Then I tried to fit the network with the same architecture and dataset on Amazon’s p2.xlarge instance with NVIDIA K80 on-board. All this computations was done on my GPU-enabled laptop with NVIDIA GeForce 840M card-not a best choice, but convenient for the first try. After first try, even though I use GPU for training (TensorFlow backend for Keras), it was quite a long time to fit the network for dozens of epochs on quite a tiny dataset.
I used high-level machine learning framework Keras for this purposes. Recently we decided to try RNN in our project and started investigation in this direction. However there was found that GPU computational facilities is not fully exploited on such operations and resulting performance is not even close to the maximum. We’re figured out that on basic hardware, such as NVIDIA GeForce 840M installed on my laptop, speedup is not so significant compared to CPU, but NVIDIA K80 card gives quite a good speedup.
BENCHMARK TEST GPU NEURAL NETWORK CODE
We compare different approaches, such as usage of ready-to-use computation frameworks such as TensorFlow, cuBLAS as well as handwritten code using CUDA. In this article we’re testing performance of the basic neural network training operation-matrix-vector multiplication using basic and kind of top GPUs, AWS p2.xlarge instance concretely, to see whether they are doing well in such operations.
It’s clear that GPUs are faster than CPU, but how much and do they do their best on such tasks. Nowadays GPUs are widely used for neural networks training and inference. Posted on FebruFebruby Julia Nedzelskaya Abstract
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