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Scaling FMM with data-driven OpenMP tasks on multicore architectures

Abdelhalim Amer ; Satoshi Matsuoka ; Miquel Pericàs (Institutionen för data- och informationsteknik, Datorteknik (Chalmers)) ; Naoya Maruyama ; Kenjiro Taura ; Rio Yokota ; Pavan Balaji
Lecture Notes in Computer Science: 12th International Workshop on OpenMP, IWOMP 2016; Nara; Japan; 5 October 2016 through 7 October 2016 (03029743). Vol. 9903 LNCS (2016), p. 156-170.
[Konferensbidrag, refereegranskat]

Poor scalability on parallel architectures can be attributed to several factors, among which idle times, data movement, and runtime overhead are predominant. Conventional parallel loops and nested parallelism have proved successful for regular computational patterns. For more complex and irregular cases, however, these methods often perform poorly because they consider only a subset of these costs. Although data-driven methods are gaining popularity for efficiently utilizing computational cores, their data movement and runtime costs can be prohibitive for highly dynamic and irregular algorithms, such as fast multipole methods (FMMs). Furthermore, loop tiling, a technique that promotes data locality and has been successful for regular parallel methods, has received little attention in the context of dynamic and irregular parallelism. We present a method to exploit loop tiling in data-driven parallel methods. Here, we specify a methodology to spawn work units characterized by a high data locality potential. Work units operate on tiled computational patterns and serve as building blocks in an OpenMP task-based data-driven execution. In particular, by the adjusting work unit granularity, idle times and runtime overheads are also taken into account. We apply this method to a popular FMM implementation and show that, with careful tuning, the new method outperforms existing parallel-loop and user-level thread-based implementations by up to fourfold on 48 cores.



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Denna post skapades 2016-12-19. Senast ändrad 2017-01-27.
CPL Pubid: 246282

 

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