Tag: distributed computing
Sparse matrix-vector multiplication on GPGPU clusters
Sparse matrix-vector multiplication (spMVM) is the dominant operation in many sparse solvers. We investigate performance properties of spMVM with matrices of various sparsity patterns on the nVidia “Fermi” class of GPGPUs. A new “padded jagged diagonals storage” (pJDS) format is proposed which may substantially reduce the memory overhead intrinsic to the widespread ELLPACK-R scheme. Inour…
U.S. Department of Energy Magellan project report: Role of cloud computing in science
The goal of Magellan, a project funded through ASCR, was to investigate the potential role of cloud computing in addressing the ever growing computational needs of Science, particularly in midrange computing and data-intensive computing workloads.
Fast online triggering in high-energy physics experiments using GPUs
We discuss an approach for using commercial graphic processors (GPUs) at the earliest trigger stages in high-energy physics experiments, and study its implementation on a real trigger system in preparation.
Integrated tools for molecular dynamics simulation data analysis in the MolDynGrid virtual laboratory
MolDynGrid virtual laboratory has grown into the unique service complex for performing MD simulations in Grid environment. It provides powerful facilities for running the resource-intensive biomolecular simulations and performing state-of-the-art analysis of MD trajectories.
Monte Carlo simulation of photon migration in a cloud computing environment with MapReduce
The purpose of this work is to report on our implementation of a simple MapReduce method for performing fault-tolerant Monte Carlo computations in a massively-parallel cloud computing environment.
Complete reconstruction of an enzyme-inhibitor binding process by molecular dynamics simulations
In this work authors present a kinetic model for the binding process of serine protease β-trypsin inhibitor benzamidine obtained from extensive high-throughput all-atom MD simulations of free ligand binding using the ACEMD (GPU accelerated biomolecular dynamics) software on the GPUGRID.net distributed computing network.
Folding@home: Petaflops Today, Exaflops Soon?
In this video, Vijay Pande from Stanford University presents: Folding@home: Petaflops Today, Exaflops Soon? Recorded at the HPC Advisory Council Stanford Workshop on Dec. 6, 2011
Proceedings of 2011 International Conference for High Performance Computing (SC11)
Table of Contents of 2011 International Conference for High Performance Computing, Networking, Storage and Analysis Proceedings (SC11)
Copernicus: a new paradigm for parallel adaptive molecular dynamics
We have shown that the Copernicus framework effectively increases the scale at which biomolecular molecular dynamics simulations can be performed from a few hundred cores to many thousands and beyond, likely reaching millions of cores for large molecules.
Supporting GPU sharing in cloud environments with a transparent runtime consolidation framework
This paper presents a framework to enable applications executing within virtual machines to transparently share one or more GPUs. Our contributions are twofold: we extend an open source GPU virtualization software to include efficient GPU sharing, and we propose solutions to the conceptual problem of GPU kernel consolidation.
