IPERF - A Framework for Intelligent Performance Prediction

Project Description

Advanced optimizing compilers perform multiple optimization transformation on program representations with decreasing levels of program abstraction, ranging from source-to-source transformations (e.g. loop interchange), transformations on intermediate program representations (e.g. common subexpression elimination on quadruples code), to transformations on a program representation close to the target architecture object code (e.g. peephole optimizations). For each transformation, a performance prediction model is needed to determine whether the transformation should be applied and, in the case of a set of transformation alternatives, which alternative should be chosen. In addition to this accuracy requirement, a performance model has to satisfy efficiency constraints due to compile time bounds that are considered acceptable in different compilation environments (e.g. static vs. dynamic optimizing compilers). The thesis of this project is that finding a performance model with a required accuracy/cost tradeoff for a given prediction task and level of program abstraction is difficult, and current approaches to the problem are ad-hoc and unsatisfactory because they largely consist of trial-and-error strategies without any user support to systematically guide the search process. What is needed is a system that produces performance models automatically for a specified accuracy/cost tradeoff. The goals of this project are as follows:


  • C-H. Hsu and U. Kremer. A Stable and Efficient Loop Tiling Algorithm .
    Technical Report LCSR-TR407, Department of Computer Science, Rutgers University, December 1999.

  • C-H. Hsu and U. Kremer. Tile Selection Algorithms and Their Performance Models.
    Technical Report LCSR-TR401, Department of Computer Science, Rutgers University, October 1999.

  • C-H. Hsu and U. Kremer. IPERF: A Framework for Automatic Construction of Performance Prediction Models .
    Workshop on Profile and Feedback-Directed Compilation (PFDC), Paris, France, October 1998.

  • C-H. Hsu and U. Kremer. A Framework for Qualitative Performance Prediction}.
    Technical Report LCSR-TR363, Department of Computer Science, Rutgers University, July 1998.