I am learning deep learning recently and my friend recommended me caffe. After install it with OpenBLAS, I followed the tutorial, MNIST task in the doc. But later I found it was
@Karthik. That also works for me. One interesting discovery that I made was that using 4 threads reduces forward/backward pass during the caffe timing test by a factor of 2. However, increasing the thread count to 8 or even 24 results in f/b speed that is less than what I get with OPENBLAS_NUM_THREADS=4. Here are times for a few thread counts (tested on NetworkInNetwork model).
[#threads] [f/b time in ms]
1 223
2 150
4 113
8 125
12 144
For comparison, on a Titan X GPU the f/b pass took 1.87 ms.