Abstract:

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 »

Abstract:

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])

Abstract:

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])

Abstract:

We fundamentally reconsider implementation of the Fast Multipole Method (FMM) on a computing node with a heterogeneous CPU-GPU architecture with multicore CPU(s) and one or more GPU accelerators, as well as on an interconnected cluster of such nodes. The FMM is a divide-and-conquer algorithm that performs a fast N-body sum using a spatial decomposition and is often used in a time-stepping or iterative loop. Using the observation that the local summation and the analysis-based translation parts of the FMM are independent, we map these respectively to the GPUs and CPUs. Careful analysis of the FMM is performed to distribute work optimally between the multicore CPUs and the GPU accelerators. We first develop a single node version where the CPU part is parallelized using OpenMP and the GPU version via CUDA. New parallel algorithms for creating FMM data structures are presented together with load balancing strategies for the single node and distributed multiple-node versions. Our 8 GPU performance
is comparable with performance of a 256 GPU version of the FMM that won the 2009 Bell prize.

(Qi Hu, Nail A. Gumerov and Ramani Duraswami: “Scalable fast multipole methods on distributed heterogeneous architectures”, accepted for SC’11. [PDF])

Abstract

It is increasingly easy to develop software that exploits Graphics Processing Units (GPUs). The molecular dynamics simulation community has embraced this recent opportunity. Herein, we outline the current approaches that exploit this technology. In the context of biomolecular simulations, we discuss some of the algorithms that have been implemented and some of the aspects that distinguish the GPU from previous parallel environments. The ubiquity of GPUs and the ingenuity of the simulation community augur well for the scale and scope of future computational studies of biomolecules.

(Baker, J. A. and Hirst, J. D.: “Molecular Dynamics Simulations Using Graphics Processing Units”. Molecular Informatics, 30:498–504. [DOI])

GPIUTMD stands for Graphic Processors at Isfahan University of Technology for Many-particle Dynamics. It performs general-purpose many-particle dynamic simulations on a single workstation, taking advantage of NVIDIA GPUs to attain a level of performance equivalent to thousands of cores on a fast cluster. Flexible and configurable, GPIUTMD is currently being used for all atom and coarse-grained molecular dynamics simulations of nano-materials, glasses, and surfactants; dissipative particle dynamics simulations (DPD) of polymers; and crystallization of metals using EAM potentials. GPIUTMD 0.9.6 adds many new features. Highlights include:

Abstract:

The calculation of radial distribution functions (RDFs) from molecular dynamics trajectory data is a common and computationally expensive analysis task. The rate limiting step in the calculation of the RDF is building a histogram of the distance between atom pairs in each trajectory frame. Here we present an implementation of this histogramming scheme for multiple graphics processing units (GPUs). The algorithm features a tiling scheme to maximize the reuse of data at the fastest levels of the GPU’s memory hierarchy and dynamic load balancing to allow high performance on heterogeneous configurations of GPUs. Several versions of the RDF algorithm are presented, utilizing the specific hardware features found on different generations of GPUs. We take advantage of larger shared memory and atomic memory operations available on state-of-the-art GPUs to accelerate the code significantly. The use of atomic memory operations allows the fast, limited-capacity on-chip memory to be used much more efficiently, resulting in a fivefold increase in performance compared to the version of the algorithm without atomic operations. The ultimate version of the algorithm running in parallel on four NVIDIA GeForce GTX 480 (Fermi) GPUs was found to be 92 times faster than a multithreaded implementation running on an Intel Xeon 5550 CPU. On this multi-GPU hardware, the RDF between two selections of 1,000,000 atoms each can be calculated in 26.9 s per frame. The multi-GPU RDF algorithms described here are implemented in VMD, a widely used and freely available software package for molecular dynamics visualization and analysis.

(Benjamin G. Levine, John E. Stone, and Axel Kohlmeyer: “Fast Analysis of Molecular Dynamics Trajectories with Graphics Processing Units — Radial Distribution Function Histogramming”, Journal of Computational Physics, 230(9):3556-3569, 2011. [DOI: 10.1016/j.jcp.2011.01.048])

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 0.9.2 adds many new features. Highlights include:

• Long-ranged electrostatics via PPPM
• Support for CUDA 3.2 and 4.0
• New neighbor list option to exclude by particle diameter (for pair.slj)
• New syntax to specify multiple pair coefficients at once
• Improved documentation
• Significant performance boosts for small simulations
• RPM and .deb packaging for CentOS, Fedora, and Ubuntu
• and more

HOOMD-blue 0.9.2 is available for download under an open source license. Check out the quick start tutorial to get started, or check out the full documentation to see everything it can do.