Programming Massively Parallel Processors with CUDA
Stanford UniversityVirtually all semiconductor market domains, including PCs, game consoles, mobile handsets, servers, supercomputers, and networks, are converging to concurrent platforms. There are two important reasons for this trend. First, these concurrent processors can potentially offer more effective use of chip space and power than traditional monolithic microprocessors for many demanding applications. Secon... more
| Publishes | Daily | Episodes | 16 | Founded | 16 years ago |
|---|---|---|---|---|---|
| Category | Technology | ||||
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Michael Garland, of NVIDIA Research, discusses sorting methods in order to make searching, categorization, and building of data structures in parallel easier. (April 20, 2010)
John Nicholis discusses how to optimize with Parallel GPU Performance. (May 20, 2010)
Avi Bleiweiss delivers a lecture on the path planning system on the GPU. (May 18, 2010)
Students are taught how to effectively program massively parallel processors using the CUDA C programming language. Students also develop familiarity with the language itself and are exposed to the architecture of modern GPUs. (April 15, 2010)
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