[Colloquium] Time Correction - Yuliana Zamora Dissertation Defense/Jun 3, 2022

[Megan Woodward]

This is an announcement of Yuliana Zamora's Dissertation Defense.
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Candidate: Yuliana Zamora

Date: Friday, June 03, 2022

Time: 10:30 am CST

Remote Location:  https://zoom.us/j/91528203161?pwd=K0U5RXBsUktORGtlVDVoSkQwMU9QZz09 Meeting ID: 915 2820 3161 Passcode: T35nAc


Title: Machine Learning for Performance Acceleration and Prediction in Scientific Computing

Abstract: Scientific applications often require massive amounts of compute time and power. With the
constantly expanding architecture landscape and growing complexity of application runs,
understanding how to improve performance is vital. In this thesis, we will use machine
learning in two distinct ways to improve science applications.
First, we use a data-driven approach and leverage machine learning to understand and
improve performance in high-performance computing applications. The goal of this work
is to create a streamlined workflow of integrating machine learning surrogates into such
applications. Using control theory to automatically and dynamically configure parameters,
we can meet accuracy constraints while maximizing performance. This workflow, which we
examine in the context of molecular dynamics simulations, will allow for faster and larger
simulations by improving overall performance. By replacing typically high-cost functions,
such as density functional theory calls or Hartree-Fock calls, with a low-cost machine learning
inference call, our proposed workflow can reduce run-time while producing scientifically
usable results. We create a decision engine that will automate finding the accuracy and
performance trade-off relationship between using a high-fidelity, high-cost function call, and
a lower fidelity machine learning inference.
Second, in an effort to understand future performance, we focus on predicting performance
across multiple architectures. Cross-architecture metric mapping is heavily studied
with hopes of understanding application performance on future or untested machine architectures.
Often, there are months or even years spent on porting applications to new
architectures, that may or may not provide an increase in performance. Here, we will use a
data-driven approach to predict total throughput across different GPU architectures in order
to understand future success of these applications. Our goal is to create a general framework
that can predict application performance on a target hardware, given performance metrics
from a different hardware architecture, without expert input. In this thesis, we propose such
a framework and use it to predict total throughput and IPC between two GPU architectures.

Advisors: Ian Foster and Hank Hoffman

Committee Members: Hank Hoffmann, Logan Ward, and Ian Foster


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