The 1.0 Beta version of OpenMM has just been released.  OpenMM is a freely downloadable, high performance, extensible library that allows molecular dynamics (MD) simulations to run on high performance computer architectures, such as graphics processing units (GPUs). It currently supports NVIDIA GPUs and provides preliminary support for the new cross-platform, parallel programming standard OpenCL, which will enable it to be used on ATI GPUs.

The new release includes support for Particle Mesh Ewald and custom non-bonded interactions.  In conjunction with this release, a new version of the code needed for accelerating the GROMACS molecular dynamics software using OpenMM is also available.

OpenMM is a collaborative project between Vijay Pande’s lab at Stanford University and Simbios, the National Center for Physics-based Simulation of Biological Structures at Stanford, which is supported by the National Institutes of Health. For more information on OpenMM, visit http://simtk.org/home/openmm.

This article describes computational challenges involved in the study of biomolecular complexes, and relates some of the authors’ early experiences using GPUs to accelerate computationally demanding biomolecular modeling and simulation tasks.  The article reviews a number of early successes in the application of GPUs to molecular modeling and touches on future challenges in this rapidly developing area of science and technology.  The article is written to be readable by a fairly general audience. (
Probing Biomolecular Machines with Graphics Processors. James C. Phillips, John E. Stone. Communications of the ACM 52(10):34-41, 2009.)

OpenMM is an open-source library that enables molecular dynamics (MD) simulations to be accelerated on high performance computer architectures, such as GPUs. This latest release adds support for:

• A complete set of C and Fortran wrappers
• Energy computations on GPUs
• Ewald summation
• A faster algorithm for handling constraints
• And more!

Download the latest version of OpenMM from http://simtk.org/home/openmm.

VMD is a molecular visualization program for building, displaying, and analyzing large biomolecular systems using 3-D graphics and built-in scripting. One of the key advancements included in VMD 1.8.7 is support for GPU-accelerated visualization and analysis, based on CUDA. VMD uses CUDA to accelerate several of its most computationally demanding algorithms, with additional modules planned for GPU acceleration in upcoming releases. Typical GPU acceleration factors for the algorithms in VMD are: electrostatics 22x to 44x, implicit ligand sampling 20x to 30x, molecular orbital calculation 100x to 120x.

ACEMD is a production-class bio-molecular dynamics (MD) simulation program designed specifically for GPUs which is able to achieve supercomputing scale performance of 40 nanoseconds /day for all-atom protein systems with over 23,000 atoms.  With GPU technology it has become possible to run a microsecond-long trajectory for an all-atom molecular system in explicit water on a single workstation computer equipped with just 3 GPUs. This performance would have required over 100 CPU cores.  Visit the project website for details.

(M. J. Harvey, G. Giupponi, G. De Fabritiis, ACEMD: Accelerating bio-molecular dynamics in the microsecond time-scale. Link to preprint.)

OpenMM is a freely downloadable, high performance, extensible library that allows molecular dynamics (MD) simulations to run on high performance computer architectures, such as graphics processing units (GPUs). Significant performance speedups of 100 times were achieved in some cases by running OpenMM on GPUs in desktop PCs (vs CPU). The new release includes a version of the widely used MD package GROMACS that integrates the OpenMM library, enabling acceleration on high-end NVIDIA and AMD/ATI GPUs. OpenMM is a collaborative project between Vijay Pande’s lab at Stanford University and Simbios, the National Center for Physics-based Simulation of Biological Structures at Stanford, which is supported by the National Institutes of Health. For more information on OpenMM, go to http://simtk.org/home/openmm. (Full press release.)

Graphics processing units (GPUs) have become an attractive option for accelerating scientific computations as a result of advances in the performance and flexibility of GPU hardware, and due to the availability of GPU software development tools targeting general purpose and scientific computation. However, effective use of GPUs in clusters presents a number of application development and system integration challenges. We describe strategies for the decomposition and scheduling of computation among CPU cores and GPUs, and techniques for overlapping communication and CPU computation with GPU kernel execution. We report the adaptation of these techniques to NAMD, a widely-used parallel molecular dynamics simulation package, and present performance results for a 64-core 64-GPU cluster. (Adapting a message-driven parallel application to GPU-accelerated clusters. James C. Phillips, John E. Stone, and Klaus Schulten. In Proceedings of the 2008 ACM/IEEE conference on Supercomputing. Research web site)

The computer-aided prediction of protein-ligand complex conformations, i.e. docking a small ligand into the active site of a protein, is an important application in the early stages of the modern drug discovery process. For this problem a new approach called PLANTS (Protein-Ligand ANT System) is presented which is based on Ant Colony Optimization (ACO). Part of the work deals with the acceleration of this approach by moving the most time-consuming steps, the transformation of the protein and ligand structure and the evaluation of the objective function, to the GPU. The combined CPU-GPU approach is able to reach a speedup of 5 on average when comparing an optimized CPU-version (single core of a dual-core Pentium 4, 3 GHz) with the GPU-accelerated version (Nvidia Geforce 8800 GTX). Especially virtual screening applications, where the complex conformations of thousands to millions of ligands need to be predicted, can benefit from this speedup.

(Efficient Ant Colony Optimization Algorithms for Structure- and Ligand-Based Drug Design.  Oliver Korb, PhD thesis, University of Konstanz, 2008)

The advent of systems biology requires the simulation of ever-larger biomolecular systems, demanding a commensurate growth in computational power. This paper examines the use of the NVIDIA Tesla C870 graphics card programmed through the CUDA toolkit to accelerate the calculation of cutoff pair potentials, one of the most prevalent computations required by many different molecular modeling applications. The paper presents algorithms to calculate electrostatic potential maps for cutoff pair potentials. Whereas a straightforward approach for decomposing atom data leads to low computational efficiency, a new strategy enables fine-grained spatial decomposition of atom data that maps efficiently to the C870′s memory system while increasing work efficiency of atom data traversal by a factor of 5. The memory addressing flexibility exposed through CUDA’s SPMD programming model is crucial in enabling this new strategy. An implementation of the new algorithm provides a greater than threefold performance improvement over our previously published implementation and runs 12 to 20 times faster than optimized CPU-only code. The lessons learned are generally applicable to algorithms accelerated by uniform grid spatial decomposition. (C. I. Rodrigues, D. J. Hardy, J. E. Stone, K. Schulten, W. W. Hwu., GPU acceleration of cutoff pair potentials for molecular modeling applications. Proceedings of the 2008 Conference On Computing Frontiers, pp.273-282, 2008.) (http://www.ks.uiuc.edu/Research/gpu/)

This article at Genome Technology gives a brief overview of GPGPU, with a focus on biological information processing using NVIDIA CUDA Technology. The article discusses the results from UIUC’s NAMD / VMD project and neurological simulation company Evolved Machines.