Exploration of supervised machine learning techniques for runtime selection of CPU vs. GPU execution in java programs

Abstract

While multi-core CPUs and many-core GPUs are both viable platforms for parallel computing, programming models for them can impose large burdens upon programmers due to their complex and low-level APIs. Since managed languages like Java are designed to be run on multiple platforms, parallel language constructs and APIs such as Java 8 Parallel Stream APIs can enable high-level parallel programming with the promise of performance portability for mainstream (“non-ninja”) programmers. To achieve this goal, it is important for the selection of the hardware device to be automated rather than be specified by the programmer, as is done in current programming models. Due to a variety of factors affecting performance, predicting a preferable device for faster performance of individual kernels remains a difficult problem. While a prior approach uses machine learning to address this challenge, there is no comparable study on good supervised machine learning algorithms and good program features to track. In this paper, we explore (1) program features to be extracted by a compiler and (2) various machine learning techniques that improve accuracy in prediction, thereby improving performance. The results show that an appropriate selection of program features and machine learning algorithm can further improve accuracy. In particular, support vector machines (SVMs), logistic regression, and J48 decision tree are found to be reliable techniques for building accurate prediction models from just two, three, or four program features, achieving accuracies of 99.66%, 98.63%, and 98.28% respectively from 5-fold-cross-validation.