Please use this identifier to cite or link to this item: http://hdl.handle.net/123456789/5164
DC FieldValueLanguage
dc.contributor.authorLiang, Guicaien_US
dc.contributor.authorDaud, Siti Norbayaen_US
dc.contributor.authorIsmail, Nor Alinaen_US
dc.date.accessioned2023-11-23T03:16:54Z-
dc.date.available2023-11-23T03:16:54Z-
dc.date.issued2023-
dc.identifier.isbn979-840070070-5-
dc.identifier.urihttp://hdl.handle.net/123456789/5164-
dc.descriptionScopusen_US
dc.description.abstractThis paper discusses Remote Direct Memory Access(RDMA) communication technology and the congestion control methods for Graphics Processing Unit(GPU) clusters. The implementation methods of RDMA networks widely used in GPU clusters are studied. Three implementation modes including InfiniBand, iWARP, and RoCE are analysed with comparison of their performance and applicable environments. Then, based on the analysis of a new congestion controls algorithm, DBCC & CBFC algorithm, is proposed. This algorithm based on delay feedback control and credit flow control prevents network congestion or increased latency in GPU cluster RDMA networks. The working principles of the algorithm are introduced including calculating the adjustment amount of the sending rate, initializing the sender and receiver and mechanisms to handle packet loss and timeout. Experimental results show that the algorithm optimizes network performance with RDMA communication in GPU clusters, while avoiding congestion and minimizing packet loss. However, due to the limitation of experimental conditions, it is not possible to conduct more environmental tests. In practical application, the applicability of the algorithm needs to be carefully evaluated and adjusted according to the specific situations. © 2023 ACM.en_US
dc.language.isoenen_US
dc.publisherAssociation for Computing Machineryen_US
dc.subjectcongestion control algorithmen_US
dc.subjectGPU clusteren_US
dc.subjectRDMAen_US
dc.subjecttransport protocolen_US
dc.titleGPU Cluster RDMA communication technology and congestion controlen_US
dc.typeInternationalen_US
dc.relation.conferenceACM International Conference Proceeding Seriesen_US
dc.identifier.doi10.1145/3603781.3603876-
dc.description.page541 - 547en_US
dc.relation.seminar4th International Conference on Computing, Networks and Internet of Things, CNIOT 2023en_US
dc.date.seminarstartdate2023-05-26-
dc.date.seminarenddate2023-05-28-
dc.description.placeofseminarXiamenen_US
dc.description.typeIndexed Proceedingsen_US
item.fulltextNo Fulltext-
item.languageiso639-1en-
item.openairetypeInternational-
item.grantfulltextnone-
crisitem.author.deptUniversiti Malaysia Kelantan-
Appears in Collections:Faculty of Data Science and Computing - Proceedings
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