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RDMA Communciation Patterns: A Systematic Evaluation

Ziegler, Tobias ; Leis, Viktor ; Binnig, Carsten (2024)
RDMA Communciation Patterns: A Systematic Evaluation.
In: Datenbank-Spektrum : Zeitschrift für Datenbanktechnologien und Information Retrieval, 2020, 20 (3)
doi: 10.26083/tuprints-00024013
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Item Type: Article
Type of entry: Secondary publication
Title: RDMA Communciation Patterns: A Systematic Evaluation
Language: English
Date: 26 April 2024
Place of Publication: Darmstadt
Year of primary publication: November 2020
Place of primary publication: Berlin ; Heidelberg
Publisher: Springer
Journal or Publication Title: Datenbank-Spektrum : Zeitschrift für Datenbanktechnologien und Information Retrieval
Volume of the journal: 20
Issue Number: 3
DOI: 10.26083/tuprints-00024013
Corresponding Links:
Origin: Secondary publication DeepGreen
Abstract:

Remote Direct Memory Access (RDMA) is a networking protocol that provides high bandwidth and low latency accesses to a remote node’s main memory. Although there has been much work around RDMA, such as building libraries on top of RDMA or even applications leveraging RDMA, it remains a hard problem to identify the most suitable RDMA primitives and their combination for a given problem. While there have been some initial studies included in papers that aim to investigate selected performance characteristics of particular design choices, there has not been a systematic study to evaluate the communication patterns of scale-out systems. In this paper, we address this issue by systematically investigating how to efficiently use RDMA for building scale-out systems.

Uncontrolled Keywords: Information Storage and Retrieval, Data Mining and Knowledge Discovery, Database Management, Data Structures and Information Theory, IT in Business, Computer Systems Organization and Communication Networks
Status: Publisher's Version
URN: urn:nbn:de:tuda-tuprints-240135
Additional Information:

Special Issue: Data Management for Future Hardware

Classification DDC: 000 Generalities, computers, information > 004 Computer science
Divisions: 20 Department of Computer Science > Data and AI Systems
Date Deposited: 26 Apr 2024 12:37
Last Modified: 14 Aug 2024 09:34
SWORD Depositor: Deep Green
URI: https://tuprints.ulb.tu-darmstadt.de/id/eprint/24013
PPN: 520633059
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