Data and computation abstractions for dynamic and irregular computations

Authors:
Sriram Krishnamoorthy;Jarek Nieplocha;P. Sadayappan
Affiliations:
Department of Computer Science and Engineering, The Ohio State University, Columbus, OH;Computational Sciences and Mathematics, Pacific Northwest National Laboratory, Richland, WA;Department of Computer Science and Engineering, The Ohio State University, Columbus, OH
Venue:
HiPC'05 Proceedings of the 12th international conference on High Performance Computing
Year:
2005

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Abstract

Effective data distribution and parallelization of computations involving irregular data structures is a challenging task. We address the twin-problems in the context of computations involving block-sparse matrices. The programming model provides a global view of a distributed block-sparse matrix. Abstractions are provided for the user to express the parallel tasks in the computation. The tasks are mapped onto processors to ensure load balance and locality. The abstractions are based on the Aggregate Remote Memory Copy Interface, and are interoperable with the Global Arrays programming suite and MPI. Results are presented that demonstrate the utility of the approach.