The GPU Programming for Molecular Modeling workshop will extend GPU programming techniques to the field of molecular modeling, including subjects such as particle-grid algorithms (electrostatics, molecular surfaces, density maps, and molecular orbitals), particle-particle algorithms with an emphasis on non-bonded force calculations, radial distribution functions in GPU histogramming, single-node multi-GPU algorithms, and GPU clusters. Specific examples utilizing the NAMD and VMD software programs will be introduced and discussed in detail. The workshop is designed for researchers in computational and/or biophysical fields who seek to extend their GPU programming skills to include molecular modeling. Advanced lecture sessions will be followed by extended discussion periods between lecturers and participants and laboratory time in which students will be able to work on their own molecular modeling GPU codes. See workshop website for details and application: http://www.ks.uiuc.edu/Training/Workshop/GPU_Jul2014/
GPU-Accelerated Analysis and Visualization of Large Structures Solved by Molecular Dynamics Flexible FittingMarch 26th, 2014
Hybrid structure fitting methods combine data from cryo-electron microscopy and X-ray crystallography with molecular dynamics simulations for the determination of all-atom structures of large biomolecular complexes. Evaluating the quality-of-fit obtained from hybrid fitting is computationally demanding, particularly in the context of a multiplicity of structural conformations that must be evaluated. Existing tools for quality-of-fit analysis and visualization have previously targeted small structures and are too slow to be used interactively for large biomolecular complexes of particular interest today such as viruses or for long molecular dynamics trajectories as they arise in protein folding. We present new data-parallel and GPU-accelerated algorithms for rapid interactive computation of quality-of-fit metrics linking all-atom structures and molecular dynamics trajectories to experimentally-determined density maps obtained from cryo-electron microscopy or X-ray crystallography. We evaluate the performance and accuracy of the new quality-of-fit analysis algorithms vis-a-vis existing tools, examine algorithm performance on GPU-accelerated desktop workstations and supercomputers, and describe new visualization techniques for results of hybrid structure fitting methods.
(John E. Stone, Ryan McGreevy, Barry Isralewitz, and Klaus Schulten: “GPU-Accelerated Analysis and Visualization of Large Structures Solved by Molecular Dynamics Flexible Fitting”. Faraday Discussion 169, 2014. [DOI])
Petascale supercomputers create new opportunities for the study of the structure and function of large biomolecular complexes such as viruses and photosynthetic organelles, permitting all-atom molecular dynamics simulations of tens to hundreds of millions of atoms. Together with simulation and analysis, visualization provides researchers with a powerful “computational microscope”. Petascale molecular dynamics simulations produce tens to hundreds of terabytes of data that can be impractical to transfer to remote facilities, making it necessary to perform visualization and analysis tasks in-place on the supercomputer where the data are generated. We describe the adaptation of key visualization features of VMD, a widely used molecular visualization and analysis tool, for GPU-accelerated petascale computers. We discuss early experiences adapting ray tracing algorithms for GPUs, and compare rendering performance for recent petascale molecular simulation test cases on Cray XE6 (CPU-only) and XK7 (GPU-accelerated) compute nodes. Finally, we highlight opportunities for further algorithmic improvements and optimizations.
(John E. Stone, Kirby L. Vandivort, and Klaus Schulten: “GPU-Accelerated Molecular Visualization on Petascale Supercomputing Platforms”. UltraVis’13: Proceedings of the 8th International Workshop on Ultrascale Visualization, pp. 6:1-6:8, 2013. [DOI])
This webinar will demonstrate how real-world computational research in soft matter physics can be accelerated on a GPU-equipped desktop computer with the HOOMD-blue molecular dynamics software. A presentation of how to set up a simulation of a dense polymer liquid, and how to analyze and visualize the results is provided. There will be a demonstration of how self-assembled ordered structures of block copolymers emerge out of an initially disordered configuration. With external potentials, an artificially ordered phase can be produced as well. HOOMD-blue’s easy-to-use scripting interface and plug-ins are used to create a productive work-flow and extend its capabilities. As an advanced topic, there will be a discussion of how the upcoming version of HOOMD-blue can be used on compute clusters running on ten to hundreds of GPUs in parallel, to boost simulations of long polymer chains or large-scale systems.
January 21, 2014, 11:00 a.m. EST, Registration required.
The Virtual School of Computational Science and Engineering is hosting two upcoming webinars.
- Introduction to HOOMD-blue, December 10, 2013, 11:00 EST.
- Using HOOMD-blue for Polymer Simulations and Big Systems, January 21, 2014, 11:00 EST.
