Enclave Tasking for DG Methods on Dynamically Adaptive Meshes
- Submitting institution
-
University of Durham
- Unit of assessment
- 11 - Computer Science and Informatics
- Output identifier
- 129140
- Type
- D - Journal article
- DOI
-
10.1137/19M1276194
- Title of journal
- SIAM Journal on Scientific Computing (SISC)
- Article number
- -
- First page
- C69
- Volume
- 42
- Issue
- 3
- ISSN
- 10648275
- Open access status
- Compliant
- Month of publication
- -
- Year of publication
- 2020
- URL
-
https://doi.org/10.1137/19M1276194
- Supplementary information
-
-
- Request cross-referral to
- -
- Output has been delayed by COVID-19
- No
- COVID-19 affected output statement
- -
- Forensic science
- No
- Criminology
- No
- Interdisciplinary
- No
- Number of additional authors
-
2
- Research group(s)
-
A - Innovative Computing
- Citation count
- 1
- Proposed double-weighted
- No
- Reserve for an output with double weighting
- No
- Additional information
- Written under the umbrella of a FET HPC grant, this is one of the major outcomes of this research project. The paper discusses the algorithmic backbone of a lightweight task-based parallelisation which has been used in a follow-up ISC paper discussed by US colleagues over Twitter, and it has become one of the cornerstones of two successful EPSRC ExCALIBUR bids involving task-based and GPU-based parallelisation. The methodology fed into the ExaHyPE code which is a flagship code in the EU’s ChEESE (Center of Excellence in the domain of Solid Earth) project.
- Author contribution statement
- -
- Non-English
- No
- English abstract
- -