Improving Large-scale Storage System Performance via Topology-aware and Balanced Data Placement
With the advent of big data, the I/O subsystems of large-scale compute clusters are becoming a center of focus, with more applications putting greater demands on end-to-end I/O performance. These subsystems are often complex in design. They comprise of multiple hardware and software layers to cope with the increasing capacity, capability and scalability requirements of data intensive applications. The sharing nature of storage resources and the intrinsic interactions across these layers make it to realize user-level, end-to-end performance gains a great challenge. We propose a topology-aware resource load balancing strategy to improve per-application I/O performance. We demonstrate the effectiveness of our algorithm on an extreme-scale compute cluster, Titan, at the Oak Ridge Leadership Computing Facility (OLCF). Our experiments with both synthetic benchmarks and a real-world application show that, even under congestion, our proposed algorithm can improve large-scale application I/O performance significantly, resulting in both the reduction of application run times and higher resolution simulation runs.
- Publication Date:
- OSTI Identifier:
- DOE Contract Number:
- Resource Type:
- Resource Relation:
- Conference: The 20th IEEE International Conference on Parallel and Distributed Systems (ICPADS 2014), Hsinchu, Taiwan, 20141216, 20141219
- Research Org:
- Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States). Oak Ridge Leadership Computing Facility (OLCF)
- Sponsoring Org:
- USDOE Office of Science (SC)
- Country of Publication:
- United States
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