تقرير
Expediting Distributed DNN Training with Device Topology-Aware Graph Deployment
| العنوان: | Expediting Distributed DNN Training with Device Topology-Aware Graph Deployment |
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| المؤلفون: | Zhang, Shiwei, Yi, Xiaodong, Diao, Lansong, Wu, Chuan, Wang, Siyu, Lin, Wei |
| سنة النشر: | 2023 |
| المجموعة: | Computer Science |
| مصطلحات موضوعية: | Computer Science - Machine Learning, Computer Science - Distributed, Parallel, and Cluster Computing |
| الوصف: | This paper presents TAG, an automatic system to derive optimized DNN training graph and its deployment onto any device topology, for expedited training in device- and topology- heterogeneous ML clusters. We novelly combine both the DNN computation graph and the device topology graph as input to a graph neural network (GNN), and join the GNN with a search-based method to quickly identify optimized distributed training strategies. To reduce communication in a heterogeneous cluster, we further explore a lossless gradient compression technique and solve a combinatorial optimization problem to automatically apply the technique for training time minimization. We evaluate TAG with various representative DNN models and device topologies, showing that it can achieve up to 4.56x training speed-up as compared to existing schemes. TAG can produce efficient deployment strategies for both unseen DNN models and unseen device topologies, without heavy fine-tuning. Comment: Accepted by IEEE Transactions on Parallel and Distributed Systems (TPDS) 2023 |
| نوع الوثيقة: | Working Paper |
| URL الوصول: | http://arxiv.org/abs/2302.06126 |
| رقم الأكسشن: | edsarx.2302.06126 |
| قاعدة البيانات: | arXiv |
| الوصف غير متاح. |