Deploying Scientific AI Networks at Petaflop Scale on Secure Large Scale HPC Production Systems with Containers

2020/05/20 by D. Brayford, Brayford, David, Sofia Vallercorsa +1
Computer Science · Physics and Astronomy · #Advanced Data Storage Technologies #Distributed #FOS: Computer and information sciences #Parallel #Particle Detector Development and Performance #Particle physics theoretical and experimental studies #and Cluster Computing (cs.DC)

paper · pdf · doi:10.48550/arxiv.2005.10676

openalex publication_date 2020/05/20 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28

Abstract

There is an ever-increasing need for computational power to train complex artificial intelligence (AI) & machine learning (ML) models to tackle large scientific problems. High performance computing (HPC) resources are required to efficiently compute and scale complex models across tens of thousands of compute nodes. In this paper, we discuss the issues associated with the deployment of machine learning frameworks on large scale secure HPC systems and how we successfully deployed a standard machine learning framework on a secure large scale HPC production system, to train a complex three-dimensional convolutional GAN (3DGAN), with petaflop performance. 3DGAN is an example from the high energy physics domain, designed to simulate the energy pattern produced by showers of secondary particles inside a particle detector on various HPC systems.

Citations

• Application of Deep Convolutional Neural Networks for Detecting Extreme Weather in Climate Datasets
• Conditional Image Synthesis With Auxiliary Classifier GANs
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