Zero-sided RDMA: Network-driven Data Shuffling for Disaggregated Heterogeneous Cloud DBMSs

Author:

Jasny Matthias1ORCID,Thostrup Lasse1ORCID,Tamimi Sajjad1ORCID,Koch Andreas1ORCID,István Zsolt1ORCID,Binnig Carsten2ORCID

Affiliation:

1. Technical University Darmstadt, Darmstadt, Germany

2. Technical University Darmstadt & DFKI, Darmstadt, Germany

Abstract

In this paper, we present a novel communication scheme called zero-sided RDMA, enabling data exchange as a native network service using a programmable switch. In contrast to one- or two-sided RDMA, in zero-sided RDMA, neither the sender nor the receiver is actively involved in data exchange. Zero-sided RDMA thus enables efficient RDMA-based data shuffling between heterogeneous hardware devices in a disaggregated setup without the need to implement a complete RDMA stack on each heterogeneous device or the need for a CPU that is co-located with the accelerator to coordinate the data transfer. As such, we think that zero-sided RDMA is a major building block to make efficient use of heterogeneous accelerators in future cloud DBMSs. In our evaluation, we show that zero-sided RDMA can outperform existing one-sided RDMA-based schemes for accelerator-to-accelerator communication and thus speed up typical distributed database operations such as joins.

Funder

DFG priority program 2037

DFG Collaborative Research Center

Publisher

Association for Computing Machinery (ACM)

Reference54 articles.

1. GPUDirect Async: Exploring GPU synchronous communication techniques for InfiniBand clusters

2. Wei Bai, Shanim Sainul Abdeen, Ankit Agrawal, Krishan Kumar Attre, Paramvir Bahl, Ameya Bhagat, Gowri Bhaskara, Tanya Brokhman, Lei Cao, Ahmad Cheema, Rebecca Chow, Jeff Cohen, Mahmoud Elhaddad, Vivek Ette, Igal Figlin, Daniel Firestone, Mathew George, Ilya German, Lakhmeet Ghai, Eric Green, Albert G. Greenberg, Manish Gupta, Randy Haagens, Matthew Hendel, Ridwan Howlader, Neetha John, Julia Johnstone, Tom Jolly, Greg Kramer, David Kruse, Ankit Kumar, Erica Lan, Ivan Lee, Avi Levy, Marina Lipshteyn, Xin Liu, Chen Liu, Guohan Lu, Yuemin Lu, Xiakun Lu, Vadim Makhervaks, Ulad Malashanka, David A. Maltz, Ilias Marinos, Rohan Mehta, Sharda Murthi, Anup Namdhari, Aaron Ogus, Jitendra Padhye, Madhav Pandya, Douglas Phillips, Adrian Power, Suraj Puri, Shachar Raindel, Jordan Rhee, Anthony Russo, Maneesh Sah, Ali Sheriff, Chris Sparacino, Ashutosh Srivastava, Weixiang Sun, Nick Swanson, Fuhou Tian, Lukasz Tomczyk, Vamsi Vadlamuri, Alec Wolman, Ying Xie, Joyce Yom, Lihua Yuan, Yanzhao Zhang, and Brian Zill. 2023. Empowering Azure Storage with RDMA. In 20th USENIX Symposium on Networked Systems Design and Implementation, NSDI 2023, Boston, MA, April 17--19, 2023, Mahesh Balakrishnan and Manya Ghobadi (Eds.). USENIX Association, 49--67. https://www.usenix.org/conference/nsdi23/presentation/bai

3. Rack-Scale In-Memory Join Processing using RDMA

4. Rack-Scale In-Memory Join Processing using RDMA

5. The end of slow networks

同舟云学术

1.学者识别学者识别

2.学术分析学术分析

3.人才评估人才评估

"同舟云学术"是以全球学者为主线,采集、加工和组织学术论文而形成的新型学术文献查询和分析系统,可以对全球学者进行文献检索和人才价值评估。用户可以通过关注某些学科领域的顶尖人物而持续追踪该领域的学科进展和研究前沿。经过近期的数据扩容,当前同舟云学术共收录了国内外主流学术期刊6万余种,收集的期刊论文及会议论文总量共计约1.5亿篇,并以每天添加12000余篇中外论文的速度递增。我们也可以为用户提供个性化、定制化的学者数据。欢迎来电咨询!咨询电话:010-8811{复制后删除}0370

www.globalauthorid.com

TOP

Copyright © 2019-2024 北京同舟云网络信息技术有限公司
京公网安备11010802033243号  京ICP备18003416号-3