Memory sharing predictor: the key to a speculative coherent DSM

Abstract

Recent research advocates using general message predictors to learn and predict the coherence activity in distributed shared memory (DSM). By accurately predicting a message and timely invoking the necessary coherence actions, a DSM can hide much of the remote access latency. This paper proposes the Memory Sharing Predictors (MSPs), pattern-based predictors that significantly improve prediction accuracy and implementation cost over general message predictors. An MSP is based on the key observation that to hide the remote access latency, a predictor must accurately predict only the remote memory accesses (i.e., request messages) and not the subsequent coherence messages invoked by an access. Simulation results indicate that MSPs improve prediction accuracy over general message predictors from 81% to 93% while requiring less storage overhead. This paper also presents the first design and evaluation for a speculative coherent DSM using pattern-based predictors. We identify simple techniques and mechanisms to trigger prediction timely and perform speculation for remote read accesses. Our speculation hardware readily works with a conventional full-map write-invalidate coherence protocol without any modifications. Simulation results indicate that performing speculative read requests alone reduces execution times by 12% in our shared-memory applications.