Data exploration of turbulence simulations using a database cluster

  • Authors:

  • Eric Perlman;Randal Burns;Yi Li;Charles Meneveau

  • Affiliations:

  • Johns Hopkins University, Baltimore, MD;Johns Hopkins University, Baltimore, MD;Johns Hopkins University, Baltimore, MD;Johns Hopkins University, Baltimore, MD

  • Venue:
  • Proceedings of the 2007 ACM/IEEE conference on Supercomputing
  • Year:
  • 2007

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Abstract

We describe a new environment for the exploration of turbulent flows that uses a cluster of databases to store complete histories of Direct Numerical Simulation (DNS) results. This allows for spatial and temporal exploration of high-resolution data that were traditionally too large to store and too computationally expensive to produce on demand. We perform analysis of these data directly on the databases nodes, which minimizes the volume of network traffic. The low network demands enable us to provide public access to this experimental platform and its datasets through Web services. This paper details the system design and implementation. Specifically, we focus on hierarchical spatial indexing, cache-sensitive spatial scheduling of batch workloads, localizing computation through data partitioning, and load balancing techniques that minimize data movement. We provide real examples of how scientists use the system to perform high-resolution turbulence research from standard desktop computing environments.