Optimizing FastQuery Performance on Lustre File System

FastQuery is a parallel indexing and querying system we developed for accelerating analysis and visualization of sci-entific data. We have applied it to a wide variety of HPC applications and demonstrated its capability and scalabil-ity using a petascale trillion-particle simulation in our pre-vious work. Yet, through our experience, we found that performance of reading and writing data with FastQuery, like many other HPC applications, could be significantly af-fected 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 sys-tem. 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 com-bined 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