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Abstract : |
With the ever-widening performance gap between processors and main memory, cache memory, which is used to bridge this gap, is becoming more and more significant. Caches work well for programs that exhibit sufficient locality. Other programs, however, have reference patterns that fail to exploit the cache, thereby suffering heavily from high memory latency. In order to get high cache efficiency and achieve good program performance, efficient memory accessing behavior is necessary. In fact, for many programs, program transformations or source-code changes can radically alter memory access patterns, significantly improving cache performance. Both handtuning and compiler optimization techniques are often used to transform codes to improve cache utilization. Unfortunately, cache conflicts are difficult to predict and estimate, precluding effective transformations. Hence, effective transformations require detailed knowledge about the frequency and causes of cache misses in the code. This article describes methods for generating and solving Cache Miss Equations (CMEs) that give a detailed representation of cache behavior, including conflict misses, in loop-oriented scientific code. Implemented within the SUIF compiler framework, our approach extends traditional compiler reuse analysis to generate linear Diophantine equations that summarize each loop?s memory behavior. While solving these equations is in general difficult,, |
