I am tunning my GEMM code and comparing with Eigen and MKL. I have a system with four physical cores. Until now I have used the default number of threads from OpenMP (eight on my system). I assumed this would be at least as good as four threads. However, I discovered today that if I run Eigen and my own GEMM code on a large dense matrix (1000x1000) I get better performance using four threads instead of eight. The efficiency jumped from 45% to 65%. I think this can be also seen in this plot https://plafrim.bordeaux.inria.fr/doku.php?id=people:guenneba
The difference is quite substantial. However, the performance is much less stable. The performance jumps around quit a bit each iteration both with Eigen and my own GEMM code. I'm surprised that Hyperthreading makes the performance so much worse. I guess this is not not a question. It's an unexpected observation which I'm hoping to find feedback on.
I see that not using hyper threading is also suggested here.
How to speed up Eigen library's matrix product?
I do have a question regarding measuring max performance. What I do now is run CPUz and look at the frequency as I'm running my GEMM code and then use that number in my code (4.3 GHz on one overclocked system I use). Can I trust this number for all threads? How do I know the frequency per thread to determine the maximum? How to I properly account for turbo boost?