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Ken Fuchs

dblp:10/3324 · DBLP profile ↗
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1ranked-venue papers
1as first author
0since 2021 · last 1985
—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 · 93% Performance modeling and evaluation · 7%

Topics — the 4 heaviest of 5, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Parallel and multicore computing › task scheduling › memory-aware scheduling
memory-constrained scheduling
0.011985
Memory-Constrained Task Scheduling on a Network of Dual Processors · J. ACM 1985
Parallel and multicore computing
parallel architecture
0.011985
Memory-Constrained Task Scheduling on a Network of Dual Processors · J. ACM 1985
Parallel and multicore computing › multiprocessor system
shared-memory multiprocessor
0.011985
Memory-Constrained Task Scheduling on a Network of Dual Processors · J. ACM 1985
Parallel and multicore computing
task scheduling
0.011985
Memory-Constrained Task Scheduling on a Network of Dual Processors · J. ACM 1985

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

largest-memory-first scheduling · 0.0enumerative optimal scheduling · 0.0
YearPublicationVenuePosition
1985 Memory-Constrained Task Scheduling on a Network of Dual Processors
abstract
One aspect of network design is the extent to which memory is shared among the processing elements. In this paper a model with limited sharing (only two processors connected to each memory) is analyzed and its performance compared with the performance of two other models that have appeared in the literature. One of these is a model of multiple processors sharing a single memory; the other model considers a multiprocessor configuration in which each processor has its own dedicated memory. The tasks processed by these networks are described by both time and memory requirements. The largest-memory-first (LMF) scheduling algorithm is employed and its performance with respect to an enumerative optimal scheduling algorithm is bounded. On the basis of this measure we conclude that memory sharing is only desirable on very small networks and is disadvantageous on networks of larger size.
Ken Fuchs, Dennis G. Kafura
J. ACM1