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

Charrier, Dominic E. and Weinzierl, Tobias (2018) 'An experience report on (auto-)tuning of mesh-based PDE solvers on shared memory systems.', in Parallel processing and applied mathematics : 12th International Conference, PPAM 2017, Lublin, Poland, September 10-13, 2017 ; revised selected papers. Part I. Cham: Springer, pp. 3-13. Lecture notes in computer science. (10777).

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.

Item Type:Book chapter
Keywords:Autotuning, Shared memory, Grain size, Machine learning.
Full text:(AM) Accepted Manuscript
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Status:Peer-reviewed
Publisher Web site:https://doi.org/10.1007/978-3-319-78054-2_1
Publisher statement:The final publication is available at Springer via https://doi.org/10.1007/978-3-319-78054-2_1
Date accepted:21 June 2017
Date deposited:22 June 2017
Date of first online publication:23 March 2018
Date first made open access:23 March 2019

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