Contribution to a conference proceedings

FZJ-2023-01736

http://join2-wiki.gsi.de/foswiki/pub/Main/Artwork/join2_logo100x88.png A Comprehensive I/O Knowledge Cycle for Modular and Automated HPC Workload Analysis

Zhu, Z. ; Neuwirth, S. (Corresponding author)FZJ* ; Lippert, T.FZJ*

2022
IEEE

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Please use a persistent id in citations: http://hdl.handle.net/2128/34263  doi:10.1109/CLUSTER51413.2022.00076

Abstract: On the way to the exascale era, millions of parallel processing elements are required. Accordingly, one major chal-lenge is the ever-widening gap between computational power and underlying I/O systems. To bridge this gap, I/O resources must be used efficiently, thus a profound I/O knowledge is required. In this work, we analyze state-of-the-art approaches that can be applied to improve the general I/O understanding and performance. Based on our analysis, we present an automated, modular, tool-agnostic I/Oanalysis workflow and a prototype implementation that can be used to generate, extract, store, analyze, and use I/O knowledge in a structured and reproducible way.

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Contributing Institute(s):

1. Jülich Supercomputing Center (JSC)

Research Program(s):

1. 5121 - Supercomputing & Big Data Facilities (POF4-512) (POF4-512)

Appears in the scientific report 2023

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