Multiple Page Size Modeling and Optimization

  • Authors:

  • Calin Cascaval;Evelyn Duesterwald;Peter F. Sweeney;Robert W. Wisniewski

  • Affiliations:

  • IBM T.J. Watson Research Center;IBM T.J. Watson Research Center;IBM T.J. Watson Research Center;IBM T.J. Watson Research Center

  • Venue:
  • Proceedings of the 14th International Conference on Parallel Architectures and Compilation Techniques
  • Year:
  • 2005

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Abstract

With the growing awareness that individual hardware cores will not continue to produce the same level of performance improvement, there is a need to develop an integrated approach to performance optimization. In this paper we present a paradigm for Continuous Program Optimization (CPO), whereby automatic agents monitor and optimize application and system performance. The monitoring data is used to analyze and create models of application and system behavior. Using this analysis, we describe how CPO agents can improve the performance of both the application and the underlying system. Using the CPO paradigm, we implemented cooperating page size optimization agents that automatically optimize large page usage. An of fine agent uses vertically integrated performance data to produce a page size benefitanalysis for different categories of data structures within an application. We show how an online CPO agent can use the results of the predictive analysis to automatically improve application performance. We validate that the predictions made by the CPO agent reflectthe actual performance gains of up to 60% across a range of scientific applications including the SPECcpu2000 floating point benchmarks and two large high performance computing (HPC) applications.