Demonstration venue · read-only. Every page can be browsed; the buttons that would change it are switched off. Create an account to run TaxoReview on your own data. A simulation from public publication records: the people listed were not invited and did not agree to review.
? Tutorial for this page

Pau S. Chang

dblp:36/6690 · DBLP profile ↗
← Back
1ranked-venue papers
1as first author
0since 2021 · last 1990
—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
Parallel and multicore computing · 67% High-performance computing · 33%
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
High-performance computing › large-scale simulation
numerical weather prediction
0.011990
An Implementation of a Barotropic Numerical Weather Prediction Model in the Functional Language SISAL · PPoPP 1990
Parallel and multicore computing › parallel computing
parallel implementation
0.011990
An Implementation of a Barotropic Numerical Weather Prediction Model in the Functional Language SISAL · PPoPP 1990
Parallel and multicore computing
parallelism exploitation
0.011990
An Implementation of a Barotropic Numerical Weather Prediction Model in the Functional Language SISAL · PPoPP 1990

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

transliteration · 0.0algorithm rearrangement · 0.0
YearPublicationVenuePosition
1990 An Implementation of a Barotropic Numerical Weather Prediction Model in the Functional Language SISAL
abstract
Numerical Weather Prediction (NWP) is acknowledged as being of vital importance to economy. The demand that NWP places on computing system performance has increased dramatically since the introduction of computer systems and is still growing. This paper describes the implementation of a one-level barotropic spectral NWP model using the high level functional language SISAL. Our initial approach was a direct transliteration from FORTRAN to SISAL, the result of which was a large amount of array copying and loss of parallelism. The algorithm of the model was then rearranged to enable effective exploitation of parallelism by an optimizing SISAL compiler. The parallel implementation technique and its potential for modeling with larger model sizes are discussed, and the improved results for an Encore Multimax multiprocessor are presented.
Pau S. Chang, Gregory K. Egan
PPoPP1