EDBT 2026 Demo / reviewers in the wild / expert
Udai Garg
dblp:61/2428
· DBLP profile ↗
1ranked-venue papers
1as first author
0since 2021 · last 1988
—ORCID · none
Domains — the database's venue-derived domains; a paper can count in several
Systems, architecture and hardware · 1 · 1 first-author
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 |
Interconnection networks and networks-on-chip · 50% Performance modeling and evaluation · 50% |
Topics — the 4 heaviest of 4, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Interconnection networks and networks-on-chip › switching network › multistage interconnection network
banyan network |
0.0 | 1 | 1988 | Decomposing Banyan Networks for Performance Analysis · IEEE Trans. Computers 1988 |
Performance modeling and evaluation › queueing models
buffer analysis |
0.0 | 1 | 1988 | Decomposing Banyan Networks for Performance Analysis · IEEE Trans. Computers 1988 |
Interconnection networks and networks-on-chip › switching network
multistage interconnection network |
0.0 | 1 | 1988 | Decomposing Banyan Networks for Performance Analysis · IEEE Trans. Computers 1988 |
Performance modeling and evaluation
network performance analysis |
0.0 | 1 | 1988 | Decomposing Banyan Networks for Performance Analysis · IEEE Trans. Computers 1988 |
Methods — techniques the papers use, named apart from their topics
markov chain analysis · 0.0decomposition · 0.0
| Year | Publication | Venue | Position |
|---|---|---|---|
| 1988 | Decomposing Banyan Networks for Performance AnalysisabstractA general form of input-destination distribution matrix increases state space exorbitantly, thus making any buffer at every state statistically different from another. Certain specific forms of input-destination distribution matrix to which many real-life cases may conform, are analyzed. The idea called decomposition is applied here for specific nonhomogeneous flows. State space is not allowed to increase significantly; also the reduction in network size at successive stages is utilized to increase the computational efficiency.> Udai Garg, Yo-Ping Huang |
IEEE Trans. Computers | 1 |