تقرير
GNNavigator: Towards Adaptive Training of Graph Neural Networks via Automatic Guideline Exploration
| العنوان: | GNNavigator: Towards Adaptive Training of Graph Neural Networks via Automatic Guideline Exploration |
|---|---|
| المؤلفون: | Qiao, Tong, Yang, Jianlei, Qi, Yingjie, Zhou, Ao, Bai, Chen, Yu, Bei, Zhao, Weisheng, Hu, Chunming |
| سنة النشر: | 2024 |
| المجموعة: | Computer Science |
| مصطلحات موضوعية: | Computer Science - Machine Learning, Computer Science - Artificial Intelligence |
| الوصف: | Graph Neural Networks (GNNs) succeed significantly in many applications recently. However, balancing GNNs training runtime cost, memory consumption, and attainable accuracy for various applications is non-trivial. Previous training methodologies suffer from inferior adaptability and lack a unified training optimization solution. To address the problem, this work proposes GNNavigator, an adaptive GNN training configuration optimization framework. GNNavigator meets diverse GNN application requirements due to our unified software-hardware co-abstraction, proposed GNNs training performance model, and practical design space exploration solution. Experimental results show that GNNavigator can achieve up to 3.1x speedup and 44.9% peak memory reduction with comparable accuracy to state-of-the-art approaches. Comment: Accepted by DAC'24 |
| نوع الوثيقة: | Working Paper |
| URL الوصول: | http://arxiv.org/abs/2404.09544 |
| رقم الأكسشن: | edsarx.2404.09544 |
| قاعدة البيانات: | arXiv |
| الوصف غير متاح. |