Feedback-directed Memory Disambiguation Through Store Distance Analysis

Feedback-directed optimization has developed into an increasingly important tool in designing optimizing compilers. Based upon profiling, memory distance analysis has shown much promise in predicting data locality and memory dependences, and has seen use in locality based optimizations and memory disambiguation. In this paper, we apply a form of memory distance, called store distance, to the problem of memory disambiguation in out-of-order issue processors. Store distance is defined as the number of store references between a load and the previous store accessing the same memory location. By generating a representative store distance for each load instruction, we can apply a compiler/micro-architecture cooperative scheme to direct run-time load speculation. Using store distance, the processor can, in most cases, accurately determine on which specific store instruction a load depends according to its store distance annotation. Our experiments show that the proposed store distance method performs much better than the previous distance based memory disambiguation scheme, and yields a performance very close to perfect memory disambiguation. The store distance based scheme also outperforms the store set technique with a relatively small predictor space and achieves performance comparable to that of a 16K-entry store set implementation for both floating point and integer programs