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.
@article{arxiv.1505.04694,
title = {Thread Parallelism for Highly Irregular Computation in Anisotropic Mesh Adaptation},
author = {Georgios Rokos and Gerard J. Gorman and Kristian Ejlebjerg Jensen and Paul H. J. Kelly},
journal= {arXiv preprint arXiv:1505.04694},
year = {2015}
}