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
Demo Data from VLDB 2026 / reviewers in the wild / expert

Jakub Homola

dblp:353/6937 · DBLP profile ↗
← Back
2ranked-venue papers
2as first author
2since 2021 · last 2026
0000-0001-9474-2774ORCID · corroborated

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

Systems, architecture and hardware · 2 · 2 first-author · 2 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 · 56% GPUs and heterogeneous computing · 44%

Topics — the 3 heaviest of 3, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
High-performance computing
domain decomposition		 0.912025
Utilizing Sparsity in the GPU-accelerated Assembly of Schur Complement Matrices in Domain Decomposition Methods · SC 2025
GPUs and heterogeneous computing › GPU-accelerated scientific computing
GPU-accelerated numerical computation		 0.912025
Utilizing Sparsity in the GPU-accelerated Assembly of Schur Complement Matrices in Domain Decomposition Methods · SC 2025
High-performance computing › sparse linear algebra
sparse matrix computation		 0.312025
Utilizing Sparsity in the GPU-accelerated Assembly of Schur Complement Matrices in Domain Decomposition Methods · SC 2025

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

sparsity exploitation · 0.9GPU acceleration · 0.9
YearPublicationVenuePosition
2026 GPU acceleration of hybrid FETI solver for problems of transient nonlinear dynamics
Jakub Homola, Ondrej Meca, Lubomir Riha, Tomás Brzobohatý
 Future Gener. Comput. Syst.1
2025 Utilizing Sparsity in the GPU-accelerated Assembly of Schur Complement Matrices in Domain Decomposition Methods
abstract
Schur complement matrices emerge in many domain decomposition methods that can utilize supercomputers to solve complex engineering problems. As most of today’s high-performance clusters’ performance lies in GPUs, these methods should also be accelerated.
Jakub Homola, Ondrej Meca, Lubomir Riha, Tomás Brzobohatý
 SC1