Parallel ear decomposition search (EDS) and st-numbering in graphs
Theoretical Computer Science
Introduction to algorithms
A randomized linear-time algorithm to find minimum spanning trees
Journal of the ACM (JACM)
Fast priority queues for cached memory
Journal of Experimental Algorithmics (JEA)
Building a Multicasting Tree in a High-Speed Network
IEEE Concurrency
SIGMA: a simulator infrastructure to guide memory analysis
Proceedings of the 2002 ACM/IEEE conference on Supercomputing
Estimating cache misses and locality using stack distances
ICS '03 Proceedings of the 17th annual international conference on Supercomputing
Routing using implicit connection graphs [VLSI design
VLSID '96 Proceedings of the 9th International Conference on VLSI Design: VLSI in Mobile Communication
Array regrouping and structure splitting using whole-program reference affinity
Proceedings of the ACM SIGPLAN 2004 conference on Programming language design and implementation
ASPLOS XI Proceedings of the 11th international conference on Architectural support for programming languages and operating systems
Evaluation techniques for storage hierarchies
IBM Systems Journal
Visualizing evolving networks: minimum spanning trees versus pathfinder networks
INFOVIS'03 Proceedings of the Ninth annual IEEE conference on Information visualization
Locality behavior of parallel and sequential algorithms for irregular graph problems
PDCS '07 Proceedings of the 19th IASTED International Conference on Parallel and Distributed Computing and Systems
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Locality behavior study is crucial for achieving good performance for irregular problems. Graph algorithms with large, sparse inputs, for example, oftentimes achieve only a tiny fraction of the potential peak performance on current architectures. Compared with most numerical algorithms graph algorithms lay higher pressure on the memory system. In this paper, using the minimum spanning tree problem as an example, we study the locality behavior of graph algorithms, both sequential and parallel, for arbitrary, sparse instances. We show that the inherent locality of graph algorithms may not be favored by the current architecture, and parallel graph algorithms tend to have significantly poorer locality behaviors than their sequential counterparts. As memory hierarchy gets deeper and processors start to contain multi-cores, our study suggests that architectural support and new parallel algorithm designs are necessary for achieving good performance for irregular graph problems.