About

Several research groups at Boston University are leveraging the power of graphics processors, known as GPUs, for scientific computing.  Through this site, we hope to disseminate the research output of these groups, as well as to share our experiences and successes.

Experimental GPU cluster at BU

Thanks to NSF and NIH funding,  a new GPU-enabled computational cluster has been installed at Boston University.  The cluster offers peak performance in excess of 32 teraflops (or 32 million million operations per second), at an unprecedentedly low cost for such number-crunching power.  This supercomputer is being used by the principal investigators for research in particle physics and fluid dynamics (Richard Brower, Lorena Barba, Claudio Rebbi), as well as bioinformatics and computational biology (Martin Herbordt).  An additional goal is to foster a “GPU community” at BU; to this end, the cluster will also be made available to other researchers for exploratory projects.

This initiative has already seen involvement from several other research groups, including those of Enrico Belloti (for computational electronics) and Irving Bigio (for modeling photon transport in tissue).  In November 2009, a large collection of faculty, postdoctoral researchers, and graduate students came together to learn about GPU computing in a workshop organized by the co-PIs of the NSF-funded project “Experimental GPU cluster for experimental physics.” This workshop, dubbed GPU@BU, was attended by researchers from all around the Boston area—including Harvard, MIT, Northeastern, and Brown—as well as distinguished guest speakers from NVIDIA Research and Nagasaki University.

Links

• BU Center for Computational Science
• GPU@BU workshop, November 2009.

Email list

Boston-area researchers interested in GPUs are invited to join and post announcements to the following list:

http://barbagroup.bu.edu/mailman/listinfo/gpubu

This is a very low-traffic list, used only to announce workshops, seminars, preprints, and Boston-area GPU computing news.

Acknowledgments

This material is based upon work supported by the National Science Foundation under Grant No. 0946441. Any opinions, findings, and conclusions or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views of the National Science Foundation.