Optimizing Performance on Trinity Utilizing Machine Learning, Proxy Applications and Scheduling Priorities

arXiv:2404.10617v1 Announce Type: cross
Abstract: The sheer number of nodes continues to increase in todays supercomputers, the first half of Trinity alone contains more than 9400 compute nodes. Since the speed of todays clusters are limited by the slowest nodes, it more important than ever to identify slow nodes, improve their performance if it can be done, and assure minimal usage of slower nodes during performance critical runs. This is an ongoing maintenance task that occurs on a regular basis …

abstract applications arxiv compute cs.dc cs.lg ever identify machine machine learning nodes performance scheduling speed supercomputers type