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

Reconstruction and Simulation of Neocortical Microcircuitry

The “protein folding problem” consists of three closely related puzzles: (a) What is the folding code? (b) What is the folding mechanism? (c) Can we predict the native structure of a protein from its amino acid sequence? Once regarded as a grand challenge, protein folding has seen great progress in recent years. Now, foldable proteins and nonbiological polymers are being designed routinely and moving toward successful applications. The structures of small proteins are now often well predicted by computer methods. And, there is now a testable explanation for how a protein can fold so quickly: A protein solves its large global optimization problem as a series of smaller local optimization problems, growing and assembling the native structure from peptide fragments, local structures first.

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The Protein Folding Problem

In this work we demonstrate the use of a rigorous formalism for the extraction of state-to-state transition functions as a way to study the kinetics of protein folding in the context of a Markov ch...

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Describing Protein Folding Kinetics by Molecular Dynamics Simulations. 2. Example Applications to Alanine Dipeptide and a β-Hairpin Peptide†

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

We describe the design of a dual-issue single-instruction, multiple-data-like (SIMD-like) extension of the IBM PowerPC® 440 floating-point unit (FPU) core and the compiler and algorithmic techniques to exploit it. This extended FPU is targeted at both the IBM massively parallel Blue Gene®/L machine and the more pervasive embedded platforms. We discuss the hardware and software codesign that was essential in order to fully realize the performance benefits of the FPU when constrained by the memory bandwidth limitations and high penalties for misaligned data access imposed by the memory hierarchy on a Blue Gene/L node. Using both hand-optimized and compiled code for key linear algebraic kernels, we validate the architectural design choices, evaluate the success of the compiler, and quantify the effectiveness of the novel algorithm design techniques. Our measurements show that the combination of algorithm, compiler, and hardware delivers a significant fraction of peak floating-point performance for compute-bound-kernels, such as matrix multiplication, and delivers a significant fraction of peak memory bandwidth for memorybound kernels, such as DAXPY, while remaining largely insensitive to data alignment.

Design and exploitation of a high-performance SIMD floating-point unit for Blue Gene/L

An asynchronous data channel carries an asynchronous signal (30) in which fields (211) of digital data, which are asynchronous within the field at a bit rate frequency, are separated by adjustment regions including a first pattern of control signals (213, 214) repeating at a first submultiple of the bit rate frequency and asecond pattern of control signals (215) repeating at a second submultiple of the bit rate frequency. First and second clock signals are derived at the two submultiple frequencies and phase coincidence between the two clock signals is detected. A synchronizing signal, produced in fixed time relationship with the coincidence detection indicates the start of the data field. The data channel can also include formatting means (41) and write circuitry for generating the appropriate control signal patterns prior to the data field and can be applied to an information storage subsystem of the moving medium type.

T. J. C. Ward

Papers

Describing Protein Folding Kinetics by Molecular Dynamics Simulations. 2. Example Applications to Alanine Dipeptide and a β-Hairpin Peptide†

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

Design and exploitation of a high-performance SIMD floating-point unit for Blue Gene/L

Asynchronous data channel for information storage subsystem