Demo Data from EDBT 2026 / reviewers in the wild / expert
Guilherme Loch Waltrick Goes
dblp:381/9247
· DBLP profile ↗
1ranked-venue papers
0as first author
1since 2021 · last 2024
0009-0002-4078-5781ORCID · verified
Domains — the database's venue-derived domains; a paper can count in several
Computer 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
1 paper |
Distributed systems · 33% High-performance computing · 33% Interconnection networks and networks-on-chip · 33% | |
| Computer networks
1 paper |
Datacenter networks · 100% | |
| Databases, data mining, and information retrieval
1 paper |
Machine learning and data management · 100% |
Topics — the 4 heaviest of 5, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Datacenter networks › RDMA
RDMA over Converged Ethernet |
0.8 | 1 | 2024 | RDMA over Ethernet for Distributed Training at Meta Scale · SIGCOMM 2024 |
High-performance computing
collective communication |
0.8 | 1 | 2024 | RDMA over Ethernet for Distributed Training at Meta Scale · SIGCOMM 2024 |
Distributed systems › distributed machine learning
distributed training |
0.8 | 1 | 2024 | RDMA over Ethernet for Distributed Training at Meta Scale · SIGCOMM 2024 |
Interconnection networks and networks-on-chip › remote direct memory access
RDMA networks |
0.8 | 1 | 2024 | RDMA over Ethernet for Distributed Training at Meta Scale · SIGCOMM 2024 |
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2024 | RDMA over Ethernet for Distributed Training at Meta ScaleabstractThe rapid growth in both computational density and scale in AI models in recent years motivates the construction of an efficient and reliable dedicated network infrastructure. This paper presents the design, implementation, and operation of Meta's Remote Direct Memory Access over Converged Ethernet (RoCE) networks for distributed AI training. Adithya Gangidi, Rui Miao 0001, Shengbao Zheng, Sai Jayesh Bondu, Guilherme Loch Waltrick Goes, Hany Morsy, Rohit Puri, Mohammad Riftadi, Ashmitha Jeevaraj Shetty, Shuqiang Zhang, Mikel Jimenez Fernandez, Shashidhar Gandham, Hongyi Zeng |
SIGCOMM | 5 |