Reproduction:

- Assuming x86 and preferably Linux.

- Install Nim

- Install a C compiler with OpenMP support (not the default MacOS Clang)

- Install git

The repo submodules MKLDNN (now Intel oneDNN) to bench vs Intel JIT Compiler

```

git clone https://github.com/mratsim/laser

cd laser

git submodule update --init --recursive

nim cpp -r --outdir:build -d:danger -d:openmp benchmarks/gemm/gemm_bench_float32.nim

```

This should output something like this

```

Laser production implementation

Collected 10 samples in 0.230 seconds

Average time: 22.684 ms

Stddev time: 0.596 ms

Min time: 21.769 ms

Max time: 23.603 ms

Perf: 624.037 GFLOP/s

OpenBLAS benchmark

Collected 10 samples in 0.216 seconds

Average time: 21.340 ms

Stddev time: 3.334 ms

Min time: 19.346 ms

Max time: 27.502 ms

Perf: 663.359 GFLOP/s

MKL-DNN JIT AVX512 benchmark

Collected 10 samples in 0.201 seconds

Average time: 19.775 ms

Stddev time: 8.262 ms

Min time: 15.625 ms

Max time: 43.237 ms

Perf: 715.855 GFLOP/s ```

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...). As a side bonus it also has parallel nqueens implemented.