Matthew Judd

dblp:73/1074 · DBLP profile ↗
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1ranked-venue papers
0as first author
0since 2021 · last 1993
—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
Parallel and multicore computing · 67% High-performance computing · 33%

Topics — the 3 heaviest of 3, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Parallel and multicore computing › parallel programming models
object-oriented parallel programming
0.011993
A Parallel Object-Oriented Framework for Stencil Algorithms · HPDC 1993
Parallel and multicore computing › parallel programming models and runtimes
parallel programming frameworks
0.011993
A Parallel Object-Oriented Framework for Stencil Algorithms · HPDC 1993
High-performance computing
stencil computation
0.011993
A Parallel Object-Oriented Framework for Stencil Algorithms · HPDC 1993

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

object-oriented framework · 0.0inheritance · 0.0
YearPublicationVenuePosition
1993 A Parallel Object-Oriented Framework for Stencil Algorithms
abstract
The authors present an object-oriented framework for constructing parallel implementations of stencil algorithms. This framework simplifies the development process by encapsulating the common aspects of stencil algorithms in a base stencil class so that application-specific derived classes can be easily defined via inheritance and overloading. In addition, the stencil base class contains mechanisms for parallel execution. The result is a high-performance, parallel, application-specific stencil class. The authors present the design rationale for the base class and illustrate the derivation process by defining two subclasses, an image convolution class and a PDE solver. The classes have been implemented in Mentat, an object-oriented parallel programming system that is available on a variety of platforms. Performance results are given for a network of Sun SPARCstation IPCs.>
John F. Karpovich, Matthew Judd, W. Timothy Strayer, Andrew S. Grimshaw
HPDC2