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SCHEDULE: NOV 16-21, 2014
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Efficient I/O and Storage of Adaptive Resolution Data
SESSION: Big Data Analysis
EVENT TYPE: Papers
TIME: 11:00AM - 11:30AM
SESSION CHAIR: Kelly Gaither
AUTHOR(S):Sidharth Kumar, John Edwards, Peer-Timo Bremer, Aaron Knoll, Cameron Christensen, Venkatram Vishwanath, Philip Carns, John A. Schmidt, Valerio Pascucci
ROOM:391-92
ABSTRACT:
We present an efficient, flexible, adaptive-resolution I/O framework that is suitable for both uniform and Adaptive Mesh Resolution (AMR) simulations. In an AMR setting, current solutions typically represent each resolution level as independent grids which often results in inefficient storage and performance. Our technique coalesces domain data into a unified, multi-resolution representation with fast spatially aggregated I/O. Furthermore, our framework easily extends to importance-driven storage of uniform grids, for example, by storing regions of interest at full resolution and nonessential regions at lower resolution for visualization or analysis. Our framework, which is an extension of the PIDX framework, achieves state of the art disk usage and I/O performance regardless of resolution of the data, regions of interest, and the number of processes that generated the data. We demonstrate the scalability and efficiency of our framework using the Uintah and S3D large-scale combustion codes on machines Mira and Edison.
Chair/Author Details:
Kelly Gaither (Chair) - Texas Advanced Computing Center
Sidharth Kumar - University of Utah
John Edwards - University of Utah
Peer-Timo Bremer - Lawrence Livermore National Laboratory
Aaron Knoll - University of Utah
Cameron Christensen - University of Utah
Venkatram Vishwanath - Argonne National Laboratory
Philip Carns - Argonne National Laboratory
John A. Schmidt - University of Utah
Valerio Pascucci - University of Utah
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