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Junben Weng

dblp:340/0308 · DBLP profile ↗
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1ranked-venue papers
0as first author
1since 2021 · last 2023
—ORCID · unresolved

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

Systems, architecture and hardware · 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 · 50% Processor architecture and microarchitecture · 25% Memory systems · 25%

Topics — the 4 heaviest of 4, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Memory systems
data locality
0.712023
Bio-ESMD: A Data Centric Implementation for Large-Scale Biological System Simulation on Sunway TaihuLight Supercomputer · IEEE Trans. Parallel Distributed Syst. 2023
Processor architecture and microarchitecture
many-core architecture
0.712023
Bio-ESMD: A Data Centric Implementation for Large-Scale Biological System Simulation on Sunway TaihuLight Supercomputer · IEEE Trans. Parallel Distributed Syst. 2023
High-performance computing › scientific computing systems
molecular dynamics simulation
0.712023
Bio-ESMD: A Data Centric Implementation for Large-Scale Biological System Simulation on Sunway TaihuLight Supercomputer · IEEE Trans. Parallel Distributed Syst. 2023
High-performance computing
scientific computing
0.712023
Bio-ESMD: A Data Centric Implementation for Large-Scale Biological System Simulation on Sunway TaihuLight Supercomputer · IEEE Trans. Parallel Distributed Syst. 2023

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

cell list data structure · 0.7bond lists · 0.7
YearPublicationVenuePosition
2023 Bio-ESMD: A Data Centric Implementation for Large-Scale Biological System Simulation on Sunway TaihuLight Supercomputer
abstract
Molecular dynamics (MD) simulations of biological systems are playing an increasingly important role in the research of pathogens and drugs. Most MD methods for biological simulations rely on the listed bonds which interact among specific groups of atoms identified by atom tags (unique atom tags regardless the storage location). However, efficient mapping of tags to atom locations is often challenging on modern many-core processors because data locality can not always be guaranteed for large-scale systems. In this paper, we present Bio-ESMD, a new MD implementation supporting listed bonds. Bio-ESMD is designed and developed based on our previously designed ESMD framework for many-core processors. In Bio-ESMD, we have introduced a data-centric approach for refactoring MD algorithms by reorganizing the cell list data structure to adopt bond lists with guaranteed data locality. Our implementation achieves speedups of over two compared to SW_GROMACS on Sunway TaihuLight. Furthermore, Bio-ESMD can simulate a system of 308.8 million atoms at 1.33 ns/day or 14.44 million atoms at 17.28 ns/day with linear weak scaling efficiency.
Xiaohui Duan, Junben Weng, Bertil Schmidt, Lin Gan 0001, Haohuan Fu, Wei Xue 0003, Guangwen Yang 0002
IEEE Trans. Parallel Distributed Syst.3