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Junzhou Yin

dblp:441/1556 · DBLP profile ↗
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1ranked-venue papers
0as first author
1since 2021 · last 2026
0009-0003-4360-0997ORCID · reported

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

Graphics, 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 architecture, parallel and distributed computing, and storage systems
1 paper
High-performance computing · 100%
Theoretical computer science
1 paper
Algorithms and data structures · 100%

Topics — the 1 heaviest of 2, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Algorithms and data structures › matrix approximation
subspace approximation
0.312026
Component Modes Synthesis Method with Multiple Partitions for Large-scale Eigenvalue Problem · ACM Trans. Graph. 2026

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

domain decomposition · 2.0component mode synthesis · 2.0MPI · 2.0
YearPublicationVenuePosition
2026 Component Modes Synthesis Method with Multiple Partitions for Large-scale Eigenvalue Problem
abstract
For large-scale modal analysis problems, Component Mode Synthesis (CMS) methods are very attractive, as they reduce the global problem into smaller subproblems on substructures. However, the substructure bases do not span the desired solution space efficiently, thus the error decreases slowly as the number of substructure eigenmodes increases. We demonstrate that a much more effective subspace can be constructed by combining substructure eigenmodes from multiple spatially staggered partitions of the input domain. To further accelerate the method, we replace the bases on the substructure interfaces by low-frequency approximations excited from the substructure eigenmodes from other partitions. Compared with typical CMS methods, our approach improves the accuracy by 3 orders of magnitude and achieves better strong and weak scaling in both time and memory cost. The advantages inherited from CMS, i.e. fast local updating and low communication cost between Message Passing Interface (MPI) ranks in large-scale distributed computing clusters are verified as well.
Chongyao Zhao, Junzhou Yin, Hujun Bao, Jin Huang 0001
ACM Trans. Graph.2