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

Charrier, Dominic E.; Weinzierl, Tobias

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, March). An experience report on (auto-)tuning of mesh-based PDE solvers on shared memory systems. Presented at PPAM 2017, Lublin, Poland

• Presentation Conference Type: Conference Paper (published)
• Conference Name: PPAM 2017
• 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
• Print ISSN: 0302-9743
• 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