Fair Cache Sharing and Partitioning in a Chip Multiprocessor Architecture

  • Authors:

  • Seongbeom Kim;Dhruba Chandra;Yan Solihin

  • Affiliations:

  • North Carolina State University;North Carolina State University;North Carolina State University

  • Venue:
  • Proceedings of the 13th International Conference on Parallel Architectures and Compilation Techniques
  • Year:
  • 2004

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Abstract

This paper presents a detailed study of fairness in cache sharing between threads in a chip multiprocessor (CMP) architecture. Prior work in CMP architectures has only studied throughput optimization techniques for a shared cache. The issue of fairness in cache sharing, and its relation to throughput, has not been studied. Fairness is a critical issue because the Operating System (OS) thread scheduler's effectiveness depends on the hardware to provide fair cache sharing to co-scheduled threads. Without such hardware, serious problems, such as thread starvation and priority inversion, can arise and render the OS scheduler ineffective. This paper makes several contributions. First, it proposes and evaluates five cache fairness metrics that measure the degree of fairness in cache sharing, and shows that two of them correlate very strongly with the execution-time fairness. Execution-time fairness is defined as how uniform the execution times of co-scheduled threads are changed, where each change is relative to the execution time of the same thread running alone. Secondly, using the metrics, the paper proposes static and dynamic L2 cache partitioning algorithms that optimize fairness. The dynamic partitioning algorithm is easy to implement, requires little or no profiling, has low overhead, and does not restrict the cache replacement algorithm to LRU. The static algorithm, although requiring the cache to maintain LRU stack information, can help the OS thread scheduler to avoid cache thrashing. Finally, this paper studies the relationship between fairness and throughput in detail. We found that optimizing fairness usually increases throughput, while maximizing throughput does not necessarily improve fairness. Using a set of co-scheduled pairs of benchmarks, on average our algorithms improve fairness by a factor of 4x, while increasing the throughput by 15%, compared to a non-partitioned shared cache.