Core Faculty

  1. Scott Field UMassD Math, MA

  2. Dana Fine, UMassD Math, MA

  3. Robert Fisher, UMassD Physics, MA

  4. J. P. Hsu, UMassD Physics, MA

  5. Gaurav Khanna, UMassD Physics, MA

  6. David Kagan, UMassD Physics, MA

Collaborative Faculty

  1. Martin Bojowald, Penn State, PA

  2. Lior Burko, Georgia G College, GA

  3. Richard Price, MIT / UMassD, MA

  4. Scott Hughes, MIT, MA

  5. Jorge Pullin, Louisiana State, LA

  6. Alessandra Buonanno, Max Planck Inst.

Current Students

  1. Ed McClain, UMassD Physics, MA

  2. Feroz Shaik, UMassD Physics, MA

  3. Alec Yonika, UMassD Physics, MA

  4. Caroline Mallary, UMassD Physics, MA

  5. Connor Kenyon, UMassD Physics, MA

  6. Nur Rifat, UMassD Physics, MA

Past Students (Current Location)

  1. Izak Thuestad, NUWC

  2. Eliza Miley, NUWC

  3. Rahul Kashyap, ICTS, India

  4. Will Duff, Industry

  5. Sarah Seva, Teaching

  6. Tyler Spilhaus, UAlaska

  7. David Torndorf-Dick, UNH

  8. Ed McClain, Louisiana State

  9. Charles Harnden, Teaching

  10. Dan Walsh, Teaching

  11. Gary Forrester, Teaching

  12. Mike DeSousa, Industry

  13. Justin McKennon, General Dynamics

  14. Dave Falta, Michigan State

  15. Matthew Hogan, Florida Atlantic Univ.

  16. Philip Mendonca, Florida Atlantic Univ.

  17. Rakesh Ginjupalli, IBM

  18. Sarah McLeod, Univ. of Melbourne

  19. Ian Nagle, Florida Atlantic Univ.

  20. Joshua Liberty, Univ. of Rhode Island

  21. Emanuel Simon, Univ. of Ulm, Germany

  22. Francis Boateng, UMass Lowell

  23. Subir Sabharwal, Columbia University

  24. Vishnu Paruchuri, Columbia U. Finance

  25. Jessica Rosen, Industry

  26. Peter Goetz, Univ. of Ulm, Germany

  27. Seth Connors, High-School Teacher

  28. Zhenhua Ning, Univ. of Illinois UC

  29. Nobuhiro Suzuki, Univ. of Rhode Island

  30. Mike O'Brien, Rutgers Univ.

  31. Matt Strafuss, MIT

This section is dedicated to the ongoing research projects of our group related to the use of video-gaming technologies for scientific computation. This work is currently supported under NSF grant PHY-01414440 and AFRL CRADA agreement 10-RI-CRADA-09. Initials of the faculty involved, are in parentheses. Here is a list of research articles published using results generated from this effort: Preprint arXiv:1312.5210 (2013); Phys. Rev. D90 084025 (2014); CSC’14 (2014);

Phys. Rev. D91 104017 (2015); Phys. Rev. D93 041501R (2016); Phys. Rev. D94 084049 (2016); IEEE HPEC Conf. (2017); Phys. Rev. D95 081501 (2017); Phys. Rev. D96 024020 (2017); Class. Quant. Grav.  34  205012 (2017);

Also check out the website of our new campus-wide Center for Scientific Computing & Visualization Research.


  1. The Sony PlayStation 3 Gravity Grid (GK)

        This NSF supported project has its own dedicated website. And here is its “big brother”.

  1. An Exploration of the use of OpenCL for Numerical Modeling & Data Analysis (GK)

         This NSF supported project has its own dedicated website. Please visit that site.

  1. Alternative Technologies for Numerical Relativity and LIGO Data Analysis (GK)

  2. This NSF supported project has its own dedicated website. Please visit that site.

  3. Video-Gaming Technologies for Scientific Computing in Gravitational Physics (GK)

  4. This NSF supported project began in 2014. We plan to explore the capabilities of current and next generation consumer-grade, video-gaming hardware for numerical modeling and big data analysis in the area of gravitational wave science. Specific examples of the compute hardware that we consider interesting for this study are current generation gaming GPUs like the AMD Radeon HD & Nvidia GeForce series and also the CPU-GPU hybrid or “fused” processor architectures like AMD Fusion APU, Intel's Ivy Bridge, ARM SoC and next generation gaming consoles such as the Sony PS4.

  5. The main advantage of considering such consumer-grade hardware for scientific computing is its very low cost and high power-efficiency. The parallel software development framework that we utilize in this work is the Open Computing Language (OpenCL).

  6. Partial results of this exploration are available in this presentation made at the AFRL, Rome NY in November 2014: KhannaCRADA2014.pdf and the standardized benchmarks of various compute kernels: SHOC

  7. The final 2017 results of an extensive study in this context are available in this IEEE HPEC Conf. (2017) publication.

  8. Currently, the most promising approach towards developing a very low-cost clustered configuration that is highly power efficient  as well is to use a streaming media type device as a cluster node. An excellent candidate for such a system is a cluster built using Nvidia Shield TV media players. A small solar-powered prototype system using these was released on HPC Day 2017. DIY instructions may be found here: ShieldDIY.


Video-Gaming Hardware for Scientific Computation