CPU-gpuArray transfer speeds on modern GPUs
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For a long time, the conventional wisdom has been that, when computing on the GPU, one should try to minimize CPU-GPU transfers, since these transfers present a significant bottleneck. I have a comparatively old GPU (GTX 1080 Ti), where I see that the absolute transfer time of a typical 3D image is significant, though not exactly terrible.
>> A=rand(500,500,500); tic; B=gather(gpuArray(A)); r=B(1)+1; toc
Elapsed time is 0.636720 seconds.
I am wondering if this problem is still a prominent one on newer GPUs and if it might diminsh to a non-issue as GPU technology progresses. Basically, my question is a request to people with newer GPUs to run my benchmark test above and report the results.
7 个评论
Raymond Norris
2023-8-29
Here are a couple of results I ran on some HPC clusters (so you need to factor in other potential system "noise")
- V100-32GB
Elapsed time is 0.682715 seconds. % When run a 2nd time 0.549020 seconds
- P100-16GB
Elapsed time is 1.504278 seconds. % When run a 2nd time 0.580932 seconds
- A10G-24GB
Elapsed time is 1.132200 seconds. % When run a 2nd time 0.482009 seconds
- M60-8GB
Elapsed time is 1.747954 seconds. % When run a 2nd time 1.133121 seconds
Matt J
2023-8-29
Raymond Norris
2023-8-29
They are remote, but for the HPC systems, I ran MATLAB on a compute node and for AWS on the VM.
Joss Knight
2023-8-30
The data transfer time is commonly not the issue; usually it is the cost of GPU synchronisation. Every time you gather your data (which occurs automatically to display or plot so isn't always explicit) you force the computation of the gathered variable to complete. In some workflows heavily dependent on asynchronous execution this can seriously damage performance.
Bruno Luong
2023-8-30
Is the memory type or bus matters somehow or mainly the sync cost?
Matt J
2023-8-30
Bruno Luong
2023-8-30
Nvidia GF RTX 3060 Laptop
- Elapsed time is 0.464324 seconds.
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2023-8-30
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