Overcoming VRAM limitations on Nvidia A100

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Christopher McCausland
Christopher McCausland le 13 Mar 2023
Commenté : Joss Knight le 14 Mar 2023
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.

Réponse acceptée

Joss Knight
Joss Knight le 14 Mar 2023
Just increase the MiniBatchSize and it'll use more memory.
  6 commentaires
Joss Knight
Joss Knight le 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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