Talk (non-conference) (Other)

FZJ-2024-06879

http://join2-wiki.gsi.de/foswiki/pub/Main/Artwork/join2_logo100x88.png Parallel Performance Analysis

Geimer, M. (Corresponding author)FZJ*

2024

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Please use a persistent id in citations: doi:10.34734/FZJ-2024-06879

Abstract: To effectively harness the computing capabilities of todays and future supercomputing systems, performance analysis and optimization should be a regular activity during scientific software development. Instead of using do-it-yourself solutions usually based on coarse-grained timers (e.g., time per timestep or solver iteration), developers of scientific code bases can resort to a variety of spezialized tools that have been specifically developed to assist them with this task. In this tutorial, we will introduce the open-source tools Score-P and Cube, and explore their usage and capabilities with a number of hands-on exercises.

Note: Tutorial slides

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Contributing Institute(s):

1. Jülich Supercomputing Center (JSC)

Research Program(s):

1. 5112 - Cross-Domain Algorithms, Tools, Methods Labs (ATMLs) and Research Groups (POF4-511) (POF4-511)
2. ATMLPP - ATML Parallel Performance (ATMLPP) (ATMLPP)

Appears in the scientific report 2024

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