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

@article{Zheng2013GoldRushRE,
  title={GoldRush: Resource efficient in situ scientific data analytics using fine-grained interference aware execution},
  author={Fang Zheng and Hongfeng Yu and Can Hantas and Matthew Wolf and Greg Eisenhauer and Karsten Schwan and Hasan Abbasi and Scott Klasky},
  journal={2013 SC - International Conference for High Performance Computing, Networking, Storage and Analysis (SC)},
  year={2013},
  pages={1-12}
}
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… CONTINUE READING
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