Parallel Molecular Dynamics: Implications for Massively Parallel Machines

doi:10.1006/jpdc.1997.1370

Journal of Parallel and Distributed Computing

Volume 45, Issue 2, 15 September 1997, Pages 166-175

https://doi.org/10.1006/jpdc.1997.1370 Get rights and content

Abstract

Molecular dynamics simulation is a class of applications that require reducing the execution time of fixed-size problems. This reduction in execution time is important to drug design and protein interaction studies. Many implementations of parallel molecular dynamics have been developed, but very little work has addressed issues related to the use of machines with 50,000 processors for modest-sized problems in the range of 50,000 atoms. Current massively parallel machines present a major obstacle to achieving good performance:communication overhead. In this paper we quantify the communication latency and network bandwidth necessary to achieve 30–40% efficiency on future message-passing machines with sizes on the order of tens of thousands of processors, for executing molecular dynamics problems with the same order of atoms. We derive an analytical model of a benchmark application that simulates a system of helium atoms executing on the Intel Touchstone Delta using an interaction decomposition method. This model is validated and used to extrapolate information on the startup time and network bandwidth. The results indicate that for an MPP with a four-dimensional mesh topology using 400 MHz processors, the communication startup time must be at most 30 clock cycles and the network bandwidth at least 2.3 GB/s. This configuration results in 30–40% efficiency of the MPP for a problem with 50,000 atoms executing on 50,000 processors.

References (17)

L. Greengard et al.
A fast algorithm for particle simulations
J. Comp. Phys.
(1987)
R.W. Hockney et al.
Quiet high-resolution computer models of a plasma
J. Comp. Phys.
(1974)
W. Smith
Molecular dynamics on hypercube parallel computers
Phys. Commun.
(1991)
J. Barnes et al.
A hierarchicalONN
Nature
(1986)
B.R. Brooks et al.
Parallelization of CHARMM for MIMD machines
Chem. Design Automation News
(1992)
T. W. Clark, J. A. McCammon, L. R. Scott, 1992, Parallel molecular dynamics, Proc. 5th SIAM Conference on Parallel...
H.-Q. Ding et al.
Atomic level simulations on a million particles: The cell multipole method for Coulomb and London nonbond interactions
J. Chem. Phys.
(1992)
D. Fincham
Parallel computers and molecular simulation
Mol. Simulation
(1987)

There are more references available in the full text version of this article.

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Research NoteParallel Molecular Dynamics: Implications for Massively Parallel Machines

Abstract

J. Comp. Phys.

J. Comp. Phys.

Phys. Commun.

A hierarchicalONN

Nature

Parallelization of CHARMM for MIMD machines

Chem. Design Automation News

Atomic level simulations on a million particles: The cell multipole method for Coulomb and London nonbond interactions

J. Chem. Phys.

Parallel computers and molecular simulation

Mol. Simulation

Research Note
Parallel Molecular Dynamics: Implications for Massively Parallel Machines