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Thread Parallelism for Highly Irregular Computation in Anisotropic Mesh Adaptation by Rokos, Georgios; Gorman, Gerard J.; Jensen, Kristian Ejlebjerg; Kelly, Paul H. J. is a scholarly article available to read on EtoBox.

What is Thread Parallelism for Highly Irregular Computation in Anisotropic Mesh Adaptation about?

Thread-level parallelism in irregular applications with mutable data dependencies presents challenges because the underlying data is extensively modified during execution of the algorithm and a high degree of parallelism must be realized while keeping the code race-free. In this article we describe a methodology for exploiting thread parallelism for a class of graph-mutating worklist algorithms, which guarantees safe parallel execution via processing in rounds of independent sets and using a deferred update strategy to commit changes in the underlying data structures. Scalability is assisted by atomic fetch-and-add operations to create worklists and work-stealing to balance the shared-memory workload. This work is motivated by mesh adaptation algorithms, for which we show a parallel efficiency of 60% and 50% on Intel(R) Xeon(R) Sandy Bridge and AMD Opteron(tm) Magny-Cours systems, respectively, using these techniques.

Author

Rokos, Georgios; Gorman, Gerard J.; Jensen, Kristian Ejlebjerg; Kelly, Paul H. J.

Published

2015

Language

EN