Parallel I/O is fast becoming a bottleneck to the research agendas of many users of extreme scale parallel computers. The principle cause of this is the concurrency explosion of high-end computation, coupled with the complexity of providing parallel file systems that perform reliably at such scales. More than just being a bottleneck, parallel I/O performance at scale is notoriously variable, being influenced by numerous factors inside and outside the application, thus making it extremely difficult to isolate cause and effect for performance events. In this paper, we propose a statistical approach to understanding I/O performance that moves from the analysis of performance events to the exploration of performance ensembles. Using this methodology, we examine two I/O-intensive scientific computations from cosmology and climate science, and demonstrate that our approach can identify application and middleware performance deficiencies - resulting in more than 4× run time improvement for both examined applications.
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