A Machine Learning Methodology for Cache Recommendation

Cache memories are an important component of modern processors and consume a large percentage of the processor's power consumption. The quality of service of this cache memories relies heavily on the memory demands of the software, what means that a certain program might benefit more from a certain cache configuration which is highly inefficient for another program. Moreover, finding the optimal cache configuration for a certain program is not a trivial task and usually, involves exhaustive simulation. In this paper, we propose a machine learning-based methodology that, given an unknown application as input, it outputs a prediction of the optimal cache reconfiguration for that application, regarding energy consumption and performance. We evaluated our methodology using a large benchmark suite, and our results show a 99.8% precision at predicting the optimal cache configuration for a program. Furthermore, further analysis of the results indicates that 85% of the mispredictions produce only up to a 10% increase in energy consumption in comparison to the optimal energy consumption.