activity
20202026
most citedTasking framework for Adaptive Speculative Parallel Mesh Generation

10 citations · 14 across the 6 of their papers we have counts for

collaborators

7 papers

cs.CE2026

A Survey of AI Methods for Geometry Preparation and Mesh Generation in Engineering Simulation

Steven Owen, Nathan Brown, Nikos Chrisochoides +8

Artificial intelligence is beginning to reduce the manual effort in the CAD-to-mesh pipeline. Written for meshing and geometry practitioners with limited AI background, this survey…

cs.DC2024★ 10 cited

Tasking framework for Adaptive Speculative Parallel Mesh Generation

Christos Tsolakis, Polykarpos Thomadakis, Nikos Chrisochoides

Handling the ever-increasing complexity of mesh generation codes along with the intricacies of newer hardware often results in codes that are both difficult to comprehend and maint…

cs.DC2023

Towards Distributed Semi-speculative Adaptive Anisotropic Parallel Mesh Generation

Kevin Garner, Christos Tsolakis, Polykarpos Thomadakis +1

This paper presents the foundational elements of a distributed memory method for mesh generation that is designed to leverage concurrency offered by large-scale computing. To achie…

cs.DC2023★ 1 cited

Experience with Distributed Memory Delaunay-based Image-to-Mesh Conversion Implementation

Polykarpos Thomadakis, Nikos Chrisochoides

This paper presents some of our findings on the scalability of parallel 3D mesh generation on distributed memory machines. The primary objective of this study was to evaluate a dis…

cs.DC2023★ 1 cited

Runtime Support for Performance Portability on Heterogeneous Distributed Platforms

Polykarpos Thomadakis, Nikos Chrisochoides

Hardware heterogeneity is here to stay for high-performance computing. Large-scale systems are currently equipped with multiple GPU accelerators per compute node and are expected t…

cs.DC2022★ 1 cited

Towards Performance Portable Programming for Distributed Heterogeneous Systems

Polykarpos Thomadakis, Nikos Chrisochoides

Hardware heterogeneity is here to stay for high-performance computing. Large-scale systems are currently equipped with multiple GPU accelerators per compute node and are expected t…

cs.CV2020★ 1 cited

Using Machine Learning for Particle Track Identification in the CLAS12 Detector

Polykarpos Thomadakis, Angelos Angelopoulos, Gagik Gavalian +1

Particle track reconstruction is the most computationally intensive process in nuclear physics experiments. Traditional algorithms use a combinatorial approach that exhaustively te…