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

Budvin Edippuliarachchi

dblp:360/1688 · DBLP profile ↗
← Back
1ranked-venue papers
1as first author
1since 2021 · last 2025
0000-0002-8122-3462ORCID · reported

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

Systems, architecture and hardware · 1 · 1 first-author · 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 architecture, parallel and distributed computing, and storage systems
1 paper
High-performance computing · 75% Parallel and multicore computing · 25%
Theoretical computer science
1 paper
Graph algorithms and graph theory · 100%

Topics — the 5 heaviest of 5, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
High-performance computing › scientific computing systems
adaptive mesh refinement
0.912025
AMRaCut: Scalable Partitioning for Adaptive Mesh Refinement · SC 2025
High-performance computing › domain decomposition
mesh partitioning
0.912025
AMRaCut: Scalable Partitioning for Adaptive Mesh Refinement · SC 2025
Parallel and multicore computing
parallel algorithms
0.912025
AMRaCut: Scalable Partitioning for Adaptive Mesh Refinement · SC 2025
High-performance computing
scientific computing systems
0.912025
AMRaCut: Scalable Partitioning for Adaptive Mesh Refinement · SC 2025
Graph algorithms and graph theory
graph partitioning
0.312025
AMRaCut: Scalable Partitioning for Adaptive Mesh Refinement · SC 2025

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

parallel label propagation · 1.7graph diffusion · 1.7
YearPublicationVenuePosition
2025 AMRaCut: Scalable Partitioning for Adaptive Mesh Refinement
abstract
Mesh partitioning is critical for scalable distributed PDE solvers. Traditional methods like spatial ordering and multi-level graph partitioning have significant tradeoffs between partition quality and parallel scalability. We present AMRaCut, a distributed-parallel mesh partitioner that bridges this gap using parallel label propagation and graph diffusion. It operates mostly locally on initial partitions, limiting inter-process communications to neighboring processes. This locality is especially effective in AMR, where mesh evolves dynamically with mostly local changes.
Budvin Edippuliarachchi, David Van Komen, Hari Sundar
SC1