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
Article, Secondary publication, Publisher's Version
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Item Type: | Article |
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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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