Computationally efficient canonical molecular dynamics simulations by using a multiple time‐step integrator algorithm combined with the particle mesh Ewald method and with the fast multipole method

An efficient implementation of the canonical molecular dynamics simulation using the reversible reference system propagator algorithm (r‐RESPA) combined with the particle mesh Ewald method (PMEM) and with the macroscopic expansion of the fast multipole method (MEFMM) was examined. The performance of the calculations was evaluated for systems with 3000, 9999, 30,000, 60,000, and 99,840 particles. For a given accuracy, the optimal conditions for minimizing the CPU time for the implementation of the Ewald method, the PMEM, and the MEFMM were first analyzed. Using the optimal conditions, we evaluated the performance and the reliability of the integrated methods. For all the systems examined, the r‐RESPA with the PMEM was about twice as fast as the r‐RESPA with the MEFMM. The difference arose from the difference in the numerical complexities of the fast Fourier transform in the PMEM and from the transformation of the multipole moments into the coefficients of the local field expansion in the MEFMM. Compared with conventional methods, such as the velocity‐verlet algorithm with the Ewald method, significant speedups were obtained by the integrated methods; the speedup of the calculation was a function of system size, and was a factor of 100 for a system with 3000 particles and increased to a factor of 700 for a system with 99,840 particles. These integrated calculations are, therefore, promising for realizing large‐scale molecular dynamics simulations for complex systems. © 2000 John Wiley & Sons, Inc. J Comput Chem 21: 201–217, 2000

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

[2]  N. Karasawa,et al.  Acceleration of convergence for lattice sums , 1989 .

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

[4]  Piero Procacci,et al.  A Very Fast Molecular Dynamics Method To Simulate Biomolecular Systems with Realistic Electrostatic Interactions , 1996 .

[5]  Mark E. Tuckerman,et al.  Molecular dynamics algorithm for multiple time scales: Systems with long range forces , 1991 .

[6]  G. Martyna,et al.  Electrostatic calculations and multiple time scales in molecular dynamics simulation of flexible molecular systems , 1998 .

[7]  M. Klein,et al.  Nosé-Hoover chains : the canonical ensemble via continuous dynamics , 1992 .

[8]  Andrew W. Appel,et al.  An Efficient Program for Many-Body Simulation , 1983 .

[9]  Darrin M. York,et al.  The fast Fourier Poisson method for calculating Ewald sums , 1994 .

[10]  T. Darden,et al.  A smooth particle mesh Ewald method , 1995 .

[11]  B. Berne,et al.  A new molecular dynamics method combining the reference system propagator algorithm with a fast multipole method for simulating proteins and other complex systems , 1995 .

[12]  G. W. Robinson,et al.  Flexible simple point-charge water in a self-supporting thin film , 1991 .

[13]  K. Esselink A comparison of algorithms for long-range interactions , 1995 .

[14]  Henrik G. Petersen,et al.  Error estimates for the fast multipole method , 1997 .

[15]  K. Schmidt,et al.  Implementing the fast multipole method in three dimensions , 1991 .

[16]  M. Karplus,et al.  SIMULATIONS OF MACROMOLECULES BY MULTIPLE TIME-STEP METHODS , 1995 .

[17]  R W Hockney,et al.  Computer Simulation Using Particles , 1966 .

[18]  T. Darden,et al.  A Multipole-Based Algorithm for Efficient Calculation of Forces and Potentials in Macroscopic Period , 1996 .

[19]  S. Nosé A unified formulation of the constant temperature molecular dynamics methods , 1984 .

[20]  H. G. Petersen,et al.  An algorithm for the simulation of condensed matter which grows as the 3/2 power of the number of particles , 1988 .

[21]  Martin Head-Gordon,et al.  Derivation and efficient implementation of the fast multipole method , 1994 .

[22]  T. Darden,et al.  Particle mesh Ewald: An N⋅log(N) method for Ewald sums in large systems , 1993 .

[23]  Bernard R. Brooks,et al.  New spherical‐cutoff methods for long‐range forces in macromolecular simulation , 1994, J. Comput. Chem..

[24]  Mark E. Tuckerman,et al.  Reversible multiple time scale molecular dynamics , 1992 .

[25]  J. Board,et al.  Ewald summation techniques in perspective: a survey , 1996 .

[26]  W. L. Jorgensen,et al.  Comparison of simple potential functions for simulating liquid water , 1983 .

[27]  M. S. Warren,et al.  A parallel hashed Oct-Tree N-body algorithm , 1993, Supercomputing '93.

[28]  Leslie Greengard,et al.  A fast algorithm for particle simulations , 1987 .

[29]  B. Berne,et al.  Large scale simulation of macromolecules in solution: Combining the periodic fast multipole method with multiple time step integrators , 1997 .

[30]  P. Procacci,et al.  Taming the Ewald sum in molecular dynamics simulations of solvated proteins via a multiple time step algorithm , 1996 .

[31]  William Gropp,et al.  A Parallel Version of the Fast Multipole Method-Invited Talk , 1987, PPSC.

[32]  Hoover,et al.  Canonical dynamics: Equilibrium phase-space distributions. , 1985, Physical review. A, General physics.

[33]  J. Perram,et al.  Cutoff Errors in the Ewald Summation Formulae for Point Charge Systems , 1992 .

[34]  B. Montgomery Pettitt,et al.  Simple intramolecular model potentials for water , 1987 .

[35]  J. L. Hennessy,et al.  A parallel adaptive fast multipole method , 1993, Supercomputing '93.

[36]  H. G. Petersen Accuracy and efficiency of the particle mesh Ewald method , 1995 .

[37]  W. Goddard,et al.  Atomic level simulations on a million particles: The cell multipole method for Coulomb and London nonbond interactions , 1992 .

[38]  Mark E. Tuckerman,et al.  Explicit reversible integrators for extended systems dynamics , 1996 .