Prediction of time-to-solution in material science simulations using deep learning

Predicting the time to solution for massively parallel scientific codes is a complex task. The reason for this is the presence of multiple, strongly interconnected algorithms that possibly react differently to the changes in compute power, vectorization length, memory and network bandwidth and latency and I/O throughput. A reliable prediction of execution time is however of great importance to the user who wants to plan on large scale simulations or virtual screening procedures characteristic of high throughput computing. In this article we present a practical approach based on machine learning techniques to achieve very accurate predictions of the time to solution for a DFT-based material science code. We compare our results with the predictions provided by a parametrized analytical performance model showing that deep learning solutions allow for a greater accuracy without the need of domain knowledge to introduce an explicit description of the algorithms implemented in the code.