c-examples
array
c-examples | array | |
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4 | 5 | |
4 | 189 | |
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9.1 | 6.9 | |
19 days ago | 5 months ago | |
C | C++ | |
GNU General Public License v3.0 or later | Apache License 2.0 |
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c-examples
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Benchmarking 20 programming languages on N-queens and matrix multiplication
So I actually tested your code: https://gist.github.com/bjourne/c2d0db48b2e50aaadf884e4450c6...
On my machine single-threaded OpenBLAS multiplies two single precision 4096x4096 matrices in 0.95 seconds. Your code takes over 30 seconds. For comparison, my own matrix multiplication code (https://github.com/bjourne/c-examples/blob/master/libraries/...) run in single-threaded mode takes 0.89 seconds. Which actually beats OpenBLAS, but OpenBLAS retakes the lead for larger arrays when multi-threading is added.
- Julia and Mojo (Modular) Mandelbrot Benchmark
- Reference Count, Don't Garbage Collect
array
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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?
ixy-languages - A high-speed network driver written in C, Rust, C++, Go, C#, Java, OCaml, Haskell, Swift, Javascript, and Python
optimizing-the-memory-layout-of-std-tuple - Optimizing the memory layout of std::tuple
mark-sweep - A simple mark-sweep garbage collector in C
NumPy - The fundamental package for scientific computing with Python.
.NET Runtime - .NET is a cross-platform runtime for cloud, mobile, desktop, and IoT apps.
cadabra2 - A field-theory motivated approach to computer algebra.
racket - The Racket repository
alphafold2 - To eventually become an unofficial Pytorch implementation / replication of Alphafold2, as details of the architecture get released
Mesh - A memory allocator that automatically reduces the memory footprint of C/C++ applications.
Einsum.jl - Einstein summation notation in Julia
plb2 - A programming language benchmark
einops - Flexible and powerful tensor operations for readable and reliable code (for pytorch, jax, TF and others)