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Performance Analysis of Irregular Task-Based Applications on Hybrid Platforms: Structure Matters

Marcelo Cogo Miletto 1 Lucas Leandro Nesi 1 Lucas Mello Schnorr 1, * Arnaud Legrand 2 
* Corresponding author
2 POLARIS - Performance analysis and optimization of LARge Infrastructures and Systems
Inria Grenoble - Rhône-Alpes, LIG - Laboratoire d'Informatique de Grenoble
Abstract : Efficiently exploiting computational resources in heterogeneous platforms is a real challenge which has motivated the adoption of the task-based programming paradigm where resource usage is dynamic and adaptive. Unfortunately, classical performance visualization techniques used in routine performance analysis often fail to provide any insight in this new context, especially when the application structure is irregular. In this paper, we propose several performance visualization techniques tailored for the analysis of task-based multifrontal sparse linear solvers whose structure is particularly complex. We show that by building on both a performance model of irregular tasks and on structure of the application (in particular the elimination tree), we can detect and highlight anomalies and understand resource utilization from the application point-of-view in a very insightful way. We validate these novel performance analysis techniques with the QR_mumps sparse parallel solver by describing a series of case studies where we identify and address non trivial performance issues thanks to our visualization methodology.
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Contributor : Arnaud Legrand Connect in order to contact the contributor
Submitted on : Friday, July 23, 2021 - 2:57:12 PM
Last modification on : Wednesday, July 6, 2022 - 4:23:00 AM
Long-term archiving on: : Sunday, October 24, 2021 - 6:45:20 PM


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  • HAL Id : hal-03298021, version 1


Marcelo Cogo Miletto, Lucas Leandro Nesi, Lucas Mello Schnorr, Arnaud Legrand. Performance Analysis of Irregular Task-Based Applications on Hybrid Platforms: Structure Matters. 2021. ⟨hal-03298021⟩



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