Analysis of scalable data-privatization threading algorithms for hybrid MPI/OpenMP parallelization of molecular dynamics

Abstract

We propose and analyze threading algorithms for hybrid MPI/OpenMP parallelization of a molecular-dynamics simulation, which are scalable on large multicore clusters. Two data-privatization thread scheduling algorithms via nucleation-growth allocation are introduced: (1) compact-volume allocation scheduling (CVAS); and (2) breadth-first allocation scheduling (BFAS). The algorithms combine fine-grain dynamic load balancing and minimal memory-footprint data privatization threading. We show that the computational costs of CVAS and BFAS are bounded by Θ(n 5/3 p −2/3) and Θ(n), respectively, for p threads working on n particles on a multicore compute node. Memory consumption per node of both algorithms scales as O(n+n 2/3 p 1/3), but CVAS has smaller prefactors due to a geometric effect. Based on these analyses, we derive the selection criterion between the two algorithms in terms of the granularity, n/p. We observe that memory consumption is reduced by 75 % for p=16 and n=8,192 compared to a naïve data privatization, while maintaining thread imbalance below 5 %. We obtain a strong-scaling speedup of 14.4 with 16-way threading on a four quad-core AMD Opteron node. In addition, our MPI/OpenMP code achieves 2.58× and 2.16× speedups over the MPI-only implementation on 32,768 cores of BlueGene/P for 0.84 and 1.68 million particle systems, respectively.