On the Scalability of an Automatically Parallelized Irregular Application

Abstract

Abstract. Irregular applications, i.e., programs that manipulate pointer-based data structures such as graphs and trees, constitute a challenging target for parallelization because the amount of parallelism is input dependent and changes dynamically. Traditional dependence analysis techniques are too conservative to expose this parallelism. Even manual parallelization is difficult, time consuming, and error prone. The Galois system parallelizes such applications using an optimistic approach that exploits higher-level semantics of abstract data types.In this paper, we study the performance and scalability of a Galoised, i.e., automatically parallelized, version of Delaunay mesh refinement (DR) on a shared-memory system with 128 CPUs. DR is an important irregular application that is used, e.g., in graphics and finite-element codes. The parallelized program scales to 64 threads, where it reaches a speedup of 25.8. For large numbers of threads, the performance is hampered by the load imbalance and the nonuniform memory latency, both of which grow as the number of threads increases. While these two issues will have to be addressed in future work, we believe our results already show the Galois approach to be very promising.

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“…Wu and others extend the work by proving the NPcompleteness of the remapping problem and proposing two new optimizations to the irregular computations [20]. Other related studies concentrate on the memory performance of irregular computations on GPU [21,22,23,24]. A software framework named PORPLE has been recently proposed to dynamically finds and real-izes the best placements of data on the various types of memory on GPU [25].…”