GPU training of neural network with parallel computing toolbox unreasonably slow, what am I missing?

PetterS asked: 2021-08-05 gpu , narnet , parallel computing , neural network

GPU training of neural networks with MATLABs Parallel Computing Toolbox slow? Discover potential bottlenecks and optimization tips to accelerate your training!

Expert Answer

Prashant Kumar answered . 2025-08-13 08:12:44

Getting a speed up with a GPU requires a couple things:

1) The amount of time spent in gradient calculations (which happen on CPU or GPU as you request) is significant compared to the training step update (which still happens on the CPU).

2) The problem allows enough parallelism to run efficiently on the much slower but much greater number of GPU cores relative to the CPU.

For both requirements, the larger the dataset and the larger the neural network, the more parallelism that can be taken advantage of and the greater percentage of calculations are in the gradient so the training steps are not a speed bottleneck.

The NAR problem you defined only has 899 steps with a 10 neuron network. The fact that both dataset and network are very small is why you are not seeing a speedup. Problems that take only a few seconds on CPU probably are not going to see much of a speedup with GPU.

You are correct, that at this time NAR networks using NNDATA2GPU formatted data result in faster training than gpuArray.

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