Demo Data from EDBT 2026 / reviewers in the wild / expert
Pau S. Chang
dblp:36/6690
· DBLP profile ↗
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
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
High-performance computing › large-scale simulation
numerical weather prediction |
0.0 | 1 | 1990 | An Implementation of a Barotropic Numerical Weather Prediction Model in the Functional Language SISAL · PPoPP 1990 |
Parallel and multicore computing › parallel computing
parallel implementation |
0.0 | 1 | 1990 | An Implementation of a Barotropic Numerical Weather Prediction Model in the Functional Language SISAL · PPoPP 1990 |
Parallel and multicore computing
parallelism exploitation |
0.0 | 1 | 1990 | 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
| Year | Publication | Venue | Position |
|---|---|---|---|
| 1990 | An Implementation of a Barotropic Numerical Weather Prediction Model in the Functional Language SISALabstractNumerical 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 |
PPoPP | 1 |