Perceptron Based Consumer Prediction in Shared-Memory Multiprocessors

Recent research has shown that forwarding speculative data to other processors before it is requested can improve the performance of multiprocessor systems. The most recent work in speculative data forwarding places all of the processors on a single bus, allowing the data to be forwarded to all of the processors at the same cost as any subset of the processors. Modern multiprocessors however often employ more complex switching networks in which broadcast is expensive. Accurately predicting the consumers of data can be challenging, especially in the case of programs with many shared data structures. Past consumer predictors rely on simple prediction mechanisms, a single table lookup followed by a static mapping of the table values onto a prediction. We make two main contributions in this paper. First, we show how to reduce the design space of consumer predictors to a set of interesting predictors, and how previous consumer predictors can be tuned to expand the range of available performance. Second, we propose a perceptron consumer predictor that dynamically adapts its reaction to the system behavior, and uses more history information than previous consumer predictors. This predictor outperforms the previous predictors by 21% while using only 1KByte more storage than previous predictors.