Parallel I/O, analysis, and visualization of a trillion particle simulation

Authors:
Surendra Byna;Jerry Chou;Oliver Rübel; Prabhat;Homa Karimabadi;William S. Daughton;Vadim Roytershteyn;E. Wes Bethel;Mark Howison;Ke-Jou Hsu;Kuan-Wu Lin;Arie Shoshani;Andrew Uselton;Kesheng Wu
Affiliations:
Lawrence Berkeley National Laboratory;Tsinghua University, Taiwan;Lawrence Berkeley National Laboratory;Lawrence Berkeley National Laboratory;University of California - San Diego;Los Alamos National Laboratory;University of California - San Diego;Lawrence Berkeley National Laboratory;Brown University;Tsinghua University, Taiwan;Tsinghua University, Taiwan;Lawrence Berkeley National Laboratory;Lawrence Berkeley National Laboratory;Lawrence Berkeley National Laboratory
Venue:
SC '12 Proceedings of the International Conference on High Performance Computing, Networking, Storage and Analysis
Year:
2012

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Abstract

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.