Scalability-based manycore partitioning

  • Authors:

  • Hiroshi Sasaki;Teruo Tanimoto;Koji Inoue;Hiroshi Nakamura

  • Affiliations:

  • Kyushu University, Fukuoka, Japan;Fujitsu Laboratories Limited, Kawasaki, Japan;Kyushu University, Fukuoka, Japan;The University of Tokyo, Tokyo, Japan

  • Venue:
  • Proceedings of the 21st international conference on Parallel architectures and compilation techniques
  • Year:
  • 2012

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Abstract

Multicore processors have been popular for years, and the industry is gradually shifting towards the era of manycore processors. Single-thread performance of microprocessors is not growing at a historical rate, but the existence of a number of active processes in the computer system and the continuing development of multi-threaded applications benefit from the growing core counts to sustain system throughput. This trend brings us a situation where a number of parallel applications simultaneously being executed on a single system. Since multi-threaded applications try to maximize its throughput by utilizing the whole system, each of them usually create equal or larger number of threads compared to underlying logical core counts. This introduces much greater number of threads to be co-scheduled in the entire system. However, each program has different characteristics (or scalability) and contends for shared resources, which are the CPU cores and memory hierarchies, with each other. Therefore, it is clear that OS thread scheduling will play a major role in achieving high system performance under such conditions. We develop a sophisticated scheduler that (1) dynamically predicts the scalability of programs via the use of hardware performance monitoring units, (2) decides the optimal number of cores to be allocated for each program, and (3) allocates the cores to programs while maximizing the system utilization to achieve fair and maximum performance. The evaluation results on a 48-core AMD Opteron system show improvements over the Linux scheduler for a variety of multiprogramming workloads.