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M'Lissa Smith

dblp:88/3138 · 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
Electronic design automation · 91% Parallel and multicore computing · 9%

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

TopicWeightPapersLastEvidence papers
Electronic design automation › physical design
circuit clustering		 0.011993
A Parallel Bottom-Up Clustering Algorithm with Applications to Circuit Partitioning in VLSI Design · DAC 1993
Electronic design automation › physical design
circuit partitioning		 0.011993
A Parallel Bottom-Up Clustering Algorithm with Applications to Circuit Partitioning in VLSI Design · DAC 1993
Electronic design automation › physical design › circuit partitioning
clustering-based partitioning		 0.011993
A Parallel Bottom-Up Clustering Algorithm with Applications to Circuit Partitioning in VLSI Design · DAC 1993
Parallel and multicore computing › parallel data mining
parallel clustering		 0.011993
A Parallel Bottom-Up Clustering Algorithm with Applications to Circuit Partitioning in VLSI Design · DAC 1993

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

recursive clique collapsing · 0.0random graph theory · 0.0
YearPublicationVenuePosition
1993 A Parallel Bottom-Up Clustering Algorithm with Applications to Circuit Partitioning in VLSI Design
abstract
In thk paper, we present a bottom-up clustering algorithm based on recursive collapsing of small cliques in a graph.The sizes of the small cliques are derived using random graph theory.This clustering algorithm leads to a natural parallel implementation in which multiple processors are used to identify clusters simultaneously.We also present a cluster-based partitioning method in which our clustering algorithm is used as a preprocessing step to both the bisection algorithm by Fiduccia and Mattheyses and a ratio-cut algorithm by Wei and Cheng.Our results show that cluster-based partitioning obtains cut sizes up to 49.6% smaller than the bisection algorithm, and obtains ratio cut sizes up to 66.8% smaller than the ratio-cut algorithm.Moreover, we show that cluster-based partitioning produces much stabler results than direct partitioning.
Jason Cong, M'Lissa Smith
 DAC2