Distributed Training of Generative Adversarial Networks for Fast Detector Simulation

Abstract

The simulation of the interaction of particles in High Energy Physics detectors is a computing intensive task. Since some level of approximation is acceptable, it is possible to implement fast simulation simplified models that have the advantage of being less computationally intensive. Here we present a fast simulation based on Generative Adversarial Networks (GANs). The model is constructed from a generative network describing the detector response and a discriminative network, trained in adversarial manner. The adversarial training process is compute-intensive and the application of a distributed approach becomes particularly important. We present scaling results of a data-parallel approach to distribute GANs training across multiple nodes on TACC’s Stampede2. The efficiency achieved was above 94% when going from 1 to 128 Xeon Scalable Processor nodes. We report on the accuracy of the generated samples and on the scaling of time-to-solution. We demonstrate how HPC installations could be utilized to globally optimize this kind of models leading to quicker research cycles and experimentation, thanks to their large computation power and excellent connectivity.