AutoDDL: Automatic Distributed Deep Learning With Near-Optimal Bandwidth Cost

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

IEEE Transactions on Parallel and Distributed Systems Pub Date : 2024-03-07 DOI:10.1109/TPDS.2024.3397800

Jinfan Chen;Shigang Li;Ran Guo;Jinhui Yuan;Torsten Hoefler

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Abstract

Recent advances in deep learning are driven by the growing scale of computation, data, and models. However, efficiently training large-scale models on distributed systems requires an intricate combination of data, operator, and pipeline parallelism, which exerts heavy burden on machine learning practitioners. To this end, we propose AutoDDL, a distributed training framework that automatically explores and exploits new parallelization schemes with near-optimal bandwidth cost. AutoDDL facilitates the description and implementation of different schemes by utilizing OneFlow's Split , Broadcast , and Partial Sum (SBP) abstraction. AutoDDL is equipped with an analytical performance model combined with a customized Coordinate Descent algorithm, which significantly reduces the scheme searching overhead. We conduct evaluations on Multi-Node-Single-GPU and Multi-Node-Multi-GPU machines using different models, including VGG and Transformer. Compared to the expert-optimized implementations, AutoDDL reduces the end-to-end training time by up to 31.1% and 10% for Transformer and up to 17.7% and 71.5% for VGG on the two parallel systems, respectively.

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AutoDDL：带宽成本接近最优的自动分布式深度学习

计算、数据和模型规模的不断扩大推动了深度学习的最新进展。然而，在分布式系统上高效训练大规模模型需要数据、运算器和管道并行性的复杂组合，这给机器学习从业者带来了沉重的负担。为此，我们提出了分布式训练框架 AutoDDL，它能以接近最优的带宽成本自动探索和利用新的并行化方案。AutoDDL 利用 OneFlow 的拆分、广播和部分求和（SBP）抽象，为不同方案的描述和实施提供了便利。AutoDDL 配备了分析性能模型和定制的坐标下降算法，可显著降低方案搜索开销。我们使用不同的模型（包括 VGG 和 Transformer）在多节点-单 GPU 和多节点-多 GPU 机器上进行了评估。与专家优化的实现相比，AutoDDL 在两个并行系统上的端到端训练时间分别缩短了 Transformer 的 31.1% 和 10%，VGG 的 17.7% 和 71.5%。

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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.