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When I use the previous feature extraction program, I find that the CPU utilization rate is extremely high. The experimental machine has 56 cores and the cpu occupancy rate is 5600%. Most of them are occupied by the sys kernel, indicating that the cpu is doing a lot of memory swapping.
After debugging, I found that the problem lies in the pca operation, numpy can not use gpu acceleration so that the cpu occupancy rate is higher, and because the memory exchange between the gpu and cpu cause the cpu to do a lot of extra work, so I try to use tensorflow rewrite pca operation, found that the efficiency of feature extraction increased by 27%, cpu utilization from 5600% to 240%.
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@LeegleechN Thanks for your review! I removed the extraneous print statement, please merge it. BTW, i double checked the cla email, why it is still not work
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When I use the previous feature extraction program, I find that the CPU utilization rate is extremely high. The experimental machine has 56 cores and the cpu occupancy rate is 5600%. Most of them are occupied by the sys kernel, indicating that the cpu is doing a lot of memory swapping.
After debugging, I found that the problem lies in the pca operation, numpy can not use gpu acceleration so that the cpu occupancy rate is higher, and because the memory exchange between the gpu and cpu cause the cpu to do a lot of extra work, so I try to use tensorflow rewrite pca operation, found that the efficiency of feature extraction increased by 27%, cpu utilization from 5600% to 240%.