The Potential of Computation Regrouping for Improving Locality

Chen Ding and Maks Orlovich


Improving program locality has become increasingly important on modern computer systems. An effective strategy is to group computations on the same data so that once the data are loaded into cache, the program performs all their operations before the data are evicted. However, computation regrouping is difficult to automate for programs with complex data and control structures.

This paper studies the potential of locality improvement through trace-driven computation regrouping. First, it shows that maximizing the locality is different from maximizing the parallelism or maximizing the cache utilization. The problem is NP-hard even without considering data dependences and cache organization. Then the paper describes a tool that performs constrained computation regrouping on program traces. The new tool is unique because it measures the exact control dependences and applies complete memory renaming and re-allocation. Using the tool, the paper measures the potential locality improvement in a set of commonly used benchmark programs written in C.

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