Computer Science Events
This calendar presented by
Computer Science
[PAST EVENT] Colloquium on Feb. 13 at 8AM in McGl 020
February 13, 2015
8am
Title: Reducing Computation and Communication in Scientific Computing
Speaker: Grey Ballard, Sandia National Labs
Abstract: Because of the availability of increasingly powerful computers, computational science is playing a significant role in the analysis of complex systems across disciplines, from simulations of climate change to social network analysis. While the applications are wide-ranging, solutions to these problems share many mathematical and computational techniques in common. In this talk I'll focus on numerical linear algebra, one of the key tools in this field, and I'll discuss recent innovations in developing faster algorithms for fundamental matrix computations that maintain the numerical accuracy of the results.
On today's computers, the running time of an algorithm depends not only on the number of operations it performs, but also on its communication requirements (i.e., how much data it moves up and down the memory hierarchy and between processors). I'll demonstrate that by reformulating standard approaches and reducing communication costs, we can improve the efficiency of many matrix computations, including algorithms for solving linear systems, least squares problems, eigenvalue problems, and parallelization of Strassen's matrix multiplication algorithm.
I'll also talk about the prospects of using computer-aided search to discover new algorithms for matrix multiplication that perform asymptotically fewer operations (and require less data movement) than Strassen's algorithm, as well as some other future directions for this research.
Bio: Grey Ballard is currently a Truman Fellow at Sandia National Labs in Livermore, CA. He received his PhD in 2013 from the Computer Science Division (EECS Department) at the University of California Berkeley. He worked in the BeBOP group andParallel Computing Laboratory under advisor James Demmel. Before coming to Berkeley, he received his BS in math and computer science at Wake Forest University in 2006 and his MA in math at Wake Forest in 2008.
His research interests include numerical linear algebra, high performance computing, and computational science, particularly in developing algorithmic ideas that translate to improved implementations and more efficient software. His work has been recognized with the SIAM Linear Algebra Prize and two conference best paper awards, at SPAA and IPDPS, he received the C.V. Ramamoorthy Distinguished Research Award at UC Berkeley, and his PhD thesis was recognized by the ACM Doctoral Dissertation Award - Honorable Mention.
Speaker: Grey Ballard, Sandia National Labs
Abstract: Because of the availability of increasingly powerful computers, computational science is playing a significant role in the analysis of complex systems across disciplines, from simulations of climate change to social network analysis. While the applications are wide-ranging, solutions to these problems share many mathematical and computational techniques in common. In this talk I'll focus on numerical linear algebra, one of the key tools in this field, and I'll discuss recent innovations in developing faster algorithms for fundamental matrix computations that maintain the numerical accuracy of the results.
On today's computers, the running time of an algorithm depends not only on the number of operations it performs, but also on its communication requirements (i.e., how much data it moves up and down the memory hierarchy and between processors). I'll demonstrate that by reformulating standard approaches and reducing communication costs, we can improve the efficiency of many matrix computations, including algorithms for solving linear systems, least squares problems, eigenvalue problems, and parallelization of Strassen's matrix multiplication algorithm.
I'll also talk about the prospects of using computer-aided search to discover new algorithms for matrix multiplication that perform asymptotically fewer operations (and require less data movement) than Strassen's algorithm, as well as some other future directions for this research.
Bio: Grey Ballard is currently a Truman Fellow at Sandia National Labs in Livermore, CA. He received his PhD in 2013 from the Computer Science Division (EECS Department) at the University of California Berkeley. He worked in the BeBOP group andParallel Computing Laboratory under advisor James Demmel. Before coming to Berkeley, he received his BS in math and computer science at Wake Forest University in 2006 and his MA in math at Wake Forest in 2008.
His research interests include numerical linear algebra, high performance computing, and computational science, particularly in developing algorithmic ideas that translate to improved implementations and more efficient software. His work has been recognized with the SIAM Linear Algebra Prize and two conference best paper awards, at SPAA and IPDPS, he received the C.V. Ramamoorthy Distinguished Research Award at UC Berkeley, and his PhD thesis was recognized by the ACM Doctoral Dissertation Award - Honorable Mention.
Contact
[[cs|andreas, Andreas Stathopoulos]]