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Top Schools Adopt NVIDIA CUDA for Parallel Programming Courses
As the computing industry rapidly moves to multi-core and parallel processing architectures, tomorrow’s software engineer must be educated on the best tools and methodologies for parallel computing. NVIDIA announced today that the CUDA software environment is now being actively used in parallel programming courses at over 20 universities worldwide with many more currently evaluating NVIDIA’s tools for parallel programming for inclusion in their curriculum. 
NVIDIA CUDA is a C-compiler and software development kit (SDK) for developing computing applications on graphics processing units (GPUs). Top schools actively using CUDA today include: * University of Illinois at Urbana-Champaign * University North Carolina at Chapel Hill * University of California, Davis * Purdue University * Stanford University * Johns Hopkins University * University of Pennsylvania * University of California, San Diego * University of California, Berkeley * India Institute of Technology (Delhi) “Our course focuses heavily on helping our students understand the general principles of parallelism and NVIDIA has been instrumental in helping us build and integrate this course into our curriculum,” said Dr. Wen-Mei Hwu, course lead at the University of Illinois at Urbana-Champaign. “It is our job to ensure that students leave here not just with a solid grounding in programming techniques used today but to prepare them for the techniques that will be required of them tomorrow; and with the market moving to multiple cores, parallel programming will clearly be one of those techniques.” "Perhaps the most important challenge facing the computing community is the move to parallel processing. As educators, teaching parallel hardware and software today are vital to giving our students the tools they need to build tomorrow's hardware and software,” said John Owens, assistant professor, department of computer engineering at University of California, Davis. “NVIDIA GPUs and the CUDA programming environment are a terrific way for us to put cheap, powerful data-parallel processing on the desktop for all our students." “The broad acceptance of CUDA in education is an extremely significant development for the installation of parallel programming courses as a core discipline in the curriculum of today’s computer science student,” said Dr. David Kirk, NVIDIA chief scientist and co-leader of the parallel programming course at the University of Illinois at Urbana-Champaign. “Course materials developed by NVIDIA for university level courses will allow highly parallel architectures, such as the GPU, to drive the future of HPC and ensure that the next generation of programmers understand the strategies necessary to fully exploit parallel computing.” NVIDIA Tesla GPU computing solutions and CUDA software development tools will be demonstrated at SuperComputing 07 in Reno, Nev (booth #478) from November 10-16, and more information can be downloaded online at www.nvidia.com/cuda and www.nvidia.com/object/tesla_computing_solutions.
NVIDIA CUDA is a C-compiler and software development kit (SDK) for developing computing applications on graphics processing units (GPUs). Top schools actively using CUDA today include: * University of Illinois at Urbana-Champaign * University North Carolina at Chapel Hill * University of California, Davis * Purdue University * Stanford University * Johns Hopkins University * University of Pennsylvania * University of California, San Diego * University of California, Berkeley * India Institute of Technology (Delhi) “Our course focuses heavily on helping our students understand the general principles of parallelism and NVIDIA has been instrumental in helping us build and integrate this course into our curriculum,” said Dr. Wen-Mei Hwu, course lead at the University of Illinois at Urbana-Champaign. “It is our job to ensure that students leave here not just with a solid grounding in programming techniques used today but to prepare them for the techniques that will be required of them tomorrow; and with the market moving to multiple cores, parallel programming will clearly be one of those techniques.” "Perhaps the most important challenge facing the computing community is the move to parallel processing. As educators, teaching parallel hardware and software today are vital to giving our students the tools they need to build tomorrow's hardware and software,” said John Owens, assistant professor, department of computer engineering at University of California, Davis. “NVIDIA GPUs and the CUDA programming environment are a terrific way for us to put cheap, powerful data-parallel processing on the desktop for all our students." “The broad acceptance of CUDA in education is an extremely significant development for the installation of parallel programming courses as a core discipline in the curriculum of today’s computer science student,” said Dr. David Kirk, NVIDIA chief scientist and co-leader of the parallel programming course at the University of Illinois at Urbana-Champaign. “Course materials developed by NVIDIA for university level courses will allow highly parallel architectures, such as the GPU, to drive the future of HPC and ensure that the next generation of programmers understand the strategies necessary to fully exploit parallel computing.” NVIDIA Tesla GPU computing solutions and CUDA software development tools will be demonstrated at SuperComputing 07 in Reno, Nev (booth #478) from November 10-16, and more information can be downloaded online at www.nvidia.com/cuda and www.nvidia.com/object/tesla_computing_solutions. 
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