array
TrekBASIC
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array | TrekBASIC | |
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4 | 2 | |
187 | 6 | |
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6.9 | 5.6 | |
3 months ago | about 3 years ago | |
C++ | Python | |
Apache License 2.0 | - |
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array
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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...
This gets to 90% of BLAS: https://github.com/dsharlet/array/blob/38f8ce332fc4e26af0832...
But this is quite general. I’m claiming you can beat BLAS if you have some unique knowledge of the problem that you can exploit. For example, some kinds of sparsity can be implemented within the above example code yet still far outperform the more general sparsity supported by MKL and similar.
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A basic introduction to NumPy's einsum
Compilers can be pretty good if you help them out a bit. Here's my implementation of Einstein reductions (including summations) in C++, which generate pretty close to ideal code until you start getting into processor architecture specific optimizations: https://github.com/dsharlet/array#einstein-reductions
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.
TrekBASIC
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I built my first big side-project
I had a similar flashback recently.
I remembered the old Star Trek game game from the seventies. Many versions of it are available on line, but each was specific to a particular version of basic - versions I didn't have.
I ended up downloading one of the more promising versions of Star Trek, and then implementing a basic interpreter to run it, in python.
https://github.com/cocode/TrekBASIC If any one is interested.
For me, part of the fun was implementing things like code coverage and data breakpoints. Tools I never had then, but were handy to have to find issues.
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Why am I wasting time on EndBASIC?
Ok, I just made the repository public, in case anyone is curious: https://github.com/cocode/TrekBASIC
What are some alternatives?
optimizing-the-memory-layout-of-std-tuple - Optimizing the memory layout of std::tuple
NumPy - The fundamental package for scientific computing with Python.
array - Simple array language written in kotlin
cadabra2 - A field-theory motivated approach to computer algebra.
alphafold2 - To eventually become an unofficial Pytorch implementation / replication of Alphafold2, as details of the architecture get released
einops - Flexible and powerful tensor operations for readable and reliable code (for pytorch, jax, TF and others)
c-examples - Example C code
Einsum.jl - Einstein summation notation in Julia
plb2 - A programming language benchmark
laser - The HPC toolbox: fused matrix multiplication, convolution, data-parallel strided tensor primitives, OpenMP facilities, SIMD, JIT Assembler, CPU detection, state-of-the-art vectorized BLAS for floats and integers
Tullio.jl - ⅀
Kbd - Alternative unified APL keyboard layouts (AltGr, Backtick, Compositions)