Computer simulation methods: in theoretical physics
Computer simulation methods: in theoretical physics
Computer architecture: a quantitative approach
Computer architecture: a quantitative approach
Introduction to algorithms
Cluster identification algorithms for spin models—sequential and parallel
Concurrency: Practice and Experience
Parallel Algorithms for Geometric Connected Component Labeling on a Hypercube Multiprocessor
IEEE Transactions on Computers
Digital Image Processing
IPPS '92 Proceedings of the 6th International Parallel Processing Symposium
Software infrastructure for non-uniform scientific computations on parallel processors
ACM SIGAPP Applied Computing Review
Irregular Coarse-Grain Data Parallelism under LPARX
Scientific Programming
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The cluster identification problem is a variant of connected component labeling that arises in cluster algorithms for spin models in statistical physics. We present a multidimensional version of Belkhale and Banerjee's Quad algorithm for connected component labeling on distributed memory parallel computers. Our extension abstracts away extraneous spatial connectivity information in more than two dimensions, simplifying implementation for higher dimensionality. We identify two types of locality present in cluster configurations, and present optimizations to exploit locality for better performance. Performance results from 2D, 3D, and 4D Ising model simulations with Swendson-Wang dynamics show that the optimizations improve performance by 20-80 percent.