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检索条件"机构=Advanced Parallel Computing Lab"
13 条 记 录,以下是1-10 订阅
排序:
Running Simulations in HPC and Cloud Resources by Implementing Enhanced TOSCA Workflows
Running Simulations in HPC and Cloud Resources by Implementi...
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International Conference on High Performance computing & Simulation (HPCS)
作者: Javier Carnero Francisco Javier Nieto Advanced Parallel Computing Lab ATOS Seville Spain Advanced Parallel Computing Lab ATOS Bilbao Spain
In general, one of the complexities of large simulations is related to the usage of the heterogeneous computational resources that are needed to execute them. The definition of workflows, usually linked to concrete or... 详细信息
来源: 评论
PRACTICAL MASSIVELY parallel MONTE-CARLO TREE SEARCH APPLIED TO MOLECULAR DESIGN  9
PRACTICAL MASSIVELY PARALLEL MONTE-CARLO TREE SEARCH APPLIED...
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9th International Conference on Learning Representations, ICLR 2021
作者: Yang, Xiufeng Aasawat, Tanuj Kr Yoshizoe, Kazuki Chugai Pharmaceutical Co. Ltd Japan Parallel Computing Lab - India Intel Labs India RIKEN Center for Advanced Intelligence Project Japan
It is common practice to use large computational resources to train neural networks, known from many examples, such as reinforcement learning applications. However, while massively parallel computing is often used for... 详细信息
来源: 评论
POPA: Expressing High and Portable Performance across Spatial and Vector Architectures for Tensor Computations  24
POPA: Expressing High and Portable Performance across Spatia...
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32nd ACM International Symposium on Field-Programmable Gate Arrays, FPGA 2024
作者: Hao, Xiaochen Rong, Hongbo Zhang, Mingzhe Sun, Ce Jiang, Hong Liang, Yun Peking University China Parallel Computing Lab Intel United States Tsinghua University China University of Science and Technology of China China Intel United States Peking University & Beijing Advanced Innovation Center for Integrated Circuits China
This paper aims at high and portable performance for tensor computations across spatial (e.g., FPGAs) and vector architectures (e.g., GPUs). The state-of-the-art usually address performance portability across vector a... 详细信息
来源: 评论
Delayed Difference Scheme for Large Scale Scientific Simulations
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Physical Review Letters 2014年 第21期113卷 218701-218701页
作者: Dheevatsa Mudigere Sunil D. Sherlekar Santosh Ansumali Parallel Computing Lab Intel Labs Bangalore 560103 India Engineering Mechanics Unit Jawaharlal Nehru Centre for Advanced Scientific Research Jakkur Bangalore 560064 India
We argue that the current heterogeneous computing environment mimics a complex nonlinear system which needs to borrow the concept of time-scale separation and the delayed difference approach from statistical mechanics... 详细信息
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Data structure and movement for lattice-based simulations
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Physical Review E 2013年 第1期88卷 013314-013314页
作者: Aniruddha G. Shet Shahajhan H. Sorathiya Siddharth Krithivasan Anand M. Deshpande Bharat Kaul Sunil D. Sherlekar Santosh Ansumali Parallel Computing Lab Intel Labs Bangalore 560103 India Engineering Mechanics Unit Jawaharlal Nehru Centre for Advanced Scientific Research Jakkur Bangalore 560064 India
We show that for the lattice Boltzmann model, the existing paradigm in computer science for the choice of the data structure is suboptimal. In this paper we use the requirements of physical symmetry necessary for reco... 详细信息
来源: 评论
Practical massively parallel monte-carlo tree search applied to molecular design
arXiv
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arXiv 2020年
作者: Yang, Xiufeng Aasawat, Tanuj Kr Yoshizoe, Kazuki Chugai Pharmaceutical Co. Ltd Parallel Computing Lab - India Intel Labs RIKEN Center for Advanced Intelligence Project
It is common practice to use large computational resources to train neural networks, known from many examples, such as reinforcement learning applications. However, while massively parallel computing is often used for... 详细信息
来源: 评论
Characterizing Multi-media Retrieval Applications
Characterizing Multi-media Retrieval Applications
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International Conference on parallel Processing (ICPP)
作者: Yunping Lu Xin Wang Weihua Zhang Yi Li Wenyun Zhao Shanghai Key Laboratory of Data Science Fudan University Shanghai China State Key Lab of Mathematical Engineering and Advanced Computing Wuxi China Software School Fudan University Shanghai China Parallel Processing Institute Fudan University Shanghai China
Multimedia data, especially image and video data, have become one of the most overwhelming data types on the Internet recently. Considering the user experience and real application requirements, multimedia data always... 详细信息
来源: 评论
Quantum Neuron: An elementary building block for machine learning on quantum computers
arXiv
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arXiv 2017年
作者: Cao, Yudong Guerreschi, Gian Giacomo Aspuru-Guzik, Alán Department of Chemistry and Chemical Biology Harvard University CambridgeMA02138 United States Parallel Computing Lab Intel Corporation Santa ClaraCA95054 Canadian Institute for Advanced Research TorontoONM5G 1Z8 Canada
Even the most sophisticated artificial neural networks are built by aggregating substantially identical units called neurons. A neuron receives multiple signals, internally combines them, and applies a non-linear func... 详细信息
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GREYONE: data flow sensitive fuzzing  20
GREYONE: data flow sensitive fuzzing
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Proceedings of the 29th USENIX Conference on Security Symposium
作者: Shuitao Gan Chao Zhang Peng Chen Bodong Zhao Xiaojun Qin Dong Wu Zuoning Chen State Key Laboratory of Mathematical Engineering and Advanced Computing Institute for Network Science and Cyberspace Tsinghua University and Beijing National Research Center for Information Science and Technology ByteDance AI lab Institute for Network Science and Cyberspace Tsinghua University National Research Center of Parallel Computer Engineering and Technology
Data flow analysis (e.g., dynamic taint analysis) has proven to be useful for guiding fuzzers to explore hard-to-reach code and find vulnerabilities. However, traditional taint analysis is labor-intensive, inaccurate ...
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Resource-Aware Multi-Criteria Vehicle Participation for Federated Learning in Internet of Vehicles
SSRN
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SSRN 2023年
作者: Wen, Jie Zhang, Jingbo Zhang, Zhixia Cui, Zhihua Cai, Xingjuan Chen, Jinjun The Shanxi Key Laboratory of Advanced Control and Equipment intelligence Taiyuan University of Science and Technology Shanxi Taiyuan China The Shanxi Key Laboratory of Big Data Analysis and Parallel Computing Taiyuan University of Science and Technology Shanxi Taiyuan China The State Key Lab for Novel Software Technology Nanjing University China Department of Computing Technologies Swinburne University of Technology Melbourne Australia
Federated learning (FL), as a safe distributed training mode, provides strong support for the edge intelligence of the Internet of Vehicles (IoV) to realize efficient collaborative control and safe data sharing. Howev... 详细信息
来源: 评论