The distributed diagonal force decomposition method for parallelizing molecular dynamics simulations

Parallelization is an effective way to reduce the computational time needed for molecular dynamics simulations. We describe a new parallelization method, the distributed‐diagonal force decomposition method, with which we extend and improve the existing force decomposition methods. Our new method requires less data communication during molecular dynamics simulations than replicated data and current force decomposition methods, increasing the parallel efficiency. It also dynamically load‐balances the processors' computational load throughout the simulation. The method is readily implemented in existing molecular dynamics codes and it has been incorporated into the CHARMM program, allowing its immediate use in conjunction with the many molecular dynamics simulation techniques that are already present in the program. We also present the design of the Force Decomposition Machine, a cluster of personal computers and networks that is tailored to running molecular dynamics simulations using the distributed diagonal force decomposition method. The design is expandable and provides various degrees of fault resilience. This approach is easily adaptable to computers with Graphics Processing Units because it is independent of the processor type being used. © 2011 Wiley Periodicals, Inc. J Comput Chem, 2011

[1]  David E. Shaw,et al.  Overview of neutral territory methods for the parallel evaluation of pairwise particle interactions , 2005 .

[2]  Yanli Wang,et al.  Quantum chemical study of π–π stacking interactions of the bacteriochlorophyll dimer in the photosynthetic reaction center of Rhodobacter sphaeroides , 2002 .

[3]  Min Chen,et al.  Optimization techniques for parallel force-decomposition algorithm in molecular dynamic simulations , 2003 .

[4]  Dusanka Janezic,et al.  Improving the Performance of Molecular Dynamics Simulations on Parallel Clusters , 2004, J. Chem. Inf. Model..

[5]  Joseph A. Bank,et al.  Supporting Online Material Materials and Methods Figs. S1 to S10 Table S1 References Movies S1 to S3 Atomic-level Characterization of the Structural Dynamics of Proteins , 2022 .

[6]  Thomas L. Sterling,et al.  A design study of alternative network topologies for the Beowulf parallel workstation , 1996, Proceedings of 5th IEEE International Symposium on High Performance Distributed Computing.

[7]  Klaus Schulten,et al.  Multilevel summation of electrostatic potentials using graphics processing units , 2009, Parallel Comput..

[8]  Marc Snir,et al.  A Note on N-Body Computations with Cutoffs , 2004, Theory of Computing Systems.

[9]  Ron O Dror,et al.  The midpoint method for parallelization of particle simulations. , 2006, The Journal of chemical physics.

[10]  P. Kollman,et al.  A Second Generation Force Field for the Simulation of Proteins, Nucleic Acids, and Organic Molecules , 1995 .

[11]  Franck Cappello,et al.  Toward Exascale Resilience , 2009, Int. J. High Perform. Comput. Appl..

[12]  Charles L. Seitz,et al.  Myrinet: A Gigabit-per-Second Local Area Network , 1995, IEEE Micro.

[13]  Klaus Schulten,et al.  GPU-accelerated molecular modeling coming of age. , 2010, Journal of molecular graphics & modelling.

[14]  Philip Heidelberger,et al.  Blue Gene/L torus interconnection network , 2005, IBM J. Res. Dev..

[15]  Daniel Okunbor,et al.  Efficient parallel algorithms for molecular dynamics simulations , 1999, Parallel Comput..

[16]  Jianpeng Ma,et al.  CHARMM: The biomolecular simulation program , 2009, J. Comput. Chem..

[17]  C. Brooks.  Computer simulation of liquids , 1989 .

[18]  Laxmikant V. Kalé,et al.  Scalable molecular dynamics with NAMD , 2005, J. Comput. Chem..

[19]  Steven J. Plimpton,et al.  Parallel Molecular Dynamics Algorithms for Simulation of Molecular Systems , 1995 .

[20]  Roman Trobec,et al.  Implementation and Evaluation of MPI-Based Parallel MD Program , 2001 .

[21]  Joel H. Saltz,et al.  Parallelizing Molecular Dynamics Programs for Distributed Memory Machines: An Application of the Cha , 1994 .

[22]  Catherine H. Gebotys,et al.  Proceedings of the 6th IEEE/ACM/IFIP international conference on Hardware/Software codesign and system synthesis , 2008 .

[23]  B. Brooks,et al.  The effects of truncating long‐range forces on protein dynamics , 1989, Proteins.

[24]  T. Ebisuzaki,et al.  Molecular Dynamics Machine: Special-Purpose Computer for Molecular Dynamics Simulations , 1999 .

[25]  William Gropp,et al.  User''s Guide for mpich , 1996 .

[26]  Bruce Hendrickson,et al.  A new parallel method for molecular dynamics simulation of macromolecular systems , 1996 .

[27]  Bernard R. Brooks,et al.  ENZYME MECHANISMS WITH HYBRID QUANTUM AND MOLECULAR MECHANICAL POTENTIALS.I. THEORETICAL CONSIDERATIONS , 1996 .

[28]  Urban Borštnik.  Parallel Computer Simulations on Clusters of Personal Computers , 2007 .

[29]  Berend Smit,et al.  Understanding molecular simulation: from algorithms to applications , 1996 .

[30]  Dusanka Janezic,et al.  Symplectic Molecular Dynamics Simulations on Specially Designed Parallel Computers , 2005, J. Chem. Inf. Model..

[31]  Piet Hut,et al.  A hierarchical O(N log N) force-calculation algorithm , 1986, Nature.

[32]  David E. Shaw,et al.  A fast, scalable method for the parallel evaluation of distance‐limited pairwise particle interactions , 2005, J. Comput. Chem..