Overcoming VRAM limitations on Nvidia A100

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I have access to a cluster with several Nvidia A100 40GB GPU's. I am training a deep learning network on these GPU's, however using trainNetwork() only makes use of around 10GB of the GPU's vRAM. I beleive this is a limitation of Nvidia Cuda, see here.
I have two related questions;
  1. Other cluster users are writting in python with the 'DistributedDataParallel' module in PyTorch and are able to load in 40Gb of data (over the cuda limitation) onto the GPU's; is there a similar work around for MATLAB?
  2. If this isn't the case is there any way to use Multi-instance GPU's, so essentially split the physical card into several smaller virtual GPU's and compute in parrellel?
Ideally I would like to speed up computation, so having a 3/4 of the vRAM empty which could otherwise be used for mini-batches is a little heart breaking.

Accepted Answer

Joss Knight
Joss Knight on 14 Mar 2023
Just increase the MiniBatchSize and it'll use more memory.
  6 Comments
Christopher McCausland
Christopher McCausland on 14 Mar 2023
Hi Joss,
That makes much more sense, thank you for the explination too.
I will be able to eek out the final 10% of GPU utilsation by finding the exact minibatchsize that cases the fail. Regardless, as you mentioned, down-sampling the data should allow for larger minibatchsize size too.
I will wait for an answer to https://uk.mathworks.com/matlabcentral/answers/1926685-deep-learning-with-partitionable-datastores-on-a-cluster?s_tid=srchtitle as i'll need partition to be true before I can use DispatchInBackground. Ideally, I would like to distrabute this over multiple GPU workers so hopefully I can get partition working.
In the mean time I will mark this question as answered and will @ you in the next one if I can get partition behaving.
Thank you!
Christopher
Joss Knight
Joss Knight on 14 Mar 2023
You may never get that 10% so don't get your hopes up! Also, the best utilization is not necessarily at the highest batch size.
Why not ask a new question where you show your code for your datastore and one of us can help you make it partitionable.

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