Accelerating parallel analysis of scientific simulation data via Zazen

  • Authors:
  • Tiankai Tu;Charles A. Rendleman;Patrick J. Miller;Federico Sacerdoti;Ron O. Dror;David E. Shaw

  • Affiliations:
  • D. E. Shaw Research, New York, NY;D. E. Shaw Research, New York, NY;D. E. Shaw Research, New York, NY;D. E. Shaw Research, New York, NY;D. E. Shaw Research, New York, NY;D. E. Shaw Research, New York, NY and Center for Computational Biology and Bioinformatics, Columbia University, New York, NY

  • Venue:
  • FAST'10 Proceedings of the 8th USENIX conference on File and storage technologies
  • Year:
  • 2010

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Abstract

As a new generation of parallel supercomputers enables researchers to conduct scientific simulations of unprecedented scale and resolution, terabyte-scale simulation output has become increasingly commonplace. Analysis of such massive data sets is typically I/O-bound: many parallel analysis programs spend most of their execution time reading data from disk rather than performing useful computation. To overcome this I/O bottleneck, we have developed a new data access method. Our main idea is to cache a copy of simulation output files on the local disks of an analysis cluster's compute nodes, and to use a novel task-assignment protocol to co-locate data access with computation. We have implemented our methodology in a parallel disk cache system called Zazen. By avoiding the overhead associated with querying metadata servers and by reading data in parallel from local disks, Zazen is able to deliver a sustained read bandwidth of over 20 gigabytes per second on a commodity Linux cluster with 100 nodes, approaching the optimal aggregated I/O bandwidth attainable on these nodes. Compared with conventional NFS, PVFS2, and Hadoop/HDFS, respectively, Zazen is 75, 18, and 6 times faster for accessing large (1-GB) files, and 25, 13, and 85 times faster for accessing small (2-MB) files. We have deployed Zazen in conjunction with Anton--a special-purpose supercomputer that dramatically accelerates molecular dynamics (MD) simulations-- and have been able to accelerate the parallel analysis of terabyte-scale MD trajectories by about an order of magnitude.