Predicting data structures for energy efficient computing

Abstract

Dynamic data structures in software applications have been shown to have a large impact on system performance. In this paper, we explore energy saving opportunities of interface-based dynamic data structures. Our results suggest that opportunities do exist in the C5 Collection, at least 16.95% and up to 97.50%. We propose an architecture for building adaptive green data structures by applying machine learning tools to build a model for predicting energy efficient data structures. Our neural network model can classify energy efficient data structures based on features such as the number of elements, frequency of operations, interface and set/bag semantics. The 10-fold cross validation results show 96.01% accuracy on the training data and 95.80% on the training validation data. Our n-gram model can accurately predict the most energy efficient data structure sequence in 19 simulated and real-world programs—on average, with more than 50% accuracy and up to 98% using a bigram predictor.