Demonstration venue · read-only. Every page can be browsed; the buttons that would change it are switched off. Create an account to run TaxoReview on your own data. A simulation from public publication records: the people listed were not invited and did not agree to review.
? Tutorial for this page

Matthew Kopala

dblp:88/8523 · DBLP profile ↗
← Back
1ranked-venue papers
0as first author
0since 2021 · last 2010
—ORCID · none

Domains — the database's venue-derived domains; a paper can count in several

Systems, architecture and hardware · 1

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
Distributed systems · 77% Performance modeling and evaluation · 23%

Topics — the 2 heaviest of 2, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Distributed systems › grid computing
volunteer computing
0.112010
Simultaneous performance exploration and optimized search with volunteer computing · HPDC 2010
Performance modeling and evaluation
parameter space exploration
0.012010
Simultaneous performance exploration and optimized search with volunteer computing · HPDC 2010

Methods — techniques the papers use, named apart from their topics

volunteer computing · 0.1stochastic optimization · 0.1
YearPublicationVenuePosition
2010 Simultaneous performance exploration and optimized search with volunteer computing
abstract
Volunteer computing is a powerful platform for solving complex scientific problems. [email protected] is a volunteer computing project available to the cognitive modeling community for conducting research to better understand the human mind. We are interested in optimizing search processes on volunteer resources, yet we are also interested in exploring and understanding changes in model performance across interacting, non-linear mechanisms and parameter spaces. To support both of these goals, we have developed a stochastic optimization approach and integrated it with [email protected] We tested this approach with a cognitive model on a sample parameter space, demonstrating significant decreases in computational resource utilization and search runtime, while also providing useful visual representations of performance surfaces. Future work will focus on scaling the technique to more volunteers and larger parameter spaces, as well as optimizing the performance of the search algorithm in regards to the challenges inherent with volunteer computing.
L. Richard Moore Jr., Matthew Kopala, Thomas Mielke, Michael Krusmark, Kevin A. Gluck
HPDC2