[Colloquium] [masters-presentation] Zamora/MS Presentation/Dec 12, 2019

[Margaret Jaffey]

This is an announcement of Yuliana Zamora's MS Presentation.

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Date:  Thursday, December 12, 2019

Time:  4:00 PM

Place:  John Crerar Library 298

M.S. Candidate:  Yuliana Zamora

M.S. Paper Title: Cross Architecture Performance Prediction

Abstract:
Given the rapid evolution of modern computing architectures, the time
required to understand, benchmark, and model each new chip design can
be daunting. Application performance depends on both the underlying
hardware, as well as the efficiency of the application software stack.
Since overall efficiency can depend strongly on hardware-specific
software optimizations, early performance predictions can enable
valuable software modifications be- fore the target architecture is
even in production. In this work, we explore a completely data-driven
approach to hardware performance prediction. We leverage
leadership-scale super-computing resources to build and train
empirical models, with limited profiling data, without modifying any
original source code. We employ both neural-architecture search (NAS)
optimizations and active learning to automatically design and train
deep learning models for the prediction of IPC (instructions per
cycle) across two separate NVIDIA GPU architectures (P100 to V100).
Although the massively-parallel approach certainly yields improved
performance over simple empirical models, the accuracy of IPC
predictions still falls short of expected real-world requirements.

Yuliana's advisors are Prof. Ian Foster and Prof. Henry Hoffmann

Login to the Computer Science Department website for details:
 https://newtraell.cs.uchicago.edu/phd/ms_announcements#yzamora

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Margaret P. Jaffey            margaret at cs.uchicago.edu
Department of Computer Science
Student Support Rep (JCL 350)              (773) 702-6011
The University of Chicago      http://www.cs.uchicago.edu
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