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Ruyi Zhang 0005

dblp:216/2683-5 · DBLP profile ↗
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2ranked-venue papers
0as first author
2since 2021 · last 2025
—ORCID · unresolved

Domains — the database's venue-derived domains; a paper can count in several

Systems, architecture and hardware · 1 · 1 since 2021Computer networks · 1 · 1 since 2021

Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.

Computer architecture, parallel and distributed computing, and storage systems
2 papers
High-performance computing · 28% Hardware accelerators and domain-specific architectures · 28% Distributed systems · 22%
Computer networks
1 paper
Internet architecture and protocols · 56% Datacenter networks · 28% Software-defined and programmable networks · 17%

Topics — the 9 heaviest of 9, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Hardware accelerators and domain-specific architectures
machine learning accelerator
0.912025
Accelerating Model Training on Ascend Chips: An Industrial System for Profiling, Analysis and Optimization · USENIX ATC 2025
High-performance computing
performance optimization
0.912025
Accelerating Model Training on Ascend Chips: An Industrial System for Profiling, Analysis and Optimization · USENIX ATC 2025
Internet architecture and protocols › multicast
in-network multicast
0.712023
MC-RDMA: Improving Replication Performance of RDMA-based Distributed Systems with Reliable Multicast Support · ICNP 2023
Datacenter networks
RDMA
0.712023
MC-RDMA: Improving Replication Performance of RDMA-based Distributed Systems with Reliable Multicast Support · ICNP 2023
Internet architecture and protocols › multicast
reliable multicast
0.712023
MC-RDMA: Improving Replication Performance of RDMA-based Distributed Systems with Reliable Multicast Support · ICNP 2023
Distributed systems › replication
data replication
0.712023
MC-RDMA: Improving Replication Performance of RDMA-based Distributed Systems with Reliable Multicast Support · ICNP 2023
Interconnection networks and networks-on-chip › remote direct memory access
RDMA-based replication
0.712023
MC-RDMA: Improving Replication Performance of RDMA-based Distributed Systems with Reliable Multicast Support · ICNP 2023
Software-defined and programmable networks › programmable data plane
p4 switch
0.212023
MC-RDMA: Improving Replication Performance of RDMA-based Distributed Systems with Reliable Multicast Support · ICNP 2023
Software-defined and programmable networks › programmable data plane
programmable switch
0.212023
MC-RDMA: Improving Replication Performance of RDMA-based Distributed Systems with Reliable Multicast Support · ICNP 2023

Methods — techniques the papers use, named apart from their topics

multicast routing protocol · 1.3ACK/NAK merging · 1.3profiling · 0.9performance analysis · 0.9
YearPublicationVenuePosition
2025 Accelerating Model Training on Ascend Chips: An Industrial System for Profiling, Analysis and Optimization
Zhibin Wang 0002, Ruyi Zhang 0005, Chen Tian 0001, Xiaoliang Wang 0001, Wan-Chun Dou, Guihai Chen, Bingqiang Wang, Yonghong Tian 0001, Yan Zhang 0002, Hui Wang 0030, Fuchun Wei, Boquan Sun, Bin She, Teng Su, Yaoyuan Wang, Guyue Liu
USENIX ATC4
2023 MC-RDMA: Improving Replication Performance of RDMA-based Distributed Systems with Reliable Multicast Support
abstract
Remote Direct Memory Access has been widely adopted in distributed storage systems. However, it only supports unicast operations, which degrades the performance significantly for data replication because of bandwidth waste and CPU overhead. To address the problem, we propose MC-RDMA, a distributed and reliable multicast RDMA. It is compatible with existing unicast RDMA but supports lazy packet replication with reliable RDMA multicasting. The key idea of MC-RDMA is utilizing in-network programmable switches to build a NIC-transparent reliable multicast protocol for RDMA. MC-RDMA combines the address information of the IP and RoCEv2 into a sender-initialized multicast routing protocol. Besides, it synchronizes the hardware transmission states of multiple receivers by merging ACKs and NAKs. To verify the effectiveness of MC-RDMA, we implement it with Mellanox ConnectX-6 commodity RNICs and Intel Tofino P4 programmable switches. Experimental results show that MC-RDMA can double the sender bandwidth utilization and reduce the CPU overhead significantly compared to unicast-based RDMA replications. Moreover, it reduces the storage request latency by -30% with realistic workloads and decreases the training time by -50% in the distributed training system.
Chengyuan Huang, Yixiao Gao, Duoxing Li, Yibo Xiao, Ruyi Zhang 0005, Chen Tian 0001, Xiaoliang Wang 0001, Wan-Chun Dou, Guihai Chen, Fu Xiao 0001
ICNP6