GPU slower than CPU

Hi,

I switched Deep learning to use GPU instead of CPU(1 core), but this runs slower. I see that the GPU utilization is very less (2 to 3%) while the process is running. When I use CPU the CPU utilization is 70% approx. I am using a batch size of 32. Is it because of the smaller batch size?

Thanks,
Varun

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MartinLiebig

Hi @varunm1,
on how many examples are you learning? Keep in mind that the cost of getting it on the GPU is fairly high for small data sets. GPUs are useful if your data gets a bit larger.

BR,
Martin

hughesfleming68

I have seen this as well but it does not seem to be specific to any particular DL software. The last time I tested this with tensorflow, my CPU with 28 threads was 2x faster than the GPU. For my data sets, I have not found the GPU to help much so I guess it really depends on what you are trying to do. I have also noticed the low gpu utilization, I was under the impression at the time that Windows was not reporting those stats very well.

varunm1

Hi @mschmitz @hughesfleming68

Ya true what you said but the datasets are 400k and 1 million samples with 102 attributes. Thats the reason why I felt something wrong after looking at the utilization rates comparing both cpu and gpu. One interesting observation is that earlier for a similar data set gpu utilization is around 30 to 40 percent.

One more thing is that the dataset is sparse

Thanks
Varun

David_A

Hi @varunm1,

could you perhaps share your network setup with us? It would be interesting to see if there is room for improvements?

Best,
David

varunm1

Hi @David_A

Do you mean the xml code of neural network process?

Regards,
Varun

David_A

Yes,

with that it's easier to compare the CPU vs. GPU performance.

varunm1

@David_A

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David_A

Thanks a lot.

I'll investigate it, but I can't promise anything on the short term.
As @hughesfleming68 already mentioned, that's nothing RapidMiner specific and happens at a lot of Deep Learning frameworks.

varunm1

@David_A

Sure no problem, I just want to bring it to your notice.

Thanks,
Varun