Compilation Optimization Pass Selection Using Gate Graph Attention Neural Network for Reliability Improvement

Jiang Wu,Xiankai Meng,Zhuo Zhang,Haoyu Zhang,Long Li,Jianjun Xu

doi:10.1109/access.2020.3016758

Abstract

When dealing with different programs or applications, it is necessary to select the appropriate compilation optimization pass or subsequence for the program. Machine learning is widely used as an efficient technological means of solving this problem. However, the most important problem when using machine learning is the extraction of program features. Obtaining more semantic and syntax information and complex transitions among code segments from the source code are obviously necessary in this context, and is also an area that may have been neglected by previous work. Ensuring the integrity and effectiveness of program information is key to this problem. Moreover, when performing and improving the selection, the measurement indicators are often program performance, code size, etc.; there is limited research on program reliability in this context, which requires both the longest measurement time and the most complicated measurement methods. Accordingly, this paper establishes a combined program feature extraction model and proposes a graph-based compilation optimization pass selection model that learns heuristics for program reliability. This experiment was performed using the clang compilation framework. The alternative compilation optimization pass adopts the C language standard compilation optimization passes. Compared with traditional machine learning methods, our model improves the average accuracy by between 5% and 11% in the optimization pass selection for program reliability. Our experiments also demonstrate the strong scalability of our proposed model.

Highlights

Over the past few decades, compiler developers have designed and implemented a large number of compilation optimization options in response to related needs in various complex situations
EXPERIMENTAL RESULTS In our experiments, we examine the performance of our gate graph attention neural network (GGANN) model and co-AFD graph in the compilation optimization pass selection context
Our experimental results demonstrate that our model achieves a higher accuracy on the pass selection problem, and performs better than similar neural network models and classic convolutional neural networks

Summary

INTRODUCTION

Over the past few decades, compiler developers have designed and implemented a large number of compilation optimization options in response to related needs in various complex situations. The VISTA interactive compilation system [1] uses a combination of genetic algorithms and human-assisted guidance to search for optimal compilation optimization passes; the open-source framework ‘‘OpenTuner’’ [2] uses a variety of evolutionary algorithms, including genetic algorithms, to obtain a speedup of up to 2.8 times. Researchers have begun to use machine learning algorithms to select compilation optimization sequences. To address the above problems, while taking advantage of the gate graph neural network (GGNN), we combine the GGNN model, program reliability analysis, and compilation optimization pass selection in the present research. Our literature research further suggests that our team is the first to use a GGNN-based model to implement compilation optimization pass selection for program reliability

RELATED WORK

COMBINATION GRAPH

EXPERIMENT

EXPERIMENTAL CONFIGURATION

Findings

CONCLUSION AND DISCUSSION