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Published: 1994

Total Pages: 22

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Short-range molecular dynamics simulations of molecular systems are commonly parallelized by replicated-data methods, where each processor stores a copy of all atom positions. This enables computation of bonded 2-, 3-, and 4-body forces within the molecular topology to be partitioned among processors straightforwardly. A drawback to such methods is that the inter-processor communication scales as N, the number of atoms, independent of P, the number of processors. Thus, their parallel efficiency falls off rapidly when large numbers of processors are used. In this paper a new parallel method called force-decomposition for simulating macromolecular or small-molecule systems is presented. Its memory and communication costs scale as N/(square root)P, allowing larger problems to be run faster on greater numbers of processors. Like replicated-data techniques, and in contrast to spatial-decomposition approaches, the new method can be simply load-balanced and performs well even for irregular simulation geometries. The implementation of the algorithm in a prototypical macromolecular simulation code ParBond is also discussed. On a 1024-processor Intel Paragon, ParBond runs a standard benchmark simulation of solvated myoglobin with a parallel efficiency of 61% and at 40 times the speed of a vectorized version of CHARMM running on a single Cray Y-MP processor.