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An experience report on (auto-)tuning of mesh-based PDE solvers on shared memory systems

Charrier, Dominic E.; Weinzierl, Tobias

An experience report on (auto-)tuning of mesh-based PDE solvers on shared memory systems Thumbnail


Authors

Dominic E. Charrier



Contributors

Roman Wyrzykowski
Editor

J. J. Dongarra
Editor

Ewa Deelman
Editor

Konrad Karczewski
Editor

Abstract

With the advent of manycore systems, shared memory parallelisation has gained importance in high performance computing. Once a code is decomposed into tasks or parallel regions, it becomes crucial to identify reasonable grain sizes, i.e. minimum problem sizes per task that make the algorithm expose a high concurrency at low overhead. Many papers do not detail what reasonable task sizes are, and consider their findings craftsmanship not worth discussion. We have implemented an autotuning algorithm, a machine learning approach, for a project developing a hyperbolic equation system solver. Autotuning here is important as the grid and task workload are multifaceted and change frequently during runtime. In this paper, we summarise our lessons learned. We infer tweaks and idioms for general autotuning algorithms and we clarify that such a approach does not free users completely from grain size awareness.

Citation

Charrier, D. E., & Weinzierl, T. (2018). An experience report on (auto-)tuning of mesh-based PDE solvers on shared memory systems. In R. Wyrzykowski, J. . J. Dongarra, E. Deelman, & K. Karczewski (Eds.), Parallel processing and applied mathematics : 12th International Conference, PPAM 2017, Lublin, Poland, September 10-13, 2017 ; revised selected papers. Part I (3-13). https://doi.org/10.1007/978-3-319-78054-2_1

Conference Name PPAM 2017
Conference Location Lublin, Poland
Acceptance Date Jun 21, 2017
Online Publication Date Mar 23, 2018
Publication Date Mar 23, 2018
Deposit Date Jun 21, 2017
Publicly Available Date Mar 23, 2019
Pages 3-13
Series Title Lecture notes in computer science
Series Number 10777
Series ISSN 0302-9743,1611-3349
Book Title Parallel processing and applied mathematics : 12th International Conference, PPAM 2017, Lublin, Poland, September 10-13, 2017 ; revised selected papers. Part I.
ISBN 9783319780238
DOI https://doi.org/10.1007/978-3-319-78054-2_1
Keywords Autotuning, Shared memory, Grain size, Machine learning.
Public URL https://durham-repository.worktribe.com/output/1147007

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