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Proceedings of the 22nd International Conference on Parallel Architectures and Compilation Techniques (2005)
St. Louis, Missouri
Sept. 17, 2005 to Sept. 21, 2005
ISSN: 1089-795X
ISBN: 0-7695-2429-X
pp: 27-37
Soner Onder , Department of Computer Science Michigan Technological University
Zhenlin Wang , Department of Computer Science Michigan Technological University
Steve Carr , Department of Computer Science Michigan Technological University
Changpeng Fang , Department of Computer Science Michigan Technological University
ABSTRACT
<p>Feedback-directed Optimization has become an increasingly important tool in designing and building optimizing compilers as it provides a means to analyze complex program behavior that is not possible using traditional static analysis. Feedback-directed optimization offers the compiler opportunities to analyze and optimize the memory behavior of programs even when traditional array-based analysis not applicable. As a result, both floatingpoint and integer programs can benefit memory hierarchy optimization.</p> <p>In this paper we examine the notion of memory distance as it is applied to the instruction space of a program and to feedback-quantifiable directed optimization. Memory distance is dejined as a dynamic distance in terms of memory references between two accesses to the same memory location. We use memory distance to predict the miss rates of instructions in a program. Using the miss rates, we then identifi the program?s critical instructions the set of high miss instructions whose cumulative misses account for 95% of the L2 cache misses in the program - in both integer and floating-point programs. Our experiments show that memory distance analysis can effectively identified critical instructions in both integer and floating-point programs.</p> <p>Additionally, we apply memory-distance analysis to memory disambiguation in out-of-order issue processors, using those distances to determinewhen a load may be speculated ahead of apreceding store. Our experiments show that memory-distance-based disambiguation on average achieves within 5-10% of the performance gain of the store set technique which requires hardware table.</p>
INDEX TERMS
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CITATION
Soner Onder, Zhenlin Wang, Steve Carr, Changpeng Fang, "Instruction Based Memory Distance Analysis and its Application", Proceedings of the 22nd International Conference on Parallel Architectures and Compilation Techniques, vol. 00, no. , pp. 27-37, 2005, doi:10.1109/PACT.2005.26
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