Welcome to SciGPU.org!
This is a website for an emerging community whose shared goal is harnessing the power of general-purpose programming of graphics processing units to accelerate data-intensive science. The Harvard-based SciGPU community shares knowledge through the site and informal seminars, as well as formal collaborations and publications.
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By mark
Our recent paper, entitled, `Accelerating correlated quantum chemistry calculations using graphical processing units’, by Mark A. Watson, Roberto Olivares-Amaya, Richard G. Edgar, Tomas Arias, and Alan Aspuru-Guzik, has been accepted for the special issue of Computing in Science and Engineering, `SI:Jul/Aug 2010 – High Performance Computing with Accelerators’.
Our manuscript pre-print is available here.
By admin
Our new article, Accelerating Correlated Quantum Chemistry Calculations Using Graphical Processing Units and a Mixed Precision Matrix Multiplication Library,” by Roberto Olivares-Amaya, Mark A. Watson, Richard G. Edgar, Leslie Vogt, Yihan Shao and Alan Aspuru-Guzik, is now available online at the JCTC website:
http://pubs.acs.org/doi/abs/10.1021/ct900543q
Abstract
Two new tools for the acceleration of computational chemistry codes using [click on link for more...]
By admin
scigpugemm0.8 – a tarball of the v0.8 release of the SciGPU-GEMM library.
Matrix-matrix multiplications are common in quantum chemistry calculations, and can benefit enormously from GPU acceleration. Although NVIDIA provides an implementation of the BLAS *GEMM routines with its CUDA distribution, two key problems exist when trying to use these from existing code
Most GPUs in current use have limited [click on link for more...]
By admin
Taxol speedup
In our recently submitted paper (R. Olivares-Amaya et al, JCTC), the Alan Aspuru-Guzik group has presented a new implementation of the quantum chemistry method RI-MP2 (resolution-of-the-identity second-order Møller-Plesset perturbation theory) accelerated using GPUs and the MGEMM library published on this website. For the 168-atom valinomycin molecule in a cc-pVDZ basis set, we observed [click on link for more...]
By admin
This is presentation I gave in Tokyo outlining the use of mixed-precision approaches to accelerating linear algebra in correlated quantum chemistry calculations using GPUs.
HaRIKEN is a collaboration between Harvard University and the RIKEN National Laboratory near Tokyo, Japan.
Download slides [1.49 MB]: 20090907_RIKEN
By admin
A popular account of the SciGPU project has been posted online by the Harvard News Office.
Writer Alvin Powell describes the “trio of projects at Harvard whose massive computing needs have prompted investigators to join forces to pioneer new computing techniques that will benefit not just radio astronomy, but quantum chemistry and neuroscience as well.”
In interviews [click on link for more...]
By admin
The SciGPU collaborators welcomed four students who came to Harvard for NSF-funded Research Experiences for Undergraduates during summer 2009: Dominik Gothe (University of South Carolina; astronomy), Matthias Lee (Wentworth Institute; time series analysis), Beatrice Perez (University of Puerto Rico; quantum chemistry), and Bo Wang (University of Pittsburgh; neuroscience). The SciGPU REU students were among [click on link for more...]
By admin
Our four summer Research Experience for Undergraduates (REU) students (Dominik Gothe, Matthias Lee, Beatrice Perez and Bo Wang) recently gave excellent presentations on the research they conducted this summer. Their work covered the Murchison Widefield Array, Quantum Chemistry, the Connectome and the Time Series Centre. Copies of their talks are now available for download.
Gothe_MWA_Areas
Lee_GPU_Searching
Perez_Quantum_Chemistry
Wang_Segmenting_Axons
By admin
The following news was released April 2, 2009 by NVIDIA Corporation. The company’s release can be found here.
SANTA CLARA, CA—NVIDIA Corporation, inventor of the GPU, today announced that Harvard University has been recognized as a CUDA Center of Excellence for its commitment to teaching GPU Computing and its integration of CUDA™-enabled GPUs for a host [click on link for more...]
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