Semantic Locality and Context-based Prefetching Using Reinforcement Learning

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. In this paper, we introduce the concept of Semantic Locality, which uses inherent program semantics to characterize access relations. We show how, in principle, semantic locality can capture the relationship between data elements in a manner agnostic to the actual data layout, and we argue that semantic locality transcends spatio-temporal concerns. We further introduce the context-based memory prefetcher, which approximates semantic locality using reinforcement learning. The prefetcher identifies access patterns by applying reinforcement learning methods over machine and code attributes, that provide hints on memory access semantics. We test our prefetcher on a variety of benchmarks that employ both regular and irregular patterns. For the SPEC 2006 suite, it delivers speedups as high as 2.8x (20% on average) over a baseline with no prefetching, and outperforms leading spatio-temporal prefetchers. Finally, we show that the context-based prefetcher makes it possible for naive, pointer-based implementations of irregular algorithms to achieve performance comparable to that of spatially optimized code.