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UIUC, Nvidia Partner For Parallel Processer Class
The University of Illinois at Urbana-Champaign has partnered with Nvidia to offer a pioneering new class, Programming Massively Parallel Processors, that the two hope will prepare them to be leaders in the parallel computing revolution.
April 25, 2007
2 Min Read
Author: by Beth A.
Students at the University of Illinois at Urbana-Champaign are part of a pioneering new class supported by NVIDIA that’s preparing them to be leaders in the parallel computing revolution. In a unique arrangement, the class is co-taught by Dr. David Kirk, chief scientist at graphics-processing industry leader NVIDIA, and Dr. Wen-mei Hwu, the AMD Jerry Sanders Chair of Electrical and Computer Engineering at Illinois. The course, titled ECE 498: Programming Massively Parallel Processors, is meeting a growing industry need for better prepared students, as parallel processors are quickly becoming the computing standard. “We want to help students tap into the massive computing power of these processors to allow them to do work that was too computationally expensive to do before,” Hwu says. “We also want to help them design future massively parallel processors and programming tools.” In parallel processing, multiple processors are employed simultaneously, or in parallel, to attack a problem, each processing a portion of the data. More processors equal greater speed. But using parallel processing also means a different approach for the programmer. The shift to parallel processing presents a challenge, however, because most universities are not teaching students how to use the technology. “Traditionally, computer science and computer engineering education has not really addressed parallel processing as an important part of programming. It’s a graduate course and it’s an elective,” Kirk says. “As we move forward with multi-core processors and highly-parallel GPUs, everybody will need to know how to program massively parallel processors because that’s all there will be. There won’t be any single core processors any more.” Both Kirk and Hwu hope the class will eventually become a core class in the curriculum. They believe all computer engineering and computer science students should be required to learn about parallel processors early in their undergraduate education so they can use the knowledge during the course of their studies.
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