Improving the Performance of Software Distributed Shared Memory with Speculation

We study the performance benefits of speculation in a release consistent software distributed shared memory system. We propose a new protocol, Speculative Home-based Release Consistency, that speculatively updates data at remote nodes to reduce the latency of remote memory accesses. Our protocol employs a predictor that uses patterns in past accesses to shared memory to predict future accesses. We have implemented our protocol in a software distributed shared memory system that runs on commodity hardware. We evaluate our protocol implementation on a number of software distributed shared memory benchmarks and show that it can result in significant performance improvements