SC13 Home > SC13 Schedule > SC13 Presentation - GoldRush: Resource Efficient In Situ Scientific Data Analytics Using Fine-Grained Interference Aware Execution

SCHEDULE: NOV 16-22, 2013

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GoldRush: Resource Efficient In Situ Scientific Data Analytics Using Fine-Grained Interference Aware Execution

SESSION: In-Situ Data Analytics and Reduction

EVENT TYPE: Papers

TIME: 2:30PM - 3:00PM

SESSION CHAIR: Dimitris Nikolopoulos

AUTHOR(S):Fang Zheng, Hongfeng Yu, Can Hantas, Matthew Wolf, Greg Eisenhauer, Karsten Schwan, Hasan Abbasi, Scott Klasky

ROOM:205/207

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 unused 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.

Chair/Author Details:

Dimitris Nikolopoulos (Chair) - Queen's University Belfast

Fang Zheng - Georgia Institute of Technology

Hongfeng Yu - University of Nebraska-Lincoln

Can Hantas - Georgia Institute of Technology

Matthew Wolf - Georgia Institute of Technology

Greg Eisenhauer - Georgia Institute of Technology

Karsten Schwan - Georgia Institute of Technology

Hasan Abbasi - Oak Ridge National Laboratory

Scott Klasky - Oak Ridge National Laboratory

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The full paper can be found in the ACM Digital Library