More information: http://www.ks.uiuc.edu/Research/vmd/vmd-1.9.1/

HOOMD-blue 0.10.0 adds many new features. Highlights include:

• Added pair.dpdlj which uses the DPD thermostat and the Lennard-Jones potential. In previous versions, this could be accomplished by using two pair commands but at the cost of reduced performance.
• Additional example scripts are now present in the documentation. The example scripts are cross-linked to the commands that are used in them.
• Most dump commands now accept the form: dump.ext(filename=”filename.ext”) which immediately writes out filename.ext.
• Specify rigid bodies in XML input files
• Simulations that contain rigid body constraints applied to groups of particles in BDNVT, NVE, NVT, and NPT ensembles.
• Energy minimization of rigid bodies ( integrate.mode_minimize_rigid_fire )
• Existing commands are now rigid-body aware
• NVT integration using the Berendsen thermostat ( integrate.berendsen )
• Bonds, angles, dihedrals, and impropers can now be created and deleted with the python data access API.
• and more

HOOMD-blue 0.10.0 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.

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])

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])

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])

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])

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 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.

1. Constrained by limited computing resources including access to GPGPUs
2. Manually executing the same simulation repeatedly with different parameters
3. Making simulations easier to understand, share, scale and re-use across compute resources

For more information see these two blog posts: High Throughput Parallel Molecular Dynamics and CUDA/Tesla Accelerated PMEMD on OSG. Contact Steve Cox (scox@renci.org) if you’d like to discuss further and determine if your application is a fit. If it is, RENCI can provide access to the grid as well as tools for executing and managing simulations.