Hello,
I am using "Matcovnet" to work on the Imagenet dataset for training and inference using fixed point operands. I am working on two machines simultaneously, both having different number of cores
1) 4 cores
2) 12 cores
I am running same dataset and same fixed point training code on both the machines simultaneously. I am getting 0.2 Hz frequency (0.2 images/sec) on both the machines, which ideally should not happen as the number of cores are different for both machines. I am having various parfor loops in the code.
The parfor loops are in the codegen files which are converted in the MEX functions by Matlabcoder toolbox.
My main question is:
1) Even if I use for loops instead of parfor my both machines uses all the cores by default with around 90% CPU-Utilization (just having parallel computing toolbox has this effect?) and also, my speed of computation/training is still the same that is 0.2 Hz, Can you please let me know about it ?
2) My 2nd question is, if I use parfor loop and run the code on this two machines why am I not getting any speedup on the machine with 12 cores compared to 4 cores ? Also, here on both machines I am getting the same training speed, that is 0.2 Hz.
Thank you in advance for your help !
Bhushan
