# Power Constrained Autotuning using Graph Neural Networks

arXiv: https://arxiv.org/abs/2302.11467 · HTML: https://mlsanity.com/paper/2302.11467/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 11 of its 48 references that are in the map.
- First posted on arXiv 22 Feb 2023.

## Builds on (most cited first, 11 of 11)

- [A Comprehensive Survey on Graph Neural Networks](https://mlsanity.com/paper/1901.00596/) (2019)
- [Modeling Relational Data with Graph Convolutional Networks](https://mlsanity.com/paper/1703.06103/) (2017)
- [Graph Neural Networks: A Review of Methods and Applications](https://mlsanity.com/paper/1812.08434/) (2018)
- [GraphCodeBERT: Pre-training Code Representations with Data Flow](https://mlsanity.com/paper/2009.08366/) (2020)
- [A Survey of Machine Learning for Big Code and Naturalness](https://mlsanity.com/paper/1709.06182/) (2017)
- [A Survey on Compiler Autotuning using Machine Learning](https://mlsanity.com/paper/1801.04405/) (2018)
- [MLGO: a Machine Learning Guided Compiler Optimizations Framework](https://mlsanity.com/paper/2101.04808/) (2021)
- [NeuroVectorizer: End-to-End Vectorization with Deep Reinforcement Learning](https://mlsanity.com/paper/1909.13639/) (2019)
- [Static Neural Compiler Optimization via Deep Reinforcement Learning](https://mlsanity.com/paper/2008.08951/) (2020)
- [GraphCode2Vec: Generic Code Embedding via Lexical and Program Dependence Analyses](https://mlsanity.com/paper/2112.01218/) (2021)
- [Learning Intermediate Representations using Graph Neural Networks for NUMA and Prefetchers Optimization](https://mlsanity.com/paper/2203.00611/) (2022)

## Built on by (most cited first, 2 of 2)

- [Performance Optimization using Multimodal Modeling and Heterogeneous GNN](https://mlsanity.com/paper/2304.12568/) (2023)
- [Deal: Distributed End-to-End GNN Inference for All Nodes](https://mlsanity.com/paper/2503.02960/) (2025)

## Abstract (arXiv)

Recent advances in multi and many-core processors have led to significant improvements in the performance of scientific computing applications. However, the addition of a large number of complex cores have also increased the overall power consumption, and power has become a first-order design constraint in modern processors. While we can limit power consumption by simply applying software-based power constraints, applying them blindly will lead to non-trivial performance degradation. To address the challenge of improving the performance, power, and energy efficiency of scientific applications on modern multi-core processors, we propose a novel Graph Neural Network based auto-tuning approach that (i) optimizes runtime performance at pre-defined power constraints, and (ii) simultaneously optimizes for runtime performance and energy efficiency by minimizing the energy-delay product. The key idea behind this approach lies in modeling parallel code regions as flow-aware code graphs to capture both semantic and structural code features. We demonstrate the efficacy of our approach by conducting an extensive evaluation on 30 benchmarks and proxy-/mini-applications with 68 OpenMP code regions. Our approach identifies OpenMP configurations at different power constraints that yield a geometric mean performance improvement of more than 25% and 13% over the default OpenMP configuration on a 32-core Skylake and a 16-core Haswell processor respectively. In addition, when we optimize for the energy-delay product, the OpenMP configurations selected by our auto-tuner demonstrate both performance improvement of 21% and 11% and energy reduction of 29% and 18% over the default OpenMP configuration at Thermal Design Power for the same Skylake and Haswell processors, respectively.