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Henricus Bouwmeester

dblp:14/7866 · DBLP profile ↗
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1ranked-venue papers
1as first author
0since 2021 · last 2011
—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
High-performance computing · 67% Parallel and multicore computing · 33%
Theoretical computer science
1 paper
Algorithms and data structures · 100%

Topics — the 3 heaviest of 4, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
High-performance computing
numerical linear algebra
0.112011
Tiled QR factorization algorithms · SC 2011
High-performance computing › numerical linear algebra › matrix factorization
QR factorization
0.112011
Tiled QR factorization algorithms · SC 2011
Parallel and multicore computing › parallel algorithms › parallel algorithm design
tiled algorithm
0.112011
Tiled QR factorization algorithms · SC 2011

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

tiled algorithms · 0.2complexity analysis · 0.2
YearPublicationVenuePosition
2011 Tiled QR factorization algorithms
abstract
This work revisits existing algorithms for the QR factorization of rectangular matrices composed of p × q tiles, where p ≥ q. Within this framework, we study the critical paths and performance of algorithms such as Sameh-Kuck, Fibonacci, Greedy, and those found within PLASMA. Although neither Fibonacci nor Greedy is optimal, both are shown to be asymptotically optimal for all matrices of size p = q2f(q), where f is any function such that lim+∞ f = 0. This novel and important complexity result applies to all matrices where p and q are proportional, p = λq, with λ ≥ 1, thereby encompassing many important situations in practice (least squares). We provide an extensive set of experiments that show the superiority of the new algorithms for tall matrices.
Henricus Bouwmeester, Mathias Jacquelin, Julien Langou, Yves Robert
SC1