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InArt: In-Network Aggregation with Route Selection for Accelerating Distributed Training

Published: 13 May 2024 Publication History

Abstract

Deep learning has brought about a revolutionary transformation in network applications, particularly in domains like e-commerce and online advertising. Distributed training (DT), as a critical means to expedite model training, has progressively emerged as a key foundational infrastructure for such applications. However, with the rapid advancement of hardware accelerators, the performance bottleneck in DT has shifted from computation to communication. In-network aggregation (INA) solutions have shown promise in alleviating the communication bottleneck. Regrettably, current INA solutions primarily focus on improving efficiency under the traditional parameter server (PS) architecture and do not fully address the communication bottleneck caused by limited PS ingress bandwidth. To bridge this gap, we propose InArt, the first work to introduce INA with routing selection in a multi-PS architecture. InArt employs a multi-PS architecture to split DT tasks among multiple PSs, and selects appropriate routing schemes to fully harness INA capabilities. To accommodate traffic dynamics, InArt adopts a two-phase approach: splitting the training model among multiple parameter servers and selecting routing paths for INA. We propose Lagrange multiplier and randomized rounding algorithms for these phases, respectively. We implement InArt and evaluate its performance through experiments on physical platforms (Tofino switches) and Mininet emulation (P4 Software Switches). Experimental results show that InArt can reduce communication time by 48%\!\sim57\!% compared with state-of-the-art solutions.

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      cover image ACM Conferences
      WWW '24: Proceedings of the ACM Web Conference 2024
      May 2024
      4826 pages
      ISBN:9798400701719
      DOI:10.1145/3589334
      Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected].

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      Published: 13 May 2024

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      Author Tags

      1. distributed training
      2. in-network aggregation
      3. programmable switches
      4. route selection
      5. web infrastructure

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      • the National Science Foundation of China (NSFC)
      • the National Science Foundation of China (NSFC)
      • the National Science Foundation of China (NSFC)
      • the National Science Foundation of Jiangsu Province
      • the Open Research Projects of Zhejiang Lab
      • the Fundamental Research Funds for the Central Universities
      • the Youth Innovation Promotion Association of the Chinese Academy of Science

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      WWW '24
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      WWW '24: The ACM Web Conference 2024
      May 13 - 17, 2024
      Singapore, Singapore

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