A Survey on Performance Modeling and Prediction for Distributed DNN Training

IF 5.6 2区计算机科学 Q1 COMPUTER SCIENCE, THEORY & METHODS

IEEE Transactions on Parallel and Distributed Systems Pub Date : 2024-10-07 DOI:10.1109/TPDS.2024.3476390

Zhenhua Guo;Yinan Tang;Jidong Zhai;Tongtong Yuan;Jian Jin;Li Wang;Yaqian Zhao;Rengang Li

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引用次数: 0

Abstract

The recent breakthroughs in large-scale DNN attract significant attention from both academia and industry toward distributed DNN training techniques. Due to the time-consuming and expensive execution process of large-scale distributed DNN training, it is crucial to model and predict the performance of distributed DNN training before its actual deployment, in order to optimize the design of distributed DNN training at low cost. This paper analyzes and emphasizes the importance of modeling and predicting the performance of distributed DNN training, categorizes and analyses the related state-of-the-art works, and discusses future challenges and opportunities for this research field. The objectives of this paper are twofold: first, to assist researchers in understanding and choosing suitable modeling and prediction tools for large-scale distributed DNN training, and second, to encourage researchers to propose more valuable research about performance modeling and prediction for distributed DNN training in the future.

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分布式 DNN 训练的性能建模和预测调查

近年来，大规模 DNN 取得了突破性进展，吸引了学术界和工业界对分布式 DNN 训练技术的极大关注。由于大规模分布式 DNN 训练的执行过程耗时长、成本高，因此在实际部署前对分布式 DNN 训练的性能进行建模和预测，对于低成本优化分布式 DNN 训练的设计至关重要。本文分析并强调了分布式 DNN 训练建模和性能预测的重要性，对相关的最新研究成果进行了归类和分析，并探讨了该研究领域未来的挑战和机遇。本文的目的有二：一是帮助研究人员了解和选择适合大规模分布式 DNN 训练的建模和预测工具；二是鼓励研究人员在未来提出更多有价值的分布式 DNN 训练性能建模和预测研究。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

IEEE Transactions on Parallel and Distributed Systems 工程技术-工程：电子与电气

CiteScore

11.00

自引率

9.40%

发文量

281

审稿时长

5.6 months

期刊介绍： IEEE Transactions on Parallel and Distributed Systems (TPDS) is published monthly. It publishes a range of papers, comments on previously published papers, and survey articles that deal with the parallel and distributed systems research areas of current importance to our readers. Particular areas of interest include, but are not limited to: a) Parallel and distributed algorithms, focusing on topics such as: models of computation; numerical, combinatorial, and data-intensive parallel algorithms, scalability of algorithms and data structures for parallel and distributed systems, communication and synchronization protocols, network algorithms, scheduling, and load balancing. b) Applications of parallel and distributed computing, including computational and data-enabled science and engineering, big data applications, parallel crowd sourcing, large-scale social network analysis, management of big data, cloud and grid computing, scientific and biomedical applications, mobile computing, and cyber-physical systems. c) Parallel and distributed architectures, including architectures for instruction-level and thread-level parallelism; design, analysis, implementation, fault resilience and performance measurements of multiple-processor systems; multicore processors, heterogeneous many-core systems; petascale and exascale systems designs; novel big data architectures; special purpose architectures, including graphics processors, signal processors, network processors, media accelerators, and other special purpose processors and accelerators; impact of technology on architecture; network and interconnect architectures; parallel I/O and storage systems; architecture of the memory hierarchy; power-efficient and green computing architectures; dependable architectures; and performance modeling and evaluation. d) Parallel and distributed software, including parallel and multicore programming languages and compilers, runtime systems, operating systems, Internet computing and web services, resource management including green computing, middleware for grids, clouds, and data centers, libraries, performance modeling and evaluation, parallel programming paradigms, and programming environments and tools.