Year: 2020
Type: preprint
Abstract: The ability to understand how a scientific application is executed on a large HPC system is of great importance in allocating resources within the HPC data center. In this paper, we describe how we used system performance data to identify: execution patterns, possible code optimizations and improvements to the system monitoring. We also identify candidates for employing machine learning techniques to predict the performance of similar scientific codes.
Source: arXiv (Cornell University)
Authors: Brayford, David, Bernau, Christoph, Hesse, Wolfram, Guillen, Carla
Institution: Bavarian Academy of Sciences and Humanities
Language: English
Subfield: Hardware and Architecture
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W3085656796