Data dependence profiling for speculative optimizations

Tong Chen, Jin Lin, Xiaoru Dai, Wei Chung Hsu, Pen Chung Yew

Research output: Contribution to journalArticlepeer-review

32 Scopus citations

Abstract

Data dependence analysis is the foundation to many reordering related compiler optimizations and loop parallelization. Traditional data dependence analysis algorithms are developed primarily for Fortran-like subscripted array variables. They are not very effective for pointer-based references in C or C++. With more advanced hardware support for speculative execution, such as the advanced load instructions in Intel's IA64 architecture, some data dependences with low probability can be speculatively ignored. However, such speculative optimizations must be carefully applied to avoid excessive cost associated with potential mis-speculations. Data dependence profiling is one way to provide probabilistic information on data dependences to guide such speculative optimizations and speculative thread generation. Software-based data dependence profiling requires detailed tracing of memory accesses, therefore, could be very time consuming. In this paper, we examine issues related to data dependence profiling, and propose various techniques to improve the efficiency of data dependence profiling. We use the Open Research Compiler (ORC) [15,16] to test the efficiency of our data profiling techniques. We also study the effectiveness of data dependence profiling on data speculative optimizations on Itanium systems. Our results show that efficient data dependence profiling could improve the performance for data speculative optimizations.

Original languageEnglish (US)
Pages (from-to)57-72
Number of pages16
JournalLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume2985
StatePublished - Dec 1 2004

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