EDBT 2026 Demo / reviewers in the wild / expert
Jeff A. Jones
dblp:24/6319
· DBLP profile ↗
1ranked-venue papers
1as first author
0since 2021 · last 1995
—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 |
Parallel and multicore computing · 100% | |
| Interdisciplinary, comprehensive, and emerging computing
1 paper |
Computational science and engineering · 100% |
Topics — the 2 heaviest of 3, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Computational science and engineering › parallel computing
parallelization |
0.0 | 1 | 1995 | Parallelizing the Phylogeny Problem · SC 1995 |
Parallel and multicore computing
load balancing |
0.0 | 1 | 1995 | Parallelizing the Phylogeny Problem · SC 1995 |
Methods — techniques the papers use, named apart from their topics
parallelization · 0.0distributed trie · 0.0distributed task queues · 0.0distributed task queue · 0.0
| Year | Publication | Venue | Position |
|---|---|---|---|
| 1995 | Parallelizing the Phylogeny ProblemabstractThe problem of determining the evolutionary history of species in the form of phylogenetic trees is known as the phylogeny problem. We present a parallelization of the character compatibility method for solving the phylogeny problem. Abstractly, the algorithm searches through all subsets of characters, which may be traits like opposable thumbs or DNA sequence values, looking for a maximal consistent subset. The notion of consistency in this case is the existence of a particular kind of phylogenetic tree called a perfect phylogeny tree. The two challenges to achieving an efficient implementation are load balancing and efficient sharing of information to enable pruning. In both cases, there is a trade-off between communication overhead and the quality of the solution. For load balancing we use a distributed task queue, which has imperfect load information but avoids centralization bottlenecks. For sharing pruning information, we use a distributed trie, which also avoids centralization but maintains incomplete information. We evaluate several implementations of the trie, the best of which achieves speedups of 50 on a 64-processor CM-5. Jeff A. Jones, Katherine A. Yelick |
SC | 1 |