FastQuery is a parallel indexing and querying system we developed for accelerating analysis and visualization of scientific data. We have applied it to a wide variety of HPC applications and demonstrated its capability and scalability using a petascale trillion-particle simulation in our previous work. Yet, through our experience, we found that performance of reading and writing data with FastQuery, like many other HPC applications, could be significantly affected by various tunable parameters throughout the parallel I/O stack. In this paper, we describe our success in tuning the performance of FastQuery on a Lustre parallel file system. We study and analyze the impact of parameters and tunable settings at file system, MPI-IO library, and HDF5 library levels of the I/O stack. We demonstrate that a combined optimization strategy is able to improve performance and I/O bandwidth of FastQuery significantly. In our tests with a trillion-particle dataset, the time to index the dataset reduced by more than one half.

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.