Productive GPU Programming with Thrust and Copperhead
Details
VIP Speaker: Dr Bryan Catanzaro - Productive GPU Programming with Thrust and Copperhead
Come for Table discussions, Member Self-Intro, What's New, Application Showcase, and Advanced Application Development Techniques! Exchange ideas, meet experts, share code... all HPC & GPU, all practical, all cutting-edge.
Agenda:
General Discussions:
6:15-6:30pm What’s new in HPC & GPU Supercomputing
6:30-6:45pm Member self-intros: 30 seconds for each member
Main Program:
6:45-7:45pm Productive GPU Programming with Thrust and Copperhead (Bryan Catanzaro, Ph.D.)
7:45-7:50pm Break
7:50-8:20pm Heterogeneous Parallel Computing for Cognitive Systems: An Overview (Robert Rossi)
Book Review:
8:20-8:30pm Selected Chapters
Productive GPU Programming with Thrust and Copperhead
We sometimes speak of an "abstraction tax", which reduces implementation efficiency for programmers using productivity focused programming models. Although this tax is real, in the world of parallel computing we also have a "concrete tax", which paradoxically reduces implementation efficiency for programmers using low-level programming models. In this talk, we present Thrust, a C++ library which presents a familiar and flexible set of data parallel operations which dramatically improve productivity and provide excellent performance. We also discuss Copperhead, a data parallel language embedded in Python, which allows programmers to take advantage of data parallel programming by writing data parallel programs intermixed and interoperating with standard Python code. These projects aim to circumvent both the abstraction tax and the concrete tax, making parallel programming both productive and efficient.
Speaker Bio:
Dr. Bryan Catanzaro is a research scientist at NVIDIA. His work focuses on tools and programming methodologies for parallel processors, especially GPUs. He recently earned his PhD from UC Berkeley, under the direction of Kurt Keutzer, where he built the Copperhead language and compiler. He created the GPUSVM and Damascene libraries for Support Vector Machine training and high-quality image contour detection, and has written several articles on OpenCL.
Location:
Room 109/110;
Carnegie Mellon Silicon Valley;
NASA Research Park Bldg 23;
Mountain View, CA 94043;
Directions (http://www.cmu.edu/silicon-valley/about-us/directions.html) to Carnegie Mellon Silicon Valley;
Google Map (http://maps.google.com/maps/ms?gl=us&hl=en&ie=UTF8&msa=0&ll=37.410941,-122.063169&spn=0.019191,0.048923&t=h&z=15&msid=215438781255871976989.00049cacf6f0e5596e5cc) showing parking, check point, and building entrance;
NOTE: You will need a government issued ID (e.g. Driver's License) to enter NASA Research Park
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