laser
related_post_gen
laser | related_post_gen | |
---|---|---|
6 | 15 | |
264 | 291 | |
0.8% | - | |
3.6 | 9.9 | |
5 months ago | 14 days ago | |
Nim | C++ | |
Apache License 2.0 | MIT License |
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laser
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From slow to SIMD: A Go optimization story
It depends.
You need 2~3 accumulators to saturate instruction-level parallelism with a parallel sum reduction. But the compiler won't do it because it only creates those when the operation is associative, i.e. (a+b)+c = a+(b+c), which is true for integers but not for floats.
There is an escape hatch in -ffast-math.
I have extensive benches on this here: https://github.com/mratsim/laser/blob/master/benchmarks%2Ffp...
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Benchmarking 20 programming languages on N-queens and matrix multiplication
Ah,
It was from an older implementation that wasn't compatible with Nim v2. I've commented it out.
If you pull again it should work.
> Anyway the reason for your competitive performance is likely that you are benchmarking with very small matrices. OpenBLAS spends some time preprocessing the tiles which doesn't really pay off until they become really huge.
I don't get why you think it's impossible to reach BLAS speed. The matrix sizes are configured here: https://github.com/mratsim/laser/blob/master/benchmarks/gemm...
It defaults to 1920x1920 * 1920x1920. Note, if you activate the benchmarks versus PyTorch Glow, in the past it didn't support non-multiple of 16 or something, not sure today.
Packing is done here: https://github.com/mratsim/laser/blob/master/laser/primitive...
And it also support pre-packing which is useful to reimplement batch_matmul like what CuBLAS provides and is quite useful for convolution via matmul.
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Why does working with a transposed tensor not make the following operations less performant?
For convolutions: - https://github.com/numforge/laser/blob/e23b5d63/research/convolution_optimisation_resources.md
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Improve performance with SIMD intrinsics
You can train yourself on matrix transposition first. It's straightforward to get 3x speedup between naive transposition and double loop tiling, see: https://github.com/numforge/laser/blob/d1e6ae6/benchmarks/transpose/transpose_bench.nim#L238
related_post_gen
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Speed up your code: don't pass structs bigger than 16 bytes on AMD64
Looks like the HO means hand optimized, with special datastructures for this benchmark.
see: https://github.com/jinyus/related_post_gen/#user-content-fn-...
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Benchmarking 20 programming languages on N-queens and matrix multiplication
There is one for data processing here: https://github.com/jinyus/related_post_gen
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The Neat Programming Language
Is it ready for benchmarking? D currently sits at the top of https://github.com/jinyus/related_post_gen and it would be interesting to see how neat stacks up.
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Murder is a pixel art ECS game engine in C#
[2] https://github.com/jinyus/related_post_gen#multicore-results
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Jaq – A jq clone focused on correctness, speed, and simplicity
I think my benchmark[1] would be a great test for this. The jq[2] version takes 50s on my machine.
[1] : https://github.com/jinyus/related_post_gen
[2]: https://github.com/jinyus/related_post_gen/blob/main/jq/rela...
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Gleam vs Erlang vs Go vs Zig vs Rust for data processing
I added gleam to my data processing benchmark and the performance is less than stellar...so I hope someone here can make suggestions to improve it.
- jinyus/related_post_gen: Data Processing benchmark featuring Rust, Go, Swift, Zig, Julia etc.
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Ask HN: What's the big deal with Go (Golang)?
Easy concurrency.
ps: I wrote a data processing benchmark[1] and go is currently leading the charts. I ported it to c++ but it's not performing as expected. Take a look if you have the time.
[1]: https://github.com/jinyus/related_post_gen
- Julia leads Rust,Zig,Go and Java in data processing benchmark
- Julia Ranks First in Data Processing Microbenchmark
What are some alternatives?
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