News
From PPL
| | A chip too far? MICHAEL COPELAND -- August 14, 2008 That's a dramatic change, and battalions of techies from Redmond, Wash., to San Jose are struggling to figure it out. "If I were the computer industry, I would be panicked, because it's not obvious what the solution is going to look like and whether we will get there in time for these new machines," says Kunle Olukotun, a computer science professor who is attacking the multicore challenge at Stanford's new Pervasive Parallelism Lab. "It's a crisis, and I wonder whether what we are doing and what is happening within the industry is too little, too late." [more] |
| Stanford, tech giants team up to enable software for parallel computers NEWS RELEASE -- April 30, 2008 |
| | Race Is On to Advance Software for Chips
JOHN MARKOFF -- April 30, 2008 Stanford University and six computer and chip makers plan to announce Friday the creation of the Pervasive Parallelism Lab. Besides Stanford, the backers are Sun Microsystems, Advanced Micro Devices, Nvidia, I.B.M., Hewlett-Packard and Intel. [more] |
| | Another team joins race to advance chip software
STEVEN MUSIL -- April 29, 2008 It has been supplanted by performance per watt, which addresses the greening of the chip industry. The performance bump that formerly came from cranking up clock speed is now the province of multicores. The only problem is that most software isn't good at taking advantage of multicore architectures. To overcome that hurdle, Stanford University is partnering with Sun Microsystems, Advanced Micro Devices, Nvidia, IBM, Hewlett-Packard, and Intel to create software that will allow chips to more efficiently process many tasks at the same time. [more] |
| Stanford grabs $6m to shape the future of software
ASHLEE VANCE -- April 30, 2008 In the coming days, Stanford will unveil the Pervasive Parallelism Lab on the back of $6m in funding over three years from Sun Microsystems, AMD, Nvidia, IBM, HP and even Intel. The lab will be headed by Kunle Olukotun, a Stanford professor, who made a major name for himself in the multi-core world by helping originate chips now sold by Sun as part of its Niagara or UltraSPARCTx family. The goal of the lab will be to make writing software for multi-core chips easier. [more] |
| Solving the massively-parallel software problem
CHRIS NUTTALL -- April 30, 2008 This is the question that Stanford University hopes to answer with its Pervasive Parallelism Lab, announced on Wednesday. [more] |
| | Industry, Stanford hope to fix what ails parallel processing
PETER BRIGHT -- May 1, 2008 |
| | Industry suddenly realises multi-cored chips are useless unless used
SYLVIE BARAK -- April 30, 2008 According to the New York Times, the partnership between the University and the six rival computer and chip makers will be formally announced this Friday, and the project will be dubbed the “Pervasive Parallelism Lab”. [more] |
| | Nvidia to help sponsor Stanford parallel computing research lab
April 30, 2008 The lab will develop new techniques, tools, and training materials "to allow software engineers to harness the parallelism of the multiple processors that are already available in virtually every new computer," Nvidia said. [more] |
| | Stanford kicks off parallel programming effort
RICK MERRITT -- April 30, 2008 Advanced Micro Devices, Hewlett-Packard, IBM, Intel, NVidia and Sun Microsystems are funding Stanford's new Pervasive Parallelism Lab which will consist of about nine faculty and as many as 30 graduate students. Kunle Olukotun, a Stanford computer science professor seen as the father of Sun's multicore Niagara processor, will head the new lab. [more] |
| | Stanford Builds Parallel Computing Lab
SCOTT FERGUSON -- April 30, 2008 On May 2, the university, along with some of world's largest IT companies, plans to unveil the Pervasive Parallelism Lab, which looks to develop new ways to create applications that can take advantage of the ever-increasing number of multicore processors coming into the marketplace. The lab, which will have a $6 million budget during the next three years, will not only look for ways to develop new programming languages that make it easier to create applications that work with parallel computing—breaking down information into smaller parts to take advantage of multiple processing cores—but also to create the hardware to house these new multicore processors. [more] |



