Expressing graph algorithms using generalized active messages

  • Authors:

  • Nicholas 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 27th international ACM conference on International conference on supercomputing
  • Year:
  • 2013

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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. We identify a number of common patterns in these algorithms, and explore how this programming model can express those patterns. Algorithmic transformations are discussed which expose asyn- chrony that can be leveraged by the runtime to improve performance and reduce resource utilization. Practical implementations and performance results are provided for a number of representative algorithms.