Communication Reducing Algorithms for Distributed Hierarchical N-Body Problems with Boundary Distributions

Mustafa Abdulmajeed AbdulJabbar, Georgios Markomanolis, Huda Ibeid, Rio Yokota, David E. Keyes

Research output: Chapter in Book/Report/Conference proceedingChapter

7 Scopus citations

Abstract

Reduction of communication and efficient partitioning are key issues for achieving scalability in hierarchical N-Body algorithms like Fast Multipole Method (FMM). In the present work, we propose three independent strategies to improve partitioning and reduce communication. First, we show that the conventional wisdom of using space-filling curve partitioning may not work well for boundary integral problems, which constitute a significant portion of FMM’s application user base. We propose an alternative method that modifies orthogonal recursive bisection to relieve the cell-partition misalignment that has kept it from scaling previously. Secondly, we optimize the granularity of communication to find the optimal balance between a bulk-synchronous collective communication of the local essential tree and an RDMA per task per cell. Finally, we take the dynamic sparse data exchange proposed by Hoefler et al. [1] and extend it to a hierarchical sparse data exchange, which is demonstrated at scale to be faster than the MPI library’s MPI_Alltoallv that is commonly used.
Original languageEnglish (US)
Title of host publicationLecture Notes in Computer Science
PublisherSpringer Nature
Pages79-96
Number of pages18
ISBN (Print)9783319586663
DOIs
StatePublished - May 12 2017

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