Three parallel algorithms for classical molecular dynamics are presented. The first assigns each processor a fixed subset of atoms; the second assigns each a fixed subset of inter-atomic forces to compute; the third assigns each a fixed spatial region. The algorithms are suitable for molecular dynamics models which can be difficult to parallelize efficiently—those with short-range forces where the neighbors of each atom change rapidly. They can be implemented on any distributed-memory parallel machine which allows for message-passing of data between independently executing processors. The algorithms are tested on a standard Lennard-Jones benchmark problem for system sizes ranging from 500 to 100,000,000 atoms on several parallel supercomputers--the nCUBE 2, Intel iPSC/860 and Paragon, and Cray T3D. Comparing the results to the fastest reported vectorized Cray Y-MP and C90 algorithm shows that the current generation of parallel machines is competitive with conventional vector supercomputers even for small problems. For large problems, the spatial algorithm achieves parallel efficiencies of 90% and a 1840-node Intel Paragon performs up to 165 faster than a single Cray C9O processor. Trade-offs between the three algorithms and guidelines for adapting them to more complex molecular dynamics simulations are also discussed.

Fast parallel algorithms for short-range molecular dynamics

Molecular simulation is an extremely useful, but computationally very expensive tool for studies of chemical and biomolecular systems Here, we present a new implementation of our molecular simulation toolkit GROMACS which now both achieves extremely high performance on single processors from algorithmic optimizations and hand-coded routines and simultaneously scales very well on parallel machines The code encompasses a minimal-communication domain decomposition algorithm, full dynamic load balancing, a state-of-the-art parallel constraint solver, and efficient virtual site algorithms that allow removal of hydrogen atom degrees of freedom to enable integration time steps up to 5 fs for atomistic simulations also in parallel To improve the scaling properties of the common particle mesh Ewald electrostatics algorithms, we have in addition used a Multiple-Program, Multiple-Data approach, with separate node domains responsible for direct and reciprocal space interactions Not only does this combination of a

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GROMACS 4: Algorithms for highly efficient, load-balanced, and scalable molecular simulation

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 stateof- 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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Scalable algorithms for molecular dynamics simulations on commodity clusters

The ability to perform long, accurate molecular dynamics (MD) simulations involving proteins and other biological macro-molecules could in principle provide answers to some of the most important currently outstanding questions in the fields of biology, chemistry, and medicine. A wide range of biologically interesting phenomena, however, occur over timescales on the order of a millisecond---several orders of magnitude beyond the duration of the longest current MD simulations. We describe a massively parallel machine called Anton, which should be capable of executing millisecond-scale classical MD simulations of such biomolecular systems. The machine, which is scheduled for completion by the end of 2008, is based on 512 identical MD-specific ASICs that interact in a tightly coupled manner using a specialized highspeed communication network. Anton has been designed to use both novel parallel algorithms and special-purpose logic to dramatically accelerate those calculations that dominate the time required for a typical MD simulation. The remainder of the simulation algorithm is executed by a programmable portion of each chip that achieves a substantial degree of parallelism while preserving the flexibility necessary to accommodate anticipated advances in physical models and simulation methods.

https://dl.acm.org/doi/pdf/10.1145/1364782.1364802

Anton, a special-purpose machine for molecular dynamics simulation

Long-timescale molecular dynamics simulations of protein structure and function.

Blue Matter, an application framework for molecular simulation on blue gene

This paper presents results on a communications-intensive kernel, the three-dimensional fast Fourier transform (3D FFT), running on the 2,048-node Blue Gene®/L (BG/L) prototype. Two implementations of the volumetric FFT algorithm were characterized, one built on the Message Passing Interface library and another built on an active packet Application Program Interface supported by the hardware bring-up environment, the BG/L advanced diagnostics environment. Preliminary performance experiments on the BG/L prototype indicate that both of our implementations scale well up to 1,024 nodes for 3D FFTs of size 128 × 128 × 128. The performance of the volumetric FFT is also compared with that of the Fastest Fourier Transform in the West (FFTW) library. In general, the volumetric FFT outperforms a port of the FFTW Version 2.1.5 library on large-node-count partitions.

Scalable framework for 3D FFTs on the Blue Gene/L supercomputer: implementation and early performance measurements

Blue Matter is the application framework being developed in conjunction with the scientific portion of the IBM Blue Gene® project. We describe the parallel decomposition currently being used to target the Blue Gene/L machine and discuss the application-based trace tools used to analyze the performance of the application. We also present the results of early performance studies, including a comparison of the performance of the Ewald and the particle-particle particle-mesh (P3ME) methods, compare the measured performance of some key collective operations with the limitations imposed by the hardware, and discuss some future directions for research.

Early performance data on the blue matter molecular simulation framework

N-body simulations present some of the most interesting challenges in the area of massively parallel computing, especially when the object is to improve the time to solution for a fixed-size problem. The Blue Matter molecular simulation framework was developed specifically to address these challenges, to explore programming models for massively parallel machine architectures in a concrete context, and to support the scientific goals of the IBM Blue Gene® Project. This paper reviews the key issues involved in achieving ultrastrong scaling of methodologically correct biomolecular simulations, particularly the treatment of the long-range electrostatic forces present in simulations of proteins in water and membranes. Blue Matter computes these forces using the particle-particle particle-mesh Ewald (P3ME) method, which breaks the problem up into two pieces, one that requires the use of three-dimensional fast Fourier transforms with global data dependencies and another that involves computing interactions between pairs of particles within a cutoff distance. We summarize our exploration of the parallel decompositions used to compute these finite-ranged interactions, describe some of the implementation details involved in these decompositions, and present the evolution of strong-scaling performance achieved over the course of this exploration, along with evidence for the quality of simulation achieved.

Blue matter: scaling of N-body simulations to one atom per node

The replica exchange method is a popular approach for studying the folding thermodynamics of small to modest size proteins in explicit solvent, since it is easily parallelized. However, replica exchange can become computationally expensive for large-scale studies, due to the number of replicas needed as well as interprocess or communication requirements both between and within replicas. In this paper, we discuss an implementation of replica exchange molecular dynamics on Blue Gene/L for performing large scale simulation studies of systems of biological interest. The algorithm is tuned with an awareness of the physical network topology and hardware performance features of the Blue Gene/L architecture. Performance measurements for replica exchange using the blue matter molecular dynamics application are presented on Blue Gene/L hardware with up to 256 replicas simulated on 8,192 compute nodes. Both scalability and performance are achieved with this implementation.

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R. S. Germain

Papers

Blue Matter, an application framework for molecular simulation on blue gene

Scalable framework for 3D FFTs on the Blue Gene/L supercomputer: implementation and early performance measurements

Early performance data on the blue matter molecular simulation framework

Blue matter: scaling of N-body simulations to one atom per node

Parallel implementation of the replica exchange molecular dynamics algorithm on Blue Gene/L