Publication | Closed Access
Molecular dynamics---Scalable algorithms for molecular dynamics simulations on commodity clusters
2.4K
Citations
39
References
2006
Year
Unknown Venue
Cluster ComputingEngineeringComputer ArchitectureComputational ChemistryChemistryMolecular DynamicsMolecular DesignMolecular ComputingParallel SoftwareMd CodeParallel ComputingBiophysicsCluster ScienceMassively-parallel ComputingComputer EngineeringComputer ScienceBiomolecular DynamicsParallel Md SimulationsNatural SciencesParallel ProcessingComputational BiologyParallel ProgrammingData-level ParallelismMultiscale Modeling
MD simulations of biomolecular systems often take days to months, yet many important events occur on longer timescales that remain beyond reach. The study presents new algorithms and implementation techniques to significantly accelerate parallel MD simulations compared with current state‑of‑the‑art codes. The authors introduce a novel parallel decomposition, efficient message‑passing primitives, and single‑precision numerical techniques, all integrated into the Desmond code to achieve unprecedented throughput and scalability on commodity clusters. Desmond’s parallel performance surpasses all previously described codes, even outperforming IBM’s Blue Gene/L Blue Matter on a benchmark with 2,000 Opteron processors versus 32,000 processors.
Although molecular dynamics (MD) simulations of biomolecular systems often run for days to months, many events of great scientific interest and pharmaceutical relevance occur on long time scales that remain beyond reach. We present several new algorithms and implementation techniques that significantly accelerate parallel MD simulations compared with current state-of-the-art codes. These include a novel parallel decomposition method and message-passing techniques that reduce communication requirements, as well as novel communication primitives that further reduce communication time. We have also developed numerical techniques that maintain high accuracy while using single precision computation in order to exploit processor-level vector instructions. These methods are embodied in a newly developed MD code called Desmond that achieves unprecedented simulation throughput and parallel scalability on commodity clusters. Our results suggest that Desmond's parallel performance substantially surpasses that of any previously described code. For example, on a standard benchmark, Desmond's performance on a conventional Opteron cluster with 2K processors slightly exceeded the reported performance of IBM's Blue Gene/L machine with 32K processors running its Blue Matter MD code.
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