Several algorithms have been used for parallel molecular dynamics, including the replicated algorithm and those based on spatial decompositions. The replicated algorithm stores the entire system's coordinates and forces at each processor, and therefore has a low overhead in maintaining the data distribution. Spatial decompositions distribute the data, providing better locality and scalability with respect to memory and computation. We present EulerGromos, a parallelization of the Gromos molecular dynamics program which is based on a spatial decomposition. EulerGromos parallelizes all molecular dynamics phases, with most data structures using O(N=P ) memory. This paper focuses on the structure of EulerGromos and analyses its performance using molecular systems of current interest in the molecular dynamics community. EulerGromos achieves performance increases with as few as twenty atoms per processor. We also compare EulerGromos with an earlier parallelization of Gromos, UHGromos, wh...

A molecular-dynamics program typically takes several man-years to write and therefore is representative for a large class of scientific programs whose rewriting should not be taken lightly. This paper discusses two Intel hypercube adaptations, UHGROMOS and EulerGROMOS (in progress), of a "dusty-deck" moleculardynamics code, GROMOS. UHGROMOS uses a low-impact parallelization strategy to minimize modifications to GROMOS. In UHGROMOS, the nonbonded force computation, which usually accounts for at least 90% of the computation time in GROMOS, is the focus for parallelization. This simple approach results already in acceptable performance for typical applications and parallelcomputer resources. However, the lack of spatial locality in data distribution limits overall processor utilization. To overcome this limitation, EulerGROMOS uses a spatial decomposition which enhances locality and permits the design of scalable data-structures and algorithms for all parts of GROMOS. The two implementati...

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