Pattern-based Autotuning of OpenMP Loops Using Graph Neural Networks

Stagnation of Moore's law has led to the increased adoption of parallel programming for enhancing performance of scientific applications. Frequently occurring code and design patterns in scientific applications are often used for transforming serial code to parallel. But, identifying these patterns is not easy. To this end, we propose using Graph Neural Networks for modeling code flow graphs to identify patterns in such parallel code. Additionally, identifying the runtime parameters for best performing parallel code is also challenging. We propose a pattern-guided deep learning based tuning approach, to help identify the best runtime parameters for OpenMP loops. Overall, we aim to identify commonly occurring patterns in parallel loops and use these patterns to guide auto-tuning efforts. We validate our hypothesis on 20 different applications from Polybench, and STREAM benchmark suites. This deep learning-based approach can identify the considered patterns with an overall accuracy of 91%. We validate the usefulness of using patterns for auto-tuning on tuning the number of threads, scheduling policies and chunk size on a single socket system, and the thread count and affinity on a multi-socket machine. Our approach achieves geometric mean speedups of $1.1\times$ and $4.7\times$ respectively over default OpenMP configurations, compared to brute-force speedups of $1.27\times$ and $4.93\times$ respectively.