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Henry J. Alme

dblp:07/33 · DBLP profile ↗
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2ranked-venue papers
1as first author
0since 2021 · last 2001
—ORCID · none

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

Systems, architecture and hardware · 2 · 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
Performance modeling and evaluation · 91% High-performance computing · 9%

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

TopicWeightPapersLastEvidence papers
Performance modeling and evaluation
analytical modeling		 0.012001
Predictive performance and scalability modeling of a large-scale application · SC 2001
Performance modeling and evaluation
performance prediction		 0.012001
Predictive performance and scalability modeling of a large-scale application · SC 2001
Performance modeling and evaluation › parallel system performance
scalability modeling		 0.012001
Predictive performance and scalability modeling of a large-scale application · SC 2001
High-performance computing
scientific computing systems		 0.012001
Predictive performance and scalability modeling of a large-scale application · SC 2001

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

parametric modeling · 0.0analytical modeling · 0.0
YearPublicationVenuePosition
2001 Predictive performance and scalability modeling of a large-scale application
abstract
In this work we present a predictive analytical model that encompasses the performance and scaling characteristics of an important ASCI application. SAGE (SAIC's Adaptive Grid Eulerian hydrocode) is a multidimensional hydrodynamics code with adaptive mesh refinement. The model is validated against measurements on several systems including ASCI Blue Mountain, ASCI White, and a Compaq Alphaserver ES45 system showing high accuracy. It is parametric --- basic machine performance numbers (latency, MFLOPS rate, bandwidth) and application characteristics (problem size, decomposition method, etc.) serve as input. The model is applied to add insight into the performance of current systems, to reveal bottlenecks, and to illustrate where tuning efforts can be effective. We also use the model to predict performance on future systems.
Darren J. Kerbyson, Henry J. Alme, Adolfy Hoisie, Fabrizio Petrini, Harvey J. Wasserman, Michael L. Gittings
 SC2
2001 Domain Decomposition Models for Parallel Monte Carlo Transport
Henry J. Alme, Garry H. Rodrigue, George B. Zimmerman
 J. Supercomput.1