HeterPS: Distributed Deep Learning With Reinforcement Learning Based Scheduling in Heterogeneous Environments

2021/11/20 by Ji Liu, Zhihua Wu, Liu, Ji +15 · 5 citations
Computer Science · #Advanced Neural Network Applications #Artificial Intelligence (cs.AI) #Distributed #FOS: Computer and information sciences #FOS: Electrical engineering #Machine Learning (cs.LG) #Parallel #Privacy-Preserving Technologies in Data #Stochastic Gradient Optimization Techniques #Systems and Control (eess.SY) #and Cluster Computing (cs.DC) #electronic engineering #information engineering

paper · pdf · doi:10.48550/arxiv.2111.10635

openalex publication_date 2021/11/20 · openalex created_date 2021/12/06 · openalex updated_date 2026/07/28

Abstract

Deep neural networks (DNNs) exploit many layers and a large number of parameters to achieve excellent performance. The training process of DNN models generally handles large-scale input data with many sparse features, which incurs high Input/Output (IO) cost, while some layers are compute-intensive. The training process generally exploits distributed computing resources to reduce training time. In addition, heterogeneous computing resources, e.g., CPUs, GPUs of multiple types, are available for the distributed training process. Thus, the scheduling of multiple layers to diverse computing resources is critical for the training process. To efficiently train a DNN model using the heterogeneous computing resources, we propose a distributed framework, i.e., Paddle-Heterogeneous Parameter Server (Paddle-HeterPS), composed of a distributed architecture and a Reinforcement Learning (RL)-based scheduling method. The advantages of Paddle-HeterPS are three-fold compared with existing frameworks. First, Paddle-HeterPS enables efficient training process of diverse workloads with heterogeneous computing resources. Second, Paddle-HeterPS exploits an RL-based method to efficiently schedule the workload of each layer to appropriate computing resources to minimize the cost while satisfying throughput constraints. Third, Paddle-HeterPS manages data storage and data communication among distributed computing resources. We carry out extensive experiments to show that Paddle-HeterPS significantly outperforms state-of-the-art approaches in terms of throughput (14.5 times higher) and monetary cost (312.3% smaller). The codes of the framework are publicly available at: https://github.com/PaddlePaddle/Paddle.

Citations

• Neural Architecture Search with Reinforcement Learning

Cited by

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• Efficient Federated Learning with Heterogeneous Data and Adaptive Dropout
• Performance Characterization of Distributed Deep Learning Strategies: A Quantitative Evaluation of DDP, FSDP, and Parameter Server Architectures on GPU Clusters
• SGDPO: Self-Guided Direct Preference Optimization for Language Model Alignment

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