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D. Arapattu

dblp:125/2382 · DBLP profile ↗
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1ranked-venue papers
1as first author
0since 2021 · last 1989
—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
Performance modeling and evaluation · 87% Memory systems · 13%
Software engineering, system software, and programming languages
1 paper
Compilers and program optimization · 100%

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

TopicWeightPapersLastEvidence papers
Performance modeling and evaluation
analytical modeling
0.011989
Building analytical models into an interactive performance prediction tool · SC 1989
Performance modeling and evaluation › performance prediction
parallel program performance prediction
0.011989
Building analytical models into an interactive performance prediction tool · SC 1989
Memory systems
memory hierarchy
0.011989
Building analytical models into an interactive performance prediction tool · SC 1989

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

dependence analysis · 0.0analytical modeling · 0.0
YearPublicationVenuePosition
1989 Building analytical models into an interactive performance prediction tool
abstract
In this paper we describe an interactive tool designed for performance prediction of parallel programs. Static performance prediction, in general, is a very difficult task. In order to avoid some inherent problems, we concentrate on reasonably structured scientific programs. Our prediction system, which is built as a sub-system of a larger interactive environment, uses a parser, dependence analyzer, database and an X-window based front end in analyzing programs. The system provides the user with execution times of different sections of programs. When there are unknowns involved, such as number of processors or unknown loop bounds, the output is an algebraic expression in terms of these variables. We propose a simple analytical model as an attempt to predict performance degradation due to data references in hierarchical memory systems. The predicted execution times of some Lawrence Livermore loop kernels are given together with the experimental values obtained by executing the loops on Alliant FX/8.
D. Arapattu, Dennis Gannon
SC1