Semantic locality and context-based prefetching using reinforcement learning

Abstract

Most modern memory prefetchers rely on spatio-temporal locality to predict the memory addresses likely to be accessed by a program in the near future. Emerging workloads, however, make increasing use of irregular data structures, and thus exhibit a lower degree of spatial locality. This makes them less amenable to spatio-temporal prefetchers.