The Democratization of Parallel Computing
Virginia Tech College of Engineering Professor Wu Feng has a vision to broadly apply parallel computing to advance science and address major challenges. A recent expose on Feng’s work details his involvement with the NSF, Microsoft, and the Air Force using innovative computing techniques to solve problems.
“Delivering personalized medicine to the masses is just one of the grand challenge problems facing society,” said Feng. “To accelerate the discovery to such grand challenge problems requires more than the traditional pillars of scientific inquiry, namely theory and experimentation. It requires computing. Computing has become our ‘third pillar’ of scientific inquiry, complementing theory and experimentation. This third pillar can empower researchers to tackle problems previously viewed as infeasible.”
He addresses the question of why bolstering these disciplines is no longer a matter of throwing more FLOPs at the problem.
“In short, with the rise of ‘big data’, data is being generated faster than our ability to compute on it,” he explains. “For instance, next-generation sequencers (NGS) double the amount of data generated every eight to nine months while our computational capability doubles only every 24 months, relative to Moore’s Law. Clearly, tripling our institutional computational resources every eight months is not a sustainable solution… and clearly not a fiscally responsible one either. This is where parallel computing in the cloud comes in.”
“…Rather than having an institution set-up, maintain, and support an information technology infrastructure that is seldom utilized anywhere near its capacity… and having to triple these resources every eight to nine months to keep up with the data deluge of next-generation sequencing, cloud computing is a viable and more cost effective avenue for accessing necessary computational resources on the fly and then releasing them when not needed.”
Much of his work centers on the promise of parallel computing, which he sees as analogous to the Internet in terms of its ability to transform the way people interact.
In the mid-2000s, Feng was part of a team that created an ad-hoc supercomputing cloud to process genomics data. They were able to reduce the time it took to identify missing gene annotations in genomes from a period of three years down to two weeks by adopting added parallelism. This project is now being formalized and expanded with funding from NSF and Microsoft with the aim of commoditizing biocomputing in the cloud.
To facilitate this important research, Feng founded a new center at Virginia Tech — Synergistic Environments for Experimental Computing (SEEC). The center is co-funded by Virginia Tech’s Institute for Critical Technology and Applied Science (ICTAS), the Office of Information Technology, and the Department of Computer Science. Under Feng’s leadership, the research center seeks to democratize parallel computing through the codesign of algorithms, software, and hardware to accelerate discovery and innovation. Emphasis will be placed on five areas, each with varying degrees of “big compute” and “big data” requirements: cyber-physical systems where computing and physical systems intersect; health and life sciences, including the medical sciences; business and financial analytics; cybersecurity; and scientific simulation.