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There is now a CONTENT FREEZE for Mercury while we switch to a new platform. It began on Friday, March 10 at 6pm and will end on Wednesday, March 15 at noon. No new content can be created during this time, but all material in the system as of the beginning of the freeze will be migrated to the new platform, including users and groups. Functionally the new site is identical to the old one. webteam@gatech.edu
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Tiankai Tu
Computer Scientist
D. E. Shaw Research
For more information please contact Dr. George Biros at gbrios@cc.gatech.edu
"Accelerating Parallel Analysis of Scientific Simulation Data via Zazen"
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.
Bio:
Tiankai Tu is a computer scientist at D. E. Shaw Research, where he architects and implements parallel software systems for analyzing very long molecular dynamics trajectories. He is also a visiting scientist at the University of Texas at Austin, developing parallel adaptive mesh refinement (AMR) algorithms for simulating global mantle convection on petascale computers. Tiankai earned a Ph.D. in Computer Science from Carnegie Mellon University, where he developed computational database systems and parallel algorithms for simulating earthquake ground motion on terascale systems.
He received the Gordon Bell Award for Special Achievement in 2003, the SC06 HPC Analytics Challenge Award in 2006, the TeraGrid Capability Computing Challenge Award in 2008, and the SC09 Best Poster Award in 2009. He was also a finalist for the SC06 Best Student Paper Award, the SC08 Best Technical Paper Award, and the 2008 Gordon Bell Award for Special Achievement.
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