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0 pointsmratsim6y ago0 comments

Thanks for the kind words, I think that crown is for MKL-DNN / OneDNN though, for now ;), and in the deep learning domain.

I also expect Halide[1], Taichi[2], DaCe[3] and Tiramisu[4] to be able to more easily reach those levels of performance hence why I want to add a compiler to Laser (implemented at compile-time via Nim macros) but prerequisite is excellent and composable multithreading support which OpenMP didn't provide.

In statistics / data analysis, my PCA though (Principal Component Analysis) beats all implementations I've seen from Scipy, Facebook and R.

[1]: https://halide-lang.org/

[2]: https://github.com/taichi-dev/taichi

[3]: https://github.com/spcl/dace

[4]: http://tiramisu-compiler.org/

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gavinray6y ago

I had seen Taichi (still not entirely sure what "Differentiable Computing" is!) and Tiramisu before but not Halide or DaCe. Neat =D

For whatever it's worth, Arraymancer was #1 on Matmul benchmark (no claims to it being a realistic measure of realworld implementation) until what must have been only the last few weeks:

https://github.com/kostya/benchmarks#matmul

At some point recently that D lang library snuck in the top spot.

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