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Multiple GPU setup slower than single GPU

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arvid Martens
arvid Martens on 24 Apr 2018
Commented: Joss Knight on 17 May 2018
For my research I have to perform a lot of repetition of the same optimization (for statistics). I already found out that my fitness function is way faster on the GPU and as such I am performing those calculations on the available GPUs. Fortunately, I have 3 GPUs at my disposal, I worked out a scheme where I open a parallel pool and using parfeval I assign each GPU to a different optimization.
When I checked the performance of the this setup, I noticed that the speed of a single GPU decreases a lot (by half) when it is used in the multiple GPU setup (3 workers) compared to a single GPU setup (1 worker).
I rechecked the implementations and saw no signs that data has to be sent from one GPU to the other so they never have to be synchronized.
Solutions I have tried: - Make a fitness function mfile for each GPU (did not work) - Open a matlab instance for each GPU separately (did not work)
Suggestions on this problem are appreciated?
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Joss Knight
Joss Knight on 17 May 2018
You're right, sorry (about the double precision performance).
I wouldn't put too much stock in the Utilization measure, it is only weakly linked to performance. Much better would be to look at how long it is taking to run your code.
The only thing I can think of is that you are being limited by shared system resources. All three processes are sharing the PCI bus and system memory - perhaps there is a lot of data transfer. Or perhaps you are doing some large computations on the CPU that use all your cores? Even some GPU functions do that because they are hybrid algorithms (e.g. mldivide, eig, chol etc). Waiting for the CPU would slow the rate at which kernels are being launched on the GPU.
If you are running on Linux it would be interesting to see whether you can get any benefit out of using the Multi-Process Service.

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