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Data center networks (DCNs) have widely deployed RDMA to support data-intensive applications such as machine learning. While DCNs are designed with rich multi-path topology, current RDMA (hardware) technology does not support multi-path transport. In this paper we advance Maestro- a purely software-basedmulti-path RDMA solution - to effectively utilize the rich multi-path topology for load balancing and reliability. As a "middleware"operating at the user-space, Maestro is modulaR@and software-defined:Maestro decouples path selection and load balancing mechanisms from hardware features, and allows DCN operators and applications to make flexible decisions by employing the best mechanisms as needed. As such, Maestro can be readily deployed using existing RDMA hardware (NICs) to support distributed deep learning (DDL) applications. Our experiments show that Maestro is capable of fully utilizing multiple paths with negligible CPU overheads, thereby enhancing the performance of DDL applications.
|Original language||English (US)|
|Title of host publication||SOSR 2021 - Proceedings of the 2021 ACM SIGCOMM Symposium on SDN Research|
|Publisher||Association for Computing Machinery, Inc|
|Number of pages||13|
|State||Published - Oct 11 2021|
|Event||2021 ACM SIGCOMM Symposium on SDN Research, SOSR 2021 - Virtual, Online, United States|
Duration: Sep 20 2021 → Sep 21 2021
|Name||Proceedings of the ACM SIGCOMM Symposium on SDN Research (SOSR)|
|Conference||2021 ACM SIGCOMM Symposium on SDN Research, SOSR 2021|
|Period||9/20/21 → 9/21/21|
Bibliographical noteFunding Information:
The research was supported in part by NSF under Grants CNS-1618339, CNS-1814322, CNS-1831140, CNS-1836772, CNS-1901103, CNS-2106771 and CCF-2123987.
© 2021 ACM.
- Data Center Networks
- Distributed Deep Learning
- Multi-Path RDMA
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7/15/21 → 6/30/24
Project: Research project