Instruction based memory distance analysis and its application to optimization

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 memory hierarchy optimization. In this we examine the notion of memory distance as it is applied to the instruction space of a program and to 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-set of high miss instructions whose cumulative misses account for 95 % of the L2 cache misses in the program-in both integer andfloating-point pmgrams. Our experimentsshow that distance analysis can effectively identifi critical instructions in both integer programs. 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 of the performance gain of the store set technique which requires hardware table. 1