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Comment by ap4 | Hacker News Reader

I swapped one of the kernels in the code from the article to my kernel, and left only the multiplication of matrices of size 4096².

On average over 20 runs:

CuBLAS (./sgemm 0) has 50.9 TFLOPS.

My kernel has 61.8 TFLOPS, so it's actually +21% speedup in this benchmark.

How do I collect my paycheck?

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I gotta see it to believe it ;)

Believe it or not.

On a 4090 gpu, average of 20 runs of SGEMM_CUDA:

  size    tflops_cublas  tflops_my  diff
  4096²   50.8-50.9      61.8       +21%
  8192²   56.3-56.4      67.1       +19%
  16384²  53.6           66.7       +24%

I guess the right thing to do now would be to hire a B2B salesman and figure out, which company needs it.

For all doubters, I open-sourced it: https://github.com/arekpaterek/Faster_SGEMM_CUDA

Post the code and your curriculum

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