Simbios, the NIH Center for Biomedical Computation at Stanford University, is excited to announce the release of OPENMM 2.0.

OPENMM was designed to enhance the performance of almost any molecular dynamics simulation package (MD package) by allowing the code to be executed on high performance computer architectures, in particular Graphics Processing Units (GPUs). Most molecular dynamics packages can be modified to call OPENMM, resulting in significant acceleration on such high performance architectures, without changing the way users interact with the MD package. Read the rest of this entry »

Petapath, NVIDIA and Supermicro would like to invite researchers, students and industrial users to a series of free seminars and workshops dedicated to the Bio Workbench. The seminars will principally cover the use of AMBER 11′s CUDA-accelerated PMEMD (Particle Mesh Ewald Molecular Dynamics) tool but will be of interest to anyone using other molecular dynamics packages covered by the Bio Workbench.

Guest speakers include Ross Walker (SDSC) and Ian Gould (UCL) and currently there are two events being held in the UK, the 8th of July at Imperial College London and the 16th of July at The University of Manchester. Please visit www.petapath.com/nvidia to register.

GPU-Accelerated Ion Placement

The Theoretical and Computational Biophysics Group, NIH Resource for Macromolecular Modeling and Bioinformatics (www.ks.uiuc.edu) at the University of Illinois at Urbana-Champaign, presents a Workshop on GPU Programming for Molecular Modeling to be held August 6-8, 2010, at the Beckman Institute for Advanced Science and Technology, on the University of Illinois campus in Urbana, Illinois, USA. Application, selection, and notification of participants is on-going through July 29, 2010.

Note: Participants are encouraged to attend the multi-site “Proven Algorithmic Techniques for Many-core Processors” workshop the preceding week (August 2-6) at the location of their choice. Registration for this workshop is required for participants without equivalent GPU-programming training or experience.

HOOMD-blue stands for Highly Optimized Object-oriented Many-particle Dynamics — Blue Edition. It performs general-purpose particle dynamics simulations on a single workstation, taking advantage of  NVIDIA GPUs to attain a level of performance equivalent to dozens of processor cores on a fast cluster.

HOOMD-blue 0.9.0 is a major new release. Highlights include:

• Support for Fermi generation GPUs
• Performance enhancements
• New pair potentials
• Particle data is now accessible from hoomd scripts
• Binary format dump files for simulation restarts
• Numerous small enhancements to enable easily restartable jobs
• 2D simulations are now possible
• Integration methods can now be applied to specified groups of particles
• All IMD commands issued by VMD are now understood
• … and more

HOOMD-blue 0.9.0 is available for download under an open source license.

Molecular Workshop Series – Running and Developing MD Algorithms on GPUs with OpenMM and PyOpenMM + Intro to MD and Trajectory Analysis

Simbios is excited to announce its upcoming Molecular Dynamics (MD) Workshop Series, highlighting new capabilities within the recently released OpenMM 1.0 and introducing PyOpenMM for rapid MD code development with high performance:

Day 1: Running and Developing MD Algorithms on GPUs with OpenMM
Day 2: Introduction to MD and Trajectory Analysis with Markov State Models

When: March 1-2, 2010 (sign up for one or two days)
Where: Stanford University

Registration is free but required and spaces are limited. Please visit http://simbios.stanford.edu/MDWorkshops.htm for the workshop agenda and to register.

Occasionally, we receive news submissions pointing us to interesting older papers that somehow slipped by without our notice. This post collects a few of those. If you want your work to be posted on GPGPU.org  in a timely manner, please remember to use the news submission form.

• Joshua A. Anderson, Chris D. Lorenz and Alex Travesset present and discuss molecular dynamics simulations and compare a single GPU against a 36-CPU cluster (General purpose molecular dynamics simulations fully implemented on graphics processing units, Journal of Computational Physics 227(10), May 2008, DOI 10.1016/j.jcp.2008.01.047).
• Wen-mei Hwu et al. derive and discuss goals and concepts of programming models for fine-grained parallel architectures, from the point of view of both a programmer and a hardware /compiler designer, and analyze CUDA as one current representative  (Implicitly parallel programming models for thousand-core microprocessors, Proceedings of DAC’07, June 2007, DOI 10.1145/1278480.1278669).
• Jeremy Sugerman et al. present GRAMPS, a prototype implementation of future graphics hardware that allows pipelines to be specified as graphs in software (GRAMPS: A Programming Model for Graphics Pipelines, ACM Transactions on Graphics 28(1), January 2009, DOI 10.1145/1477926.1477930).
• William R. Mark discusses concepts of future graphics architectures in this contribution to the 2008 ACM Queue special issue on GPUs (Future graphics architectures, ACM Queue 6(2), March/April 2008,  DOI 10.1145/1365490.1365501).
• BSGP by Qiming Hou et al. is a new programming language for general purpose GPU computing that achieves the same efficiency as well-tuned CUDA programs but makes code much easier to read, develop and maintain (BSGP: bulk-synchronous GPU programming, ACM Siggraph 2008, August 2008, DOI 10.1145/1399504.1360618).
• Finally, Che et al. and Garland et al. survey the field of GPU computing and discuss many different application domains. These articles are, in addition to the ones we have collected on the developer pages, recommended to GPGPU newcomers.

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.