Demo Data from EDBT 2026 / reviewers in the wild / expert
Justin Ebedes
dblp:57/4901
· DBLP profile ↗
1ranked-venue papers
1as first author
0since 2021 · last 2004
—ORCID · none
Domains — the database's venue-derived domains; a paper can count in several
Applied, interdisciplinary, general and emerging computing · 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 |
Parallel and multicore computing · 67% High-performance computing · 33% | |
| Interdisciplinary, comprehensive, and emerging computing
1 paper |
Bioinformatics and computational biology · 100% |
Topics — the 5 heaviest of 5, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Bioinformatics and computational biology
multiple sequence alignment |
0.0 | 1 | 2004 | Multiple sequence alignment in parallel on a workstation cluster · Bioinform. 2004 |
Bioinformatics and computational biology
sequence analysis |
0.0 | 1 | 2004 | Multiple sequence alignment in parallel on a workstation cluster · Bioinform. 2004 |
Parallel and multicore computing › parallel programming models
message passing |
0.0 | 1 | 2004 | Multiple sequence alignment in parallel on a workstation cluster · Bioinform. 2004 |
High-performance computing › cluster computing
network of workstations |
0.0 | 1 | 2004 | Multiple sequence alignment in parallel on a workstation cluster · Bioinform. 2004 |
Parallel and multicore computing
parallel computing |
0.0 | 1 | 2004 | Multiple sequence alignment in parallel on a workstation cluster · Bioinform. 2004 |
Methods — techniques the papers use, named apart from their topics
message passing interface · 0.1guide tree · 0.1distance matrix · 0.1
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2004 | Multiple sequence alignment in parallel on a workstation clusterabstractSUMMARY: Multiple sequence alignment is the NP-hard problem of aligning three or more DNA or amino acid sequences in an optimal way so as to match as many characters as possible from the set of sequences. The popular sequence alignment program ClustalW uses the classical method of approximating a sequence alignment, by first computing a distance matrix and then constructing a guide tree to show the evolutionary relationship of the sequences. We show that parallelizing the ClustalW algorithm can result in significant speedup. We used a cluster of workstations using C and message passing interface for our implementation. Experimental results show that speedup of over 5.5 on six processors is obtainable for most inputs. AVAILABILITY: The software is available upon request from the second author. Justin Ebedes, Amitava Datta |
Bioinform. | 1 |