MOANA: Modeling and Analyzing I/O Variability in Parallel System Experimental Design
Kirk W. Cameron, Ali Anwar, Yue Cheng, Li Hong Xu, Bo Li, Uday Ananth, Jon Bernard, Chandler Jearls, Thomas C. H. Lux, Yili Hong, Layne T. Watson, Ali Raza Butt · IEEE Transactions on Parallel and Distributed Systems · 2019
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 in an effort to demonstrate that variability is often a predictable artifact of system design. Using over 8 months of data collection on 6 identical systems, we propose and validate 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.