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Efficient Cache Modeling with Sparse Data

  • Erik Hagersten
  • David Eklöv
  • David Black-Schaffer
Chapter

Abstract

Obtaining good application performance requires tuning for effective use of the cache hierarchy. However, most tools to analyze cache usage either generate architecture-specific results (e.g., hardware performance counters) or incur prohibitively high overheads for real-world workloads (e.g., trace-based simulations). This chapter reviews several recently introduced techniques that address these issues to efficiently model cache systems and coherent memory hierarchies in an architecturally independent manner. The techniques utilize only sparse, architecturally independent runtime information that can be collected with an overhead of 10–30%. This information is then processed by statistical models to quickly predict cache behavior across a range of architectures. With these approaches, accurate modeling is possible from data sampled with ’ as low as 1 in 106 memory accesses.

Keywords

Memory Reference Cache Size Cache Line Sample Window Target Architecture 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer Science+business Media, LLC 2010

Authors and Affiliations

  • Erik Hagersten
    • 1
  • David Eklöv
    • 2
  • David Black-Schaffer
    • 2
  1. 1.Acumem ABUppsalaSweden
  2. 2.University of UppsalaUppsalaSweden

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