Learning Intermediate Representations using Graph Neural Networks for NUMA and Prefetchers Optimization

Abstract

There is a large space of NUMA and hardware prefetcher configurations that can significantly impact the performance of an application. Previous studies have demonstrated how a model can automatically select configurations based on the dynamic properties of the code to achieve speedups. This paper demonstrates how the static Intermediate Representation (IR) of the code can guide NUMA/prefetcher optimizations without the prohibitive cost of performance profiling. We propose a method to create a comprehensive dataset that includes a diverse set of intermediate representations along with optimum configurations. We then apply a graph neural network model in order to validate this dataset. We show that our static intermediate representation based model achieves 80% of the performance gains provided by expensive dynamic performance profiling based strategies. We further develop a hybrid model that uses both static and dynamic information. Our hybrid model achieves the same gains as the dynamic models but at a reduced cost by only profiling 30% of the programs.

Heterogeneous Graph Neural Networks for Software Effort Estimation

“…Aravind et al [19] propose a GNN model to detect the similarity between two input programs. Tehrani et al [33] applied Relational Graph Convolutional Networks (RGCN) model for predicting optimum NUMA/prefetcher configurations of C/C++ programs. In future, applying techniques on optimization of GNN models [38,39] can help increasing the performance and accuracy in solving SE problems.…”