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Conference Paper
이종 GPU 클러스터를 위한 분산 딥러닝 학습 플랫폼
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- Authors
- 안신영
- Issue Date
- 2023-06
- Citation
- 대한전자공학회 학술 대회 (하계) 2023, pp.2694-2697
- Publisher
- 대한전자공학회
- Language
- Korean
- Type
- Conference Paper
- Abstract
- As artificial intelligence technology matures, the competition for developing DNNs with higher accuracy has become increasingly intense. To achieve higher performance, the size of DNNs is increasing, leading to a significant increase in the time and cost of development. In this paper, we propose EDDIS, a distributed DNN training platform that integrates heterogeneous GPU resources to provide high-speed distributed training to support faster DNN training. EDDIS provides a methodology for modifying existing Tensorflow/PyTorch codes to enable distributed training and also offers an asynchronous parameter update method to solve the straggler problem associated with synchronous parameter update methods, thus providing superior distributed training performance.
- KSP Keywords
- Artificial intelligence technology, Distributed training, High Speed, Higher performance, Straggler problem