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Peter E. Vincent

dblp:01/9486 · DBLP profile ↗
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2ranked-venue papers
1as first author
1since 2021 · last 2022
0000-0002-1314-8827ORCID · reported

Domains — the database's venue-derived domains; a paper can count in several

Systems, architecture and hardware · 1 · 1 first-authorGraphics, computer vision, multimedia, augmented reality and games · 1 · 1 since 2021

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 graphics and multimedia
1 paper
Visualization and visual analytics · 50% Geometric modeling and processing · 50%
Computer architecture, parallel and distributed computing, and storage systems
1 paper
High-performance computing · 91% GPUs and heterogeneous computing · 9%
Interdisciplinary, comprehensive, and emerging computing
1 paper
Computational science and engineering · 100%

Topics — the 8 heaviest of 9, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Visualization and visual analytics › topological data analysis
contour tree
0.612022
Identification and Classification of Off-Vertex Critical Points for Contour Tree Construction on Unstructured Meshes of Hexahedra · IEEE Trans. Vis. Comput. Graph. 2022
Geometric modeling and processing › mesh generation
hexahedral mesh
0.612022
Identification and Classification of Off-Vertex Critical Points for Contour Tree Construction on Unstructured Meshes of Hexahedra · IEEE Trans. Vis. Comput. Graph. 2022
Geometric modeling and processing
mesh processing
0.612022
Identification and Classification of Off-Vertex Critical Points for Contour Tree Construction on Unstructured Meshes of Hexahedra · IEEE Trans. Vis. Comput. Graph. 2022
Visualization and visual analytics
topological data analysis
0.612022
Identification and Classification of Off-Vertex Critical Points for Contour Tree Construction on Unstructured Meshes of Hexahedra · IEEE Trans. Vis. Comput. Graph. 2022
Computational science and engineering › computational fluid dynamics
turbulence simulation
0.212016
Towards green aviation with python at petascale · SC 2016
High-performance computing › scientific computing systems
computational fluid dynamics
0.212016
Towards green aviation with python at petascale · SC 2016
High-performance computing › large-scale simulation
petascale simulation
0.212016
Towards green aviation with python at petascale · SC 2016
High-performance computing
unstructured mesh computation
0.212016
Towards green aviation with python at petascale · SC 2016

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

gradient analysis · 0.6asymptotic decider · 0.6runtime code generation · 0.5python-based solver · 0.5
YearPublicationVenuePosition
2022 Identification and Classification of Off-Vertex Critical Points for Contour Tree Construction on Unstructured Meshes of Hexahedra
abstract
The topology of isosurfaces changes at isovalues of critical points, making such points an important feature when building contour trees or Morse-Smale complexes. Hexahedral elements with linear interpolants can contain additional off-vertex critical points in element bodies and on element faces. Moreover, a point on the face of a hexahedron which is critical in the element-local context is not necessarily critical in the global context. Weber et al. (2002) introduce a method to determine whether critical points on faces are also critical in the global context, based on the gradient of the asymptotic decider (G. M. Nielson and B. Hamann) (1991) in each element that shares the face. However, as defined, the method of Weber et al. contains an error, and can lead to incorrect results. In this work we correct the error.
Marius K. Koch, Paul H. J. Kelly, Peter E. Vincent
IEEE Trans. Vis. Comput. Graph.3
2016 Towards green aviation with python at petascale
abstract
Accurate simulation of unsteady turbulent flow is critical for improved design of greener aircraft that are quieter and more fuel-efficient. We demonstrate application of PyFR, a Python based computational fluid dynamics solver, to petascale simulation of such flow problems. Rationale behind algorithmic choices, which offer increased levels of accuracy and enable sustained computation at up to 58% of peak DP-FLOP/s on unstructured grids, will be discussed in the context of modern hardware. A range of software innovations will also be detailed, including use of runtime code generation, which enables PyFR to efficiently target multiple platforms, including heterogeneous systems, via a single implementation. Finally, results will be presented from a fullscale simulation of flow over a low-pressure turbine blade cascade, along with weak/strong scaling statistics from the Piz Daint and Titan supercomputers, and performance data demonstrating sustained computation at up to 13.7 DP-PFLOP/s.
Peter E. Vincent, Freddie D. Witherden, Brian C. Vermeire, Jin Seok Park, Arvind Iyer
SC1