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Accelerators at Virginia Tech
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Accelerators at Virginia Tech. Depts of Computer Science and. Electrical and Computer Engineering,. Our research is supported by NSF, RNet Technologies, IBM, AMD, NVIDIA, Eli Lilly and Company, SURA, and Virgnia Tech Foundation. Middot; Template design by Andreas Viklund.
Accelerators at Virginia Tech | People
http://accel.cs.vt.edu/people.php
Accelerators at Virginia Tech. Department of Computer Science. Dept of Biological Sciences. Scott D. King. Ashwin M. Aji. Our research is supported by NSF, RNet Technologies, IBM, AMD, NVIDIA, Eli Lilly and Company, SURA, and Virgnia Tech Foundation. Middot; Template design by Andreas Viklund.
Accelerators at Virginia Tech | Facilities
http://accel.cs.vt.edu/news.php
Accelerators at Virginia Tech. All news in reverse-chronological order. Paper Accepted to AsHES 2017. Kaixi and Feng, in collaboration with Che at AMD, have their paper Auto-Tuning Strategies for Parallelizing Sparse Matrix-Vector (SpMV) Multiplication on Multi- and Many-Core Processors. Accepted for publication in the 7th International Workshop on Accelerators and Hybrid Exascale Systems (AsHES). To be held in Orlando, Florida in May 2017. Papers Accepted to IEEE IPDPS 2017. Poster Accepted to CGO 2017.
Accelerators at Virginia Tech | Facilities
http://accel.cs.vt.edu/facilities.php
Accelerators at Virginia Tech. Accelerated" High-Performance Computing Resources. HokieSpeed - GPU Accelerated Supercomputer:. Curently in the design phase, slated for construction and release in mid-late 2011. GPU - Accelerated Desktop Supercomputers:. 10 GPU-accelerated desktops, each driven by one or more of the following GPUs: AMD HD 3870, HD 4870 and HD5870 and NVIDIA 8800 GTS, 9800 GX2, Tesla D870 and GTX 280. Low Power GPU Systems:. Other "Non-Accelerated" High-Performance Computing Resources.
Accelerators at Virginia Tech | Courses
http://accel.cs.vt.edu/courses.php
Accelerators at Virginia Tech. Parallel Computation (undergraduate course). Accelerator-Based Parallel Computing (undergraduate course). Advanced Parallel Computation (graduate course) (Same website as CS 4234). Accelerator-Based Parallel Computing (graduate course). University of California at Davis. EE 171: Parallel Computer Architecture. University of Illinois at Urbana-Champaign. ECE 498AL: Programming Massively Parallel Processors. CIS 665 GPU Programming and Architecture. Computation to Core Mapping.
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About
http://tom.scogland.com/about
I'm Tom Scogland, a postdoctoral scholar in the Center for Applied Scientific Computing at Lawrence Livermore National Laboratory, a technology enthusiast, programmer, productivity nut, and avid cook. This page has some general information about me, my interests, and research directions. If you are interested in collaborating, or simply want to know more feel free to contact me through one of the various means under Contact above. Alumni of the SyNeRG Lab at VT. Technical leader on the Green500.
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Accelerators at Virginia Tech
Accelerators at Virginia Tech. Depts of Computer Science and. Electrical and Computer Engineering,. Our research is supported by NSF, RNet Technologies, IBM, AMD, NVIDIA, Eli Lilly and Company, SURA, and Virgnia Tech Foundation. Middot; Template design by Andreas Viklund.
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