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Path-Based Reuse Distance Analysis

  • Changpeng Fang
  • Steve Carr
  • Soner Önder
  • Zhenlin Wang
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3923)

Abstract

Profiling can effectively analyze program behavior and provide critical information for feedback-directed or dynamic optimizations. Based on memory profiling, reuse distance analysis has shown much promise in predicting data locality for a program using inputs other than the profiled ones. Both whole-program and instruction-based locality can be accurately predicted by reuse distance analysis.

Reuse distance analysis abstracts a cluster of memory references for a particular instruction having similar reuse distance values into a locality pattern. Prior work has shown that a significant number of memory instructions have multiple locality patterns, a property not desirable for many instruction-based memory optimizations. This paper investigates the relationship between locality patterns and execution paths by analyzing reuse distance distribution along each dynamic path to an instruction. Here a path is defined as the program execution trace from the previous access of a memory location to the current access. By differentiating locality patterns with the context of execution paths, the proposed analysis can expose optimization opportunities tailored only to a specific subset of paths leading to an instruction.

In this paper, we present an effective method for path-based reuse distance profiling and analysis. We have observed that a significant percentage of the multiple locality patterns for an instruction can be uniquely related to a particular execution path in the program. In addition, we have also investigated the influence of inputs on reuse distance distribution for each path/instruction pair. The experimental results show that the path-based reuse distance is highly predictable, as a function of the data size, for a set of SPEC CPU2000 programs.

Keywords

Integer Program Locality Pattern Memory Location Cache Line Execution Path 
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-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Changpeng Fang
    • 1
  • Steve Carr
    • 2
  • Soner Önder
    • 2
  • Zhenlin Wang
    • 2
  1. 1.PathScale, Inc.Mountain ViewUSA
  2. 2.Department of Computer ScienceMichigan Technological UniversityHoughtonUSA

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