GoldRush: resource efficient in situ scientific data analytics using fine-grained interference aware execution

  • Authors:

  • Fang Zheng;Hongfeng Yu;Can Hantas;Matthew Wolf;Greg Eisenhauer;Karsten Schwan;Hasan Abbasi;Scott Klasky

  • Affiliations:

  • Georgia Institute of Technology;University of Nebraska Lincoln;Georgia Institute of Technology;Georgia Institute of Technology and Oak Ridge National Laboratory;Georgia Institute of Technology;Georgia Institute of Technology;Oak Ridge National Laboratory;Oak Ridge National Laboratory

  • Venue:
  • SC '13 Proceedings of the International Conference on High Performance Computing, Networking, Storage and Analysis
  • Year:
  • 2013

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Abstract

Severe I/O bottlenecks on High End Computing platforms call for running data analytics in situ. Demonstrating that there exist considerable resources in compute nodes un-used by typical high end scientific simulations, we leverage this fact by creating an agile runtime, termed GoldRush, that can harvest those otherwise wasted, idle resources to efficiently run in situ data analytics. GoldRush uses fine-grained scheduling to "steal" idle resources, in ways that minimize interference between the simulation and in situ analytics. This involves recognizing the potential causes of on-node resource contention and then using scheduling methods that prevent them. Experiments with representative science applications at large scales show that resources harvested on compute nodes can be leveraged to perform useful analytics, significantly improving resource efficiency, reducing data movement costs incurred by alternate solutions, and posing negligible impact on scientific simulations.