Cameron, Kirk W.Anwar, AliCheng, YueXu, LiLi, BoAnanth, UdayLux, ThomasHong, YiliWatson, Layne T.Butt, Ali R.2018-04-202018-04-202018-04-19http://hdl.handle.net/10919/82857Exponential 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.enIn CopyrightComputer SystemsHigh Performance ComputingParallel and Distributed ComputingTechnical reportTR-18-04