I'm a little sceptical of your claims. Care to share the kernel you wrote?

I would bet actual money that they are not doing an apples-to-apples comparison.

I have seen how those high-performance libraries are made and I'm still in awe at the quality and quantity of the staffing involved. Those were the smartest and most knowledgeable engineers I met in my career.

It is apples-to-apples. The code from the article runs a chosen kernel 50 times in a loop. In the case of CuBLAS it runs the function cublasGemmEx() 50 times.

Does your implementation support the complete feature set of cublasGemmEx()? E.g. varying matrix sizes, etc. Does your implementation beat cublas on a wide range of cases, or only cherry-picked examples? Does the code from the article follow the best coding practices for cublas in the first place?

Generalizing from a micro benchmark is typically hubris.

I shared the code: https://github.com/arekpaterek/Faster_SGEMM_CUDA

If this were true (and I highly doubt it) it's obvious how to make money from it: collect a 7 figure paycheck from Nvidia, AMD, or any FAANG.

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?

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