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Activity Number: 347 - Machine Learning and Applications in Complex Engineering Systems
Type: Contributed
Date/Time: Tuesday, July 31, 2018 : 10:30 AM to 12:20 PM
Sponsor: Section on Physical and Engineering Sciences
Abstract #330148 Presentation
Title: Prediction for Distributional Outcomes in the Management of High-Performance Computing Input/Output (I/O) Variability
Author(s): Li Xu* and Thomas Lux and Tyler Chang and Bo Li and Yili Hong and Layne Watson and Kirk Cameron and Jon Bernard
Companies: Virginia Tech and Virginia Tech and Virginia Tech and Virginia Tech and Virginia Tech and Virginia Tech and Virginia Tech and Virginia Tech
Keywords: computer experiment; functional data analysis

One important research area in high-performance computing (HPC) is the management of performance variability, which is affected by complicated interactions of numerous factors, such as CPU frequency, the number of I/O threads, file size and record size. In this paper, we are interested in the I/O variability, which is measured by the I/O throughputs that varies from run to run. Given a system configuration, one way to describe the variability is to use the cumulative distribution function (cdf) for the throughputs. Prediction the cdf of throughputs for a new system configuration is often of interest, which, however, is a challenging task. To overcome this challenge, computer scientists conducted large-scale experiments and collected a mass amount of data for the distribution of variability under various system configurations. We develop a Gaussian process model to predict the cdf under a new configuration using the experimental data. We also impose a monotone constraint so that the predicted function is monotonically increasing, which is one desired property of the cdf. We evaluate the performance of the proposed method by using the experimental data. We also discuss the methodolog

Authors who are presenting talks have a * after their name.

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