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Roar: A Router Microarchitecture for In-network Allreduce

Published: 21 June 2023 Publication History

Abstract

The allreduce operation is the most commonly used collective operation in distributed or parallel applications. It aggregates data collected from distributed hosts and broadcasts the aggregated result back to them. In-network computing can accelerate allreduce by offloading this operation into network devices. However, existing in-network solutions face the challenge of high throughput, performance of aggregating large message and producing repeatable results. In this work, we propose a simple and effective router microarchitecture for in-network allreduce, which uses an RDMA protocol to improve its throughput. We further discuss strategies to tackle the aforementioned challenges. Our approach not only shows advantages in comparison with the state-of-the-art in-network solutions, but also accelerates allreduce at a near-optimal level compared to host-based algorithms, as demonstrated through experiments.

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  • (2024)Sparse Gradient Communication with AlltoAll for Accelerating Distributed Deep LearningProceedings of the 53rd International Conference on Parallel Processing10.1145/3673038.3673140(148-157)Online publication date: 12-Aug-2024
  • (2024)PID-Comm: A Fast and Flexible Collective Communication Framework for Commodity Processing-in-DIMM Devices2024 ACM/IEEE 51st Annual International Symposium on Computer Architecture (ISCA)10.1109/ISCA59077.2024.00027(245-260)Online publication date: 29-Jun-2024
  • (2024)A lightweight RDMA Connection Protocol based on Post-hoc ConfirmationJournal of Parallel and Distributed Computing10.1016/j.jpdc.2024.104991(104991)Online publication date: Oct-2024
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cover image ACM Conferences
ICS '23: Proceedings of the 37th ACM International Conference on Supercomputing
June 2023
505 pages
ISBN:9798400700569
DOI:10.1145/3577193
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: 21 June 2023

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Author Tags

  1. in-network computing
  2. allreduce
  3. router
  4. RDMA

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View all
  • (2024)Sparse Gradient Communication with AlltoAll for Accelerating Distributed Deep LearningProceedings of the 53rd International Conference on Parallel Processing10.1145/3673038.3673140(148-157)Online publication date: 12-Aug-2024
  • (2024)PID-Comm: A Fast and Flexible Collective Communication Framework for Commodity Processing-in-DIMM Devices2024 ACM/IEEE 51st Annual International Symposium on Computer Architecture (ISCA)10.1109/ISCA59077.2024.00027(245-260)Online publication date: 29-Jun-2024
  • (2024)A lightweight RDMA Connection Protocol based on Post-hoc ConfirmationJournal of Parallel and Distributed Computing10.1016/j.jpdc.2024.104991(104991)Online publication date: Oct-2024
  • (2023)PiN: Processing in Network-on-ChipIEEE Design & Test10.1109/MDAT.2023.330794340:6(30-38)Online publication date: Dec-2023
  • (2023)DFAR: Dynamic-threshold Fault-tolerant Adaptive Routing for Fat Tree Networks2023 IEEE 29th International Conference on Parallel and Distributed Systems (ICPADS)10.1109/ICPADS60453.2023.00110(721-728)Online publication date: 17-Dec-2023

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