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I function `runCublasTF32` the comment is misleading/incomplete. Based on the cublas docs, the effect of `CUBLAS_COMPUTE_32F_FAST_TF32` is that it will use reduced precision `TF32` math with tensor cores for faster GEMM. Based on the documentation of `wmma` ops in the CUDA programming guide, the input will be converted with `__float_to_tf32` to a `float` (of numerically reduced `tf32` precision). As tensor cores in recent architectures support fp64 natively, I am curious what is the performance benefit of their usage over plain `fp64` CUDA computation. https://github.com/siboehm/SGEMM_CUDA/blob/60cba6f9b20a198116c76f18de8047f44df8c8b8/src/runner.cu#L145