Speaker: Erin Carson, Courant
Location: 60 Fifth Avenue 150
Date: February 7, 2018, 2 p.m.
Host: Michael Overton
Sparse linear algebra problems, typically solved using iterative methods, are ubiquitous throughout scientific and data analysis applications and are often the most expensive computations in large-scale codes due to the high cost of data movement. Approaches to improving the performance of iterative methods typically involve modifying or restructuring the algorithm to reduce or hide this cost. Such modifications can, however, result in drastically different behavior in terms of convergence rate and accuracy. A clear, thorough understanding of how inexact computations, due to either finite precision error or intentional approximation, affect numerical behavior is thus imperative in balancing the tradeoffs between accuracy, convergence rate, and performance in practical settings.
In this talk, we focus on two general classes of iterative methods for solving linear systems: Krylov subspace methods and iterative refinement. We present bounds on the attainable accuracy and convergence rate in finite precision s-step and pipelined Krylov subspace methods, two popular variants designed for high performance. For s-step methods, we demonstrate that our bounds on attainable accuracy can lead to adaptive approaches that both achieve efficient parallel performance and ensure that a user-specified accuracy is attained. We present two such adaptive approaches, including a residual replacement scheme and a variable s-step technique in which the parameter s is chosen dynamically throughout the iterations. Motivated by the recent trend of multiprecision capabilities in hardware, we present new forward and backward error bounds for a general iterative refinement scheme using three precisions. The analysis suggests that on architectures where half precision is implemented efficiently, it is possible to solve certain linear systems up to twice as fast and to greater accuracy.
As we push toward exascale level computing and beyond, designing efficient, accurate algorithms for emerging architectures and applications is of utmost importance. We discuss extensions to machine learning and data analysis applications, the development of numerical autotuning tools, and the broader challenge of understanding what increasingly large problem sizes will mean for finite precision computation both in theory and practice.
Erin Carson is a Courant Instructor/Assistant Professor at the Courant Institute of Mathematical Sciences at New York University. She received her PhD at the University of California, Berkeley in 2015, advised by James Demmel and Armando Fox and supported by a National Defense Science and Engineering Graduate Fellowship. Her research interests lie at the intersection of high-performance computing, numerical linear algebra, and parallel algorithms, with a focus on analyzing tradeoffs between accuracy and performance in algorithms for large-scale sparse linear algebra.
Refreshments will be offered starting 15 minutes prior to the scheduled start of the talk.