Q: Were you running GPU version of Tensorflow backend? The performance improvement is unremarkable.

Q: Should you overclock your CPU to reduce Neural Network training time?Ī: Probably not. Each variable contributed to the overall performance increase, but a 5.15% overall reduction in training time is hardly worth writing home about. GPU clock and memory frequencies DO affect neural network training time! However, the results are lackluster - an overall 5.15% when running the CPU at 4.8GHz(+500MHz), the GPU Core clock at 2000MHz(+100MHz), and the GPU Memory clock at 4,000MHz(+200MHz).

GPU core runs at ~2,000MHz (+100MHz) during GPU memory testing.

GPU memory is 3,800MHz (default) during GPU core testing.

GPU runs at manufacturer's settings during CPU testing.

After 5 runs at each increment, the high and lows were dropped and the mean and standard deviation of the remaining 3 runs were recorded.

CPU: 4.3GHz - 4.8GHz in 100MHz increments (no AVX offset).

GPU: GTX 1060 3GB - 1,900MHz Core Clock, 3,800MHz Memory Clock.

CPU: Intel i7 8700K - 4.3GHz all-core turbo.

Testing was done on a CNN on MNIST dataset using Keras run in Jupyter notebook. So I decided to run a simple benchmark on this code. Have you ever wondered what happens if you overclock your rig for deep learning? I have.

How Does Overclocking CPU/GPU Affect Deep Learning Training Speed?

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