Abstract
Large-scale computation on graphs and other discrete structures is becoming increasingly important in many applications, including computational biology, web search, and knowledge discovery. High-performance combinatorial computing is an infant field, in sharp contrast with numerical scientific computing.
We argue that many of the tools of high-performance numerical computing – in particular, parallel algorithms and data structures for computation with sparse matrices – can form the nucleus of a robust infrastructure for parallel computing on graphs. We demonstrate this with an implementation of a graph analysis benchmark using the sparse matrix infrastructure in Star-P, our parallel dialect of the Matlab programming language.
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Gilbert, J.R., Reinhardt, S., Shah, V.B. (2007). High-Performance Graph Algorithms from Parallel Sparse Matrices. In: Kågström, B., Elmroth, E., Dongarra, J., Waśniewski, J. (eds) Applied Parallel Computing. State of the Art in Scientific Computing. PARA 2006. Lecture Notes in Computer Science, vol 4699. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-75755-9_32
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DOI: https://doi.org/10.1007/978-3-540-75755-9_32
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-75754-2
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