TY - JOUR
T1 - Data dependence profiling for speculative optimizations
AU - Chen, Tong
AU - Lin, Jin
AU - Dai, Xiaoru
AU - Hsu, Wei Chung
AU - Yew, Pen Chung
PY - 2004/12/1
Y1 - 2004/12/1
N2 - 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.
AB - 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.
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M3 - Article
AN - SCOPUS:35048884679
SN - 0302-9743
VL - 2985
SP - 57
EP - 72
JO - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
JF - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
ER -