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Rich Vuduc

Associate Professor at the Georgia Institute of Technology

Computational Science and Engineering

Richard (Rich) Vuduc is an Associate Professor at the Georgia Institute of Technology (“Georgia Tech”), in the School of Computational Science and Engineering. His research lab, The HPC Garage (@hpcgarage on Twitter and Instagram), is interested in high-performance computing, with an emphasis on algorithms, performance analysis, and performance engineering. He is a recipient of a DARPA Computer Science Study Group grant; an NSF CAREER award; a collaborative Gordon Bell Prize in 2010; Lockheed-Martin Aeronautics Company Dean’s Award for Teaching Excellence (2013); and Best Paper Awards at the SIAM Conference on Data Mining (SDM, 2012) and the IEEE Parallel and Distributed Processing Symposium (IPDPS, 2015), among others. He has also served as his department’s Associate Chair and Director of its graduate programs. External to Georgia Tech, he currently serves as Chair of the SIAM Activity Group on Supercomputing (2018-2020); co-chaired the Technical Papers Program of the “Supercomputing” (SC) Conference in 2016; and serves as an associate editor of both the International Journal of High-Performance Computing Applications and IEEE Transactions on Parallel and Distributed Systems. He received his Ph.D. in Computer Science from the University of California, Berkeley, and was a postdoctoral scholar in the Center for Advanced Scientific Computing the Lawrence Livermore National Laboratory.

An algorithm, a data structure, and a machine that all (try to) move or store fewer bits

These are the studies. The first is a novel sparse direct solver algorithm based on communication-avoidance, namely, using a constant amount of extra memory to reduce communication volume, possibly even asymptotically depending on the input. The second is a new and tunable data structure for sparse tensor computations, which uses fewer bits than conventional methods while enhancing locality. The third is an evaluation of a new machine, called the Emu, that supports sparse and irregular computation through relatively fast fine-grained data accesses and, even more interestingly, “moving code to data” rather than the other way around. We’ll summarize each of these cases and present experimental results to validate (or refute?) their efficacy.