Coherence communication prediction in shared-memory multiprocessors

Sharing patterns in shared-memory multiprocessors are the key to performance: uniprocessor latency-tolerating techniques such as out-of-order execution and non-blocking caches have proved unable to completely hide the latency of remote memory access. Recently proposed prediction mechanisms accelerate coherence protocols by guessing where data will be used next and forwarding them to potential users before they are requested. Prior work in such shared-memory prediction schemes resulted in address-based and instruction-based predictors. Our work innovates in three areas. First, we present a taxonomy of prediction schemes that includes all previously-proposed prediction schemes in a uniform space. Second, we show how statistical techniques from epidemiological screening and polygraph testing can be applied to better measure the effectiveness of sharing prediction schemes; earlier work had reported only the ratio of incorrect predictions to correct predictions but neglected the ratio of correct predictions to actual sharing. Third, we provide simulation results of the accuracy of a practical subset of the space of schemes in our taxonomy, then analyze which components of each scheme contribute the most to prediction accuracy. Through this process, we discovered prediction schemes more accurate than those previously proposed.