MOANA: Modeling and Analyzing I/O Variability in Parallel System Experimental Design

TR Number

TR-18-04

Date

2018-04-19

Journal Title

Journal ISSN

Volume Title

Publisher

Department of Computer Science, Virginia Polytechnic Institute & State University

Abstract

Exponential increases in complexity and scale make variability a growing threat to sustaining HPC performance at exascale. Performance variability in HPC I/O is common, acute, and formidable. We take the first step towards comprehensively studying linear and nonlinear approaches to modeling HPC I/O system variability. We create a modeling and analysis approach (MOANA) that predicts HPC I/O variability for thousands of software and hardware configurations on highly parallel shared-memory systems. Our findings indicate nonlinear approaches to I/O variability prediction are an order of magnitude more accurate than linear regression techniques. We demonstrate the use of MOANA to accurately predict the confidence intervals of unmeasured I/O system configurations for a given number of repeat runs – enabling users to quantitatively balance experiment duration with statistical confidence.

Description

Keywords

Computer Systems, High Performance Computing, Parallel and Distributed Computing

Citation