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The Scalasca performance toolset architecture

Authors:

Bernd MohrAuthors Info & Claims

Concurrency and Computation: Practice & Experience, Volume 22, Issue 6

Pages 702 - 719

Published: 01 April 2010 Publication History

Abstract

Scalasca is a performance toolset that has been specifically designed to analyze parallel application execution behavior on large-scale systems with many thousands of processors. It offers an incremental performance-analysis procedure that integrates runtime summaries with in-depth studies of concurrent behavior via event tracing, adopting a strategy of successively refined measurement configurations. Distinctive features are its ability to identify wait states in applications with very large numbers of processes and to combine these with efficiently summarized local measurements. In this article, we review the current toolset architecture, emphasizing its scalable design and the role of the different components in transforming raw measurement data into knowledge of application execution behavior. The scalability and effectiveness of Scalasca are then surveyed from experience measuring and analyzing real-world applications on a range of computer systems. Copyright © 2010 John Wiley & Sons, Ltd.

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Jin YWang HZhong RZhang CLiao XZhang FZhai J(2024)Graph-Centric Performance Analysis for Large-Scale Parallel ApplicationsIEEE Transactions on Parallel and Distributed Systems10.1109/TPDS.2024.339684935:7(1221-1238)Online publication date: 6-May-2024
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The Scalasca performance toolset architecture

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cover image Concurrency and Computation: Practice & Experience

Concurrency and Computation: Practice & Experience Volume 22, Issue 6

Scalable Tools for High-End Computing

April 2010

84 pages

ISSN:1532-0626

EISSN:1532-0634

Issue’s Table of Contents

Publisher

John Wiley and Sons Ltd.

United Kingdom

Publication History

Published: 01 April 2010

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Jin YWang HZhong RZhang CLiao XZhang FZhai J(2024)Graph-Centric Performance Analysis for Large-Scale Parallel ApplicationsIEEE Transactions on Parallel and Distributed Systems10.1109/TPDS.2024.339684935:7(1221-1238)Online publication date: 6-May-2024
https://dl.acm.org/doi/10.1109/TPDS.2024.3396849
Sen SVanecek SSchulz M(2023)GPUscout: Locating Data Movement-related Bottlenecks on GPUsProceedings of the SC '23 Workshops of The International Conference on High Performance Computing, Network, Storage, and Analysis10.1145/3624062.3624208(1392-1402)Online publication date: 12-Nov-2023
https://dl.acm.org/doi/10.1145/3624062.3624208
Varbanescu ASwatman SPathania A(2023)Performance Engineering for Graduate Students: a View from AmsterdamProceedings of the SC '23 Workshops of The International Conference on High Performance Computing, Network, Storage, and Analysis10.1145/3624062.3624102(357-365)Online publication date: 12-Nov-2023
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Hunold SAjanohoun JVardas ITräff JScully-Allison CLiem RVeroneze Solorzano A(2022)An Overhead Analysis of MPI Profiling and Tracing ToolsProceedings of the 2nd Workshop on Performance EngineeRing, Modelling, Analysis, and VisualizatiOn Strategy10.1145/3526063.3535353(5-13)Online publication date: 30-Jun-2022
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Jin YWang HZhong RZhang CZhai JLee JAgrawal KSpear M(2022)PerFlowProceedings of the 27th ACM SIGPLAN Symposium on Principles and Practice of Parallel Programming10.1145/3503221.3508405(177-191)Online publication date: 2-Apr-2022
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Zhai JZheng LSun JZhang FTang XQian XHe BXue WChen WZheng W(2022)Leveraging Code Snippets to Detect Variations in the Performance of HPC SystemsIEEE Transactions on Parallel and Distributed Systems10.1109/TPDS.2022.315874233:12(3558-3574)Online publication date: 1-Dec-2022
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