Demo Data from EDBT 2026 / reviewers in the wild / expert
Bohdan Smilauer
dblp:22/6982
· DBLP profile ↗
1ranked-venue papers
1as first author
0since 2021 · last 1985
—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 |
Performance modeling and evaluation · 61% Memory systems · 30% Parallel and multicore computing · 9% |
Topics — the 4 heaviest of 4, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Performance modeling and evaluation › queueing models
mean value analysis |
0.0 | 1 | 1985 | General Model for Memory Interference in Mulitprocessors and Mean Value Analysis · IEEE Trans. Computers 1985 |
Memory systems
memory interference |
0.0 | 1 | 1985 | General Model for Memory Interference in Mulitprocessors and Mean Value Analysis · IEEE Trans. Computers 1985 |
Performance modeling and evaluation
queueing models |
0.0 | 1 | 1985 | General Model for Memory Interference in Mulitprocessors and Mean Value Analysis · IEEE Trans. Computers 1985 |
Parallel and multicore computing › multiprocessor system
shared-memory multiprocessor |
0.0 | 1 | 1985 | General Model for Memory Interference in Mulitprocessors and Mean Value Analysis · IEEE Trans. Computers 1985 |
Methods — techniques the papers use, named apart from their topics
mean value analysis · 0.0iterative approximation · 0.0
| Year | Publication | Venue | Position |
|---|---|---|---|
| 1985 | General Model for Memory Interference in Mulitprocessors and Mean Value AnalysisabstractThis correspondence seeks to generalize and clarify the general model for memory interference (GMI) in multiprocessors as proposed by Hoogendoorn. The interference model creates a queueing network where some service centers are FCFS with constant service times; therefore, we also apply the mean value analysis (MVA) approxdmation suggested by Reiser to solve this model. Furthermore, we reduce the computations in this approximation by applying the iterative scheme suggested by Schweitzer. Although many authors have studied the memory interference problem, nobody has used the above-mentioned MVA approximations. We study these MVA approximations in the context of the memory interference problem and show that they produce better results with much less computation. Bohdan Smilauer |
IEEE Trans. Computers | 1 |