Petascale plasma physics simulations have recently entered the regime of simulating trillions of particles. These unprecedented simulations generate massive amounts of data, posing significant challenges in storage, analysis, and visualization. In this paper, we present parallel I/O, analysis, and visualization results from a VPIC trillion particle simulation running on 120,000 cores, which produces ~30TB of data for a single timestep. We demonstrate the successful application of H5Part, a particle data extension of parallel HDF5, for writing the dataset at a significant fraction of system peak I/O rates. To enable efficient analysis, we develop hybrid parallel FastQuery to index and query data using multi-core CPUs on distributed memory hardware. We show good scalability results for the FastQuery implementation using up to 10,000 cores. Finally, we apply this indexing/query-driven approach to facilitate the first-ever analysis and visualization of the trillion particle dataset.

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.