A prefetch taxonomy

The growing difference between processor and main memory cycle time demands the use of aggressive prefetch algorithms to reduce the effective memory access latency. However, prefetching can significantly increase memory traffic and unsuccessful prefetches may pollute the cache. Metrics such as coverage and accuracy result from a simplistic classification of individual prefetches as "good" or "bad." They do not capture the full effect of each prefetch and, hence, do not accurately reflect the quality of the prefetch algorithm. Gross statistics such as changes in the number of misses, total traffic, and IPC are not attributable to individual prefetches. Such gross metrics are therefore useful only for ranking existing prefetch algorithms; they do not evaluate the effect of individual prefetches so that an algorithm might be tuned. We introduce a new, accurate, and complete taxonomy, called the Prefetch Traffic and Miss Taxonomy (PTMT), for classifying each prefetch by precisely accounting for the difference in traffic and misses it generates, either directly or indirectly. We illustrate the use of PTMT by evaluating two data prefetch algorithms.