More information and registration: http://www.vscse.org/
Fast Visualization of Gaussian Density Surfaces for Molecular Dynamics and Particle System TrajectoriesAugust 1st, 2012
We present an efficient algorithm for computation of surface representations enabling interactive visualization of large dynamic particle data sets. Our method is based on a GPU-accelerated data-parallel algorithm for computing a volumetric density map from Gaussian weighted particles. The algorithm extracts an isovalue surface from the computed density map, using fast GPU-accelerated Marching Cubes. This approach enables interactive frame rates for molecular dynamics simulations consisting of millions of atoms. The user can interactively adjust the display of structural detail on a continuous scale, ranging from atomic detail for in-depth analysis, to reduced detail visual representations suitable for viewing the overall architecture of molecular complexes. The extracted surface is useful for interactive visualization, and provides a basis for structure analysis methods.
(Michael Krone, John E. Stone, Thomas Ertl, and Klaus Schulten, “Fast visualization of Gaussian density surfaces for molecular dynamics and particle system trajectories”, In EuroVis – Short Papers 2012, pp. 67-71, 2012. [WWW])
VMD is a popular molecular visualization and analysis program used by thousands of researchers worldwide. VMD accelerates many of the most computationally demanding visualization and analysis features using GPU computing techqniques, resulting in improved performance and new capabilities beyond what is possible using only conventional multi-core CPUs. VMD 1.9.1 advances these capabilities further with a CUDA implementation of the new QuickSurf molecular surface representation, enabling smooth interactive animation of moderate sized biomolecular complexes consisting of a few hundred thousand to one million atoms, and allowing interactive display of molecular surfaces for static structures of very large complexes containing tens of millions of atoms, e.g. large virus capsids.
More information: http://www.ks.uiuc.edu/Research/vmd/vmd-1.9.1/
HOOMD-blue performs general-purpose particle dynamics simulations on a single workstation, taking advantage of NVIDIA GPUs to attain a level of performance equivalent to many cores on a fast cluster. Flexible and configurable, HOOMD-blue is currently being used for coarse-grained molecular dynamics simulations of nano-materials, glasses, and surfactants, dissipative particle dynamics simulations (DPD) of polymers, and crystallization of metals.
HOOMD-blue 0.10.0 adds many new features. Highlights include: Read the rest of this entry »
Rigid body constraints realized in massively-parallel molecular dynamics on graphics processing unitsAugust 20th, 2011
Molecular dynamics (MD) methods compute the trajectory of a system of point particles in response to a potential function by numerically integrating Newton’s equations of motion. Extending these basic methods with rigid body constraints enables composite particles with complex shapes such as anisotropic nanoparticles, grains, molecules, and rigid proteins to be modeled. Rigid body constraints are added to the GPU-accelerated MD package, HOOMD-blue, version 0.10.0. The software can now simulate systems of particles, rigid bodies, or mixed systems in microcanonical (NVE), canonical (NVT), and isothermalisobaric (NPT) ensembles. It can also apply the FIRE energy minimization technique to these systems. In this paper, we detail the massively parallel scheme that implements these algorithms and discuss how our design is tuned for the maximum possible performance. Two different case studies are included to demonstrate the performance attained, patchy spheres and tethered nanorods. In typical cases, HOOMD-blue on a single GTX 480 executes 2.5–3.6 times faster than LAMMPS executing the same simulation on any number of CPU cores in parallel. Simulations with rigid bodies may now be run with larger systems and for longer time scales on a single workstation than was previously even possible on large clusters.
(Trung Dac Nguyen, Carolyn L. Phillips, Joshua A. Anderson, and Sharon C. Glotzer: “Rigid body constraints realized in massively-parallel molecular dynamics on graphics processing units”, Computer Physics Communications 182(11):2307–2313, November 2011. [DOI])
Pseudo-random number generation for Brownian Dynamics and Dissipative Particle Dynamics simulations on GPU devicesAugust 20th, 2011
Brownian Dynamics (BD), also known as Langevin Dynamics, and Dissipative Particle Dynamics (DPD) are implicit solvent methods commonly used in models of soft matter and biomolecular systems. The interaction of the numerous solvent particles with larger particles is coarse-grained as a Langevin thermostat is applied to individual particles or to particle pairs. The Langevin thermostat requires a pseudo-random number generator (PRNG) to generate the stochastic force applied to each particle or pair of neighboring particles during each time step in the integration of Newton’s equations of motion. In a Single-Instruction-Multiple-Thread (SIMT) GPU parallel computing environment, small batches of random numbers must be generated over thousands of threads and millions of kernel calls. In this communication we introduce a one-PRNG-per-kernel-call-per-thread scheme, in which a micro-stream of pseudorandom numbers is generated in each thread and kernel call. These high quality, statistically robust micro-streams require no global memory for state storage, are more computationally efficient than other PRNG schemes in memory-bound kernels, and uniquely enable the DPD simulation method without requiring communication between threads.
(Carolyn L. Phillips, Joshua A. Anderson and Sharon C. Glotzer: “Dynamics and Dissipative Particle Dynamics simulations on GPU devices”, Journal of Computational Physics 230(19):7191-7201, August 2011. [DOI])