so it is comparing one thread to one thread. Parallelism would be easy to add, but I tend to want the thread parallelism outside code like this anyways.
As for the inner loop not being well optimized... the disassembly looks like the same basic thing as OpenBLAS. There's disassembly in the comments of that file to show what code it generates, I'd love to know what you think is lacking! The only difference between the one I linked and this is prefetching and outer loop ordering: https://github.com/dsharlet/array/blob/master/examples/linea...
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A basic introduction to NumPy's einsum
If you are looking for something like this in C++, here's my attempt at implementing it: https://github.com/dsharlet/array#einstein-reductions
It doesn't do any automatic optimization of the loops like some of the projects linked in this thread, but, it provides all the tools needed for humans to express the code in a way that a good compiler can turn it into really good code.
What are some alternatives?
eioio - Effects-based direct-style IO for multicore OCaml
optimizing-the-memory-layout-of-std-tuple - Optimizing the memory layout of std::tuple
httpbeast - A highly performant, multi-threaded HTTP 1.1 server written in Nim.
NumPy - The fundamental package for scientific computing with Python.
matrixmultiply - General matrix multiplication of f32 and f64 matrices in Rust. Supports matrices with general strides.
cadabra2 - A field-theory motivated approach to computer algebra.
Edith - Electronic Design in Swithft
alphafold2 - To eventually become an unofficial Pytorch implementation / replication of Alphafold2, as details of the architecture get released
ocaml-multicore - Multicore OCaml
Einsum.jl - Einstein summation notation in Julia
cosmopolitan - build-once run-anywhere c library
c-examples - Example C code