Expressing graph algorithms using generalized active messages

Authors:
Nick Edmonds;Jeremiah Willcock;Andrew Lumsdaine
Affiliations:
Indiana University, Bloomington, IN, USA;Indiana University, Bloomington, IN, USA;Indiana University, Bloomington, IN, USA
Venue:
Proceedings of the 18th ACM SIGPLAN symposium on Principles and practice of parallel programming
Year:
2013

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Expressing graph algorithms using generalized active messages

Proceedings of the 27th international ACM conference on International conference on supercomputing

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Abstract

Recently, graph computation has emerged as an important class of high-performance computing application whose characteristics differ markedly from those of traditional, compute-bound, kernels. Libraries such as BLAS, LAPACK, and others have been successful in codifying best practices in numerical computing. The data-driven nature of graph applications necessitates a more complex application stack incorporating runtime optimization. In this paper, we present a method of phrasing graph algorithms as collections of asynchronous, concurrently executing, concise code fragments which may be invoked both locally and in remote address spaces. A runtime layer performs a number of dynamic optimizations, including message coalescing, message combining, and software routing. Practical implementations and performance results are provided for a number of representative algorithms.