Jue Wang
Chinese Academy of Sciences
68 Papers
180 Citations
Jue Wang is an academic researcher from Chinese Academy of Sciences. The author has contributed to research in topics: Computer science & Scheduling (computing). The author has an hindex of 9, co-authored 50 publications. Previous affiliations of Jue Wang include University of Science and Technology Beijing.
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Papers
An Adaptive Synchronous Parallel Strategy for Distributed Machine Learning
TL;DR: The adaptive synchronous parallel strategy for distributed ML is proposed that fully improves clustering performance, and it ensures the accuracy and convergence speed of the model, increases the model training speed, and has good expansibility.
48
A photovoltaic power output dataset: Multi-source photovoltaic power output dataset with Python toolkit
Tiechui Yao,Jue Wang,Haoyan Wu,Pei Zhang,Pei Zhang,Shigang Li,Yangang Wang,Xuebin Chi,Min Shi +8 more
TL;DR: A PV power output dataset (PVOD), which contains metadata, numerical weather prediction data, and local measurements data from 10 PV systems located in China, is released.
37
Massively Scaling the Metal Microscopic Damage Simulation on Sunway TaihuLight Supercomputer
Shigang Li,Baodong Wu,Yunquan Zhang,Xianmeng Wang,Jianjiang Li,Changjun Hu,Jue Wang,Feng Yangde,Nie Ningming +8 more
- 13 Aug 2018
TL;DR: A multiscale modeling approach that couples Molecular Dynamics (MD) with Kinetic Monte Carlo (KMC) is used, which significantly reduces the memory consumption of metal materials and proposes an on-demand communication strategy for KMC to remarkably reduce the communication overhead.
25
Parallel simulation of high-dimensional American option pricing based on CPU versus MIC
TL;DR: Many Integrated Core (MIC) parallelization and acceleration techniques are presented, which result in significant numerical acceleration for large‐scale simulations, and observe speed‐ups of 21‐fold and 28‐fold for CPU and MIC, respectively, over conventional means.
24
An Automatically Learning and Discovering Human Fishing Behaviors Scheme for CPSCN
TL;DR: An identification model based on multi-step clustering algorithm (MSC-FBI) is proposed to automatically learn and discover fishing behaviors at sea and the experimental results illustrate the proposed model’s superior performance.