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Computer Science Department Colloquium

A Case for Correctly Rounded Elementary Functions

 

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Monday, September 12, 2022, 10:30am
 

Speaker: Santosh Nagarakatte

Bio

Santosh Nagarakatte is an Associate Professor and Undergraduate Program Director of Computer Science at Rutgers University. He obtained his PhD from the University of Pennsylvania in 2012. His research interests are in Hardware-Software Interfaces spanning Programming Languages, Compilers, Software Engineering, and Computer Architecture.  His group's research has been recognized with the NSF CAREER Award, two IEEE Micro Top Picks Paper Awards (2010 and 2013), five Distinguished Paper Awards (PLDI 2015, ICSE 2016, PLDI 2021, POPL 2022, and CGO 2022), SIGPLAN Research Highlights paper, CACM Research Highlight paper, 2018 ACM SIGPLAN John C Reynolds Outstanding Dissertation Award, Google Research Award, Intel Corporation Gifts, Facebook research award, and 2022 ACM SIGPLAN John C Reynolds Outstanding Dissertation Award. 

Location : CoRE 301 + Virtual

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Event Type: Computer Science Department Colloquium

Abstract: This talk will provide an overview of the RLIBM project where we are building a  collection of correctly rounded elementary functions for multiple representations and rounding modes.  Historically, polynomial approximations for elementary functions have been designed by approximating the real value.In contrast, we make a case for approximating the correctly rounded result of an elementary function rather than the real value of an elementary function in the RLIBM project.   Once we approximate the correctly rounded result, there is an interval of real values around the correctly rounded result such that producing a real value in this interval rounds to the correct result. This interval is the freedom that the polynomial approximation has for an input, which is larger than the ones with the mini-max approach.  Using these intervals, we structure the problem of generating polynomial approximations that produce correctly rounded results for all inputs as a linear programming problem. The results from the RLIBM project makes a strong case for mandating correctly rounded results with any representation that has fewer than or equal to 32-bits. Read more about the RLIBM project at   https://people.cs.rutgers.edu/~sn349/rlibm/

Organization: 

Department of Computer Science

School of Arts & Sciences

Rutgers University

Contact  Matthew Stone

Zoom: https://rutgers.zoom.us/j/94714843622?pwd=aWduaGEyWjVQeG1PcVdPWTc0V2dpQT09