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Jeff A. Jones

dblp:24/6319 · DBLP profile ↗
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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

TopicWeightPapersLastEvidence papers
Computational science and engineering › parallel computing
parallelization
0.011995
Parallelizing the Phylogeny Problem · SC 1995
Parallel and multicore computing
load balancing
0.011995
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
YearPublicationVenuePosition
1995 Parallelizing the Phylogeny Problem
abstract
The 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
SC1