Scaling FMM with data-driven OpenMP tasks on multicore architectures
Paper i proceeding, 2016

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.

Författare

Abdelhalim Amer

Argonne National Laboratory

S. Matsuoka

Tokyo Institute of Technology

Miquel Pericas

Chalmers, Data- och informationsteknik, Datorteknik

Naoya Maruyama

Riken

K. Taura

University of Tokyo

Rio Yokota

Tokyo Institute of Technology

Pavan Balaji

Argonne National Laboratory

Lecture Notes in Computer Science

0302-9743 (ISSN)

Vol. 9903 LNCS 156-170

Ämneskategorier

Datorteknik

Styrkeområden

Informations- och kommunikationsteknik

DOI

10.1007/978-3-319-45550-1_12

ISBN

978-331945549-5