Data prefetching for software DSMs

Abstract

In this paper we propose and evaluate the Adaptive++ technique, a novel runtime-only data prefetching strategy for software-based distributed shared-memory systems (software DSMs). Adaptive++ improves the performance of regular parallel applications running on software DSMs by using the past history of memory access faults to adapt between repeated-phase and repeated-stride prefetching modes. Adaptive++ does not issue prefetches during periods when the application is not exhibiting one of these two types of behavior and is thus behaving irregularly. Through detailed execution-driven simulations of several applications, we show that our prefetching technique is very successful at reducing the data access overheads of regular applications running on the TreadMarks software DSM. Adaptive++ also reduces the overhead of applications that are not strictly regular but that exhibit periods of regularity. In terms of overall performance, our results show that Adaptive++ can provide speedup improvements as significant as 34% on 16 processors. A direct comparison against two runtime-only prefetching techniques proposed thus far shows that Adaptive++ is consistently competitive in terms of performance, while being able to optimize a larger set of applications. Our main conclusion is that Adaptive++ should definitely be considered by software DSM designers as an effective way of tolerating the overhead of remote data accesses.