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Noah Elliott

dblp:19/190 · DBLP profile ↗
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1ranked-venue papers
0as 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 · 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
High-performance computing · 61% Parallel and multicore computing · 39%

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

TopicWeightPapersLastEvidence papers
Parallel and multicore computing › parallel computing
parallel communication		 0.012001
Large scale parallel structured AMR calculations using the SAMRAI framework · SC 2001
High-performance computing
performance optimization at scale		 0.012001
Large scale parallel structured AMR calculations using the SAMRAI framework · SC 2001
High-performance computing
scientific computing systems		 0.012001
Large scale parallel structured AMR calculations using the SAMRAI framework · SC 2001
Parallel and multicore computing › parallel programming models and runtimes
parallel programming frameworks		 0.012001
Large scale parallel structured AMR calculations using the SAMRAI framework · SC 2001

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

load balancing · 0.0adaptive mesh refinement · 0.0
YearPublicationVenuePosition
2001 Large scale parallel structured AMR calculations using the SAMRAI framework
abstract
This paper discusses the design and performance of the parallel data communication infrastructure in SAMRAI, a software framework for structured adaptive mesh refinement (SAMR) multi-physics applications. We describe requirements of such applications and how SAMRAI abstractions manage complex data communication operations found in them. Parallel performance is characterized for two adaptive problems solving hyperbolic conservation laws on up to 512 processors of the IBM ASCI Blue Pacific system. Results reveal good scaling for numerical and data communication operations but poorer scaling in adaptive meshing and communication schedule construction phases of the calculations. We analyze the costs of these different operations, addressing key concerns for scaling SAMR computations to large numbers of processors, and discuss potential changes to improve our current implementation.
Andrew M. Wissink, Richard D. Hornung, Scott R. Kohn, Steve S. Smith, Noah Elliott
 SC5