This talk is a featured presentation of the National Science Foundation Research Experience for Undergraduates (NSF REU) on Computational Methods for Understanding Music, Media, and Minds and is open to all faculty, staff, students and community members.
Lunch sponsored by Goergen Institute for Data Science. Register to ensure we order adequate food.
TITLE: “Identifying Differences in GPUs using Performance Data”
ABSTRACT: Graphics Processing Units (or GPUs) were originally designed to only speed up graphics computation such as games. Over many years,
however, they've become general enough to run many non-graphics
computations. Indeed, due to stagnating CPU performance, GPUs are now the platform of choice for high-performance computing. The amazing advances in deep learning would not have happened without GPUs.
Unfortunately, getting peak performance out of GPUs is hard. GPU
programmers need to understand and exploit low-level architectural
features to obtain performance. Newer, high-level programming
languages alleviate this need somewhat by transforming high-level code to low-level code. However, GPUs have proliferated extensively and are present at every level of computing -- from desktops to mobile
phones. These GPUs are very different from each other and therefore
respond differently to the same transformations -- a transformation
that speeds up a program on one class of GPU can slow it down on
another. Complicating the picture, different programs behave
differently too, and the same program can behave differently when
running on different inputs.
Amidst such differences, can we find patterns that can guide us to
obtain performance reliably on different GPUs? I show that by
examining the performance data of programs, such patterns can be
identified and as a consequence point out the truly important
differences in GPUs that matter for those programs.
[This is joint work with Tyler Sorensen and Alastair Donaldson of
Imperial College London.]
Sreepathi Pai is an Assistant Professor in the Department of Computer
Science at the University of Rochester. As an experimental computer
systems researcher, he works on high-performance heterogeneous
computer architectures and has contributed to compiler optimization,
computer architecture, and performance modelling. Currently, his focus
is on accelerator-based systems such as those containing graphics
processing units (GPUs) that can be leveraged for massive data
processing. Most recently, he has developed the IrGL compiler for
irregular graph algorithms that generates highly optimized code for
GPUs from high-level specifications and which has been demonstrated to outperform expert-written code. He earned his PhD at the Indian
Institute of Science and his B.E. in Computer Engineering at the
University of Mumbai. Prior to joining the Department of Computer
Science at Rochester, he was a Postdoctoral Fellow at the University
of Texas at Austin.
Wednesday, June 27, 2018 at 12:00pm to 1:00pm
Wegmans Hall, Auditorium 1400
250 Hutchison Rd, Rochester, NY 14620
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