Early experiences scaling VMD molecular visualization and analysis jobs on blue waters

Pataskala molecular dynamics simulations provide a powerful tool for probing the dynamics of cellular processes at atomic and nanosecond resolution not achievable by experimental methods alone. Extraction of details about the dynamics of bimolecular from terabytes of simulation output requires powerful user-extensible molecular analysis and visualization tools. Pataskala simulation trajectories are so large that it is now necessary to perform many analysis and visualization tasks formerly handled by off-site computational facilities in-place on the supercomputer itself. We report ongoing progress on porting, tuning, and scaling up the popular molecular visualization and analysis program VMD on the NSF Blue Waters pet scale supercomputer. We describe key achievements including algorithmic and memory efficiency improvements, hand-vectorization of key CPU algorithms, new and improved GPU analysis and visualization algorithms, and parallel I/O performance results. We evaluate the performance of VMD for user-developed analysis scripts with the TIMELINE trajectory analysis tool in VMD. Finally, we describe the unique capabilities provided by the Cray XK7 GPU-accelerated compute partition of Blue Waters.

[1]  Klaus Schulten,et al.  High performance computation and interactive display of molecular orbitals on GPUs and multi-core CPUs , 2009, GPGPU-2.

[2]  John E. Stone,et al.  Lattice microbes: High‐performance stochastic simulation method for the reaction‐diffusion master equation , 2013, J. Comput. Chem..

[3]  John E. Stone,et al.  Long time-scale simulations of in vivo diffusion using GPU hardware , 2009, 2009 IEEE International Symposium on Parallel & Distributed Processing.

[4]  Yao Zhang,et al.  Parallel Computing Experiences with CUDA , 2008, IEEE Micro.

[5]  Robert Sisneros,et al.  Analysis of the Blue Waters File System Architecture for Application I / O Performance , 2013 .

[6]  John E. Stone,et al.  An efficient library for parallel ray tracing and animation , 1998 .

[7]  John E. Stone,et al.  Quantifying the impact of GPUs on performance and energy efficiency in HPC clusters , 2010, International Conference on Green Computing.

[8]  David K. McAllister,et al.  OptiX: a general purpose ray tracing engine , 2010, ACM Trans. Graph..

[9]  Klaus Schulten,et al.  Mature HIV-1 capsid structure by cryo-electron microscopy and all-atom molecular dynamics , 2013, Nature.

[10]  John E. Stone,et al.  Rendering of numerical flow simulations using MPI , 1996, Proceedings. Second MPI Developer's Conference.

[11]  Klaus Schulten,et al.  Accelerating Molecular Modeling Applications with GPU Computing , 2009 .

[12]  J. Berger,et al.  Running in Reverse: The Structural Basis for Translocation Polarity in Hexameric Helicases , 2009, Cell.

[13]  John E. Stone,et al.  Fast analysis of molecular dynamics trajectories with graphics processing units - Radial distribution function histogramming , 2011, J. Comput. Phys..

[14]  John L. Klepeis,et al.  A scalable parallel framework for analyzing terascale molecular dynamics simulation trajectories , 2008, 2008 SC - International Conference for High Performance Computing, Networking, Storage and Analysis.

[15]  Vivek Sarkar,et al.  Phasers: a unified deadlock-free construct for collective and point-to-point synchronization , 2008, ICS '08.

[16]  Ivan S Ufimtsev,et al.  Quantum Chemistry on Graphical Processing Units. 1. Strategies for Two-Electron Integral Evaluation. , 2008, Journal of chemical theory and computation.

[17]  Gregory Bryan Computing in Science and Engineering , 1999, IEEE Software.

[18]  Klaus Schulten,et al.  GPU acceleration of cutoff pair potentials for molecular modeling applications , 2008, CF '08.

[19]  Jeffrey S. Vetter,et al.  Quantifying NUMA and contention effects in multi-GPU systems , 2011, GPGPU-4.

[20]  Klaus Schulten,et al.  Multilevel summation of electrostatic potentials using graphics processing units , 2009, Parallel Comput..

[21]  Klaus Schulten,et al.  GPU-accelerated molecular modeling coming of age. , 2010, Journal of molecular graphics & modelling.

[22]  Klaus Schulten,et al.  Fast Visualization of Gaussian Density Surfaces for Molecular Dynamics and Particle System Trajectories , 2012, EuroVis.

[23]  M J Harvey,et al.  The impact of accelerator processors for high-throughput molecular modeling and simulation. , 2008, Drug discovery today.

[24]  S. Lowen The Biophysical Journal , 1960, Nature.

[25]  K Schulten,et al.  VMD: visual molecular dynamics. , 1996, Journal of molecular graphics.

[26]  Patricia J. Teller,et al.  Proceedings of the 2008 ACM/IEEE conference on Supercomputing , 2008, HiPC 2008.

[27]  Vijay S. Pande,et al.  Accelerating molecular dynamic simulation on graphics processing units , 2009, J. Comput. Chem..

[28]  Klaus Schulten,et al.  Immersive Out-of-Core Visualization of Large-Size and Long-Timescale Molecular Dynamics Trajectories , 2011, ISVC.