High-Performance Graph Algorithms from Parallel Sparse Matrices

摘要

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.