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Study on Deep Reinforcement Learning for Multi-Task Scheduling in Cloud Manufacturing

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SSRN

SSRN 2022年

作者： Xiao, Jiuhong Cai, Yishuai Wang, Wanru Chen, Yong College of Economic and Social Development Nankai University Tianjin300072 China Institute for Quantum Information State Key Laboratory of High Performance Computing College of Computer Science and Technology National University of Defense Technology Changsha410073 China School of Information Management and Artificial Intelligence Zhejiang University of Finance and Economics Hangzhou310018 China College of Civil Engineering and Architecture Zhejiang University Hangzhou310058 China

Cloud manufacturing is an emerging manufacturing paradigm, which enables the simultaneous processing of multiple manufacturing tasks submitted by customers through centralized management and planning of manufacturing services offered by distributed enterprises. To date, various meta-heuristics algorithms such as ant colony optimization algorithm, and genetic algorithm have been widely employed to solve the cloud manufacturing multi-task scheduling (CMMS) problem to improve the efficiency of multi-task scheduling. However, these algorithms need sophisticated design procedures and laborious parameter tuning, making their adaptation to dynamic, large-scale manufacturing environments difficult. Deep reinforcement learning (DRL), because of its efficiency and flexibility, has recently attracted increasing attention from researchers and has shown promising performance in solving combinatorial optimization problems. Nevertheless, little is known about the performance of DRL technologies in solving the CMMS problem. Therefore, in this study, three state-of-the-art DRL technologies, including proximal policy optimization (PPO), deep deterministic policy gradient, and normalized advantage function, are selected to systematically investigate the potential of DRL in solving the CMMS problem. A comprehensive comparison of these DRL technologies is conducted in terms of effectiveness and efficiency, robustness and adaptability, and scalability. Experimental results indicate that PPO is superior to other technologies in solving the CMMS problem. © 2022, The Authors. All rights reserved.

关键词： Reinforcement learning

Benchmarking SpMV Methods on Many-Core Platforms 1st

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Benchmarking SpMV Methods on Many-Core Platforms

1st International Symposium on Benchmarking, Measuring, and Optimization, Bench 2018

作者： Xie, Biwei Jia, Zhen Bao, Yungang State Key Laboratory of Computer Architecture Institute of Computing Technology Chinese Academy of Sciences Beijing China Department of Computer Science Princeton University Princeton United States University of Chinese Academy of Sciences Beijing China

ISBN: (纸本)9783030328122

SpMV is an essential kernel existing in many HPC and data center applications. Meanwhile, the emerging many-core hardware provides promising computational power, and is widely used for acceleration. Many methods and formats have been proposed aiming at better performance of SpMV on many-core platforms. However, there is still lack of a comprehensive comparison of SpMV methods to show their performance difference on sparse matrices with various sparse patterns. Moreover, there is still no systematic work to bridge the gap between SpMV performance and sparse pattern. In this paper, we investigate the performance of 27 SpMV methods with 1500+ sparse matrices on two many-core platforms: Intel Xeon Phi (Knights Landing 7250) and Nvidia GPGPU (Tesla M40). Our work shows that no single SpMV methods is optimal for all sparse patterns, but some methods can achieve approximately the best performance on most sparse matrices. We further select 13 features to describe the sparse pattern and analyze their correlations to the performance of each SpMV method. Our observations should help other researchers and practitioners to better understand the SpMV performance and provide implications to guide the selection of suitable SpMV method. © 2019, Springer Nature Switzerland AG.

关键词： Benchmarking

High-performance parallel classical scheme for simulating shallow quantum circuits

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arXiv 2021年

作者： Zhang, Shihao Bao, Jiacheng Sun, Yifan Li, Lvzhou Sun, Houjun Zhang, Xiangdong Key Laboratory of Advanced Optoelectronic Quantum Architecture and Measurements of Ministry of Education Beijing Key Laboratory of Nanophotonics & Ultrafine Optoelectronic Systems School of Physics Beijing Institute of Technology Beijing100081 China Beijing Key Laboratory of Millimeter Wave and Terahertz Techniques School of Information and Electronics Beijing Institute of Technology Beijing100081 China School of Computer Science and Engineering Sun Yat-Sen University Guangzhou510006 China

Recently, constant-depth quantum circuits are proved more powerful than their classical counterparts at solving certain problems, e.g., the two-dimensional (2D) hidden linear function (HLF) problem regarding a symmetric binary matrix. To further investigate the boundary between classical and quantum computing models, in this work we propose a high-performance two-stage classical scheme to solve a full-sampling variant of the 2D HLF problem, which combines traditional classical parallel algorithms and a gate-based classical circuit model together for exactly simulating the target shallow quantum circuits. Under reasonable parameter assumptions, a theoretical analysis reveals our classical simulator consumes less runtime than that of near-term quantum processors for most problem instances. Furthermore, we demonstrate the typical all-connected 2D grid instances by moderate FPGA circuits, and show our designed parallel scheme is a practically scalable, high-efficient and operationally convenient tool for simulating and verifying graph-state circuits performed by current quantum hardware. © 2021, CC BY.

关键词： Timing circuits

Geographic information science in the era of geospatial big data: A cyberspace perspective

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The Innovation 2022年第5期3卷 21-22页

作者： Xintao Liu Min Chen Christophe Claramunt Michael Batty Mei-Po Kwan Ahmad MSenousi Tao Cheng Josef Strobl Arzu Cöltekin John Wilson Temenoujka Bandrova Milan Konecny Paul MTorrens Fengyuan Zhang Li He Jinfeng Wang Carlo Ratti Olaf Kolditz Alexander Klippel Songnian Li Hui Lin Guonian Lu Department of Land Surveying and Geo-Informatics Hong Kong Polytechnic UniversityHong Kong SARKowloonChina Key Laboratory of Virtual Geographic Environment(Ministry of Education of PRC) Nanjing Normal UniversityNanjing 210023China The French Naval Academy Research Institute 29240 Lanveoc-PoulmicFrance The Bartlett Centre for Advanced Spatial Analysis(CASA) University College London(UCL)WC1E 6BT LondonUK Institute of Space and Earth Information Science The Chinese University of Hong KongHong Kong SARShatin 999077China Department of Civil Engineering Cairo University12613 CairoEgypt SpaceTimeLab Department of CivilEnvironmental and Geomatic EngineeringUniversity College London(UCL)WC1E 6BT LondonUK Department of Geoinformatics University of Salzburg5020 SalzburgAustria Institute of Interactive Technologies(IIT) University of Applied Sciences and Arts Northwestern Switzerland FHNW5210 WindischSwitzerland Spatial Sciences Institute University of Southern CaliforniaLos AngelesCA 90007USA Laboratory on Cartography Department of Photogrammetry and CartographyUniversity of ArchitectureCivil Engineering and Geodesy1164 SofiaBulgaria Laboratory on Geoinformatics and Cartography Department of GeographyMasaryk University61137 BrnoCeskáRepublika Computer Science and Engineering Tandon School&Center for Urban Science+ProgressNew York UniversityNew YorkNY 10012USA School of Humanities and Social Science Xi’an Jiaotong UniversityXi’an 710049China Institute of Geographic Sciences and Natural Resources Research Chinese Academy of SciencesBeijing 100101China Department of Urban Studies and Planning Massachusetts Institute of TechnologyCambridgeMA 02139USA Centre for Environmental Research Helmholtz Centre for Environmental Research-UFZ04318 LeipzigGermany Laboratory of Geo-information Science and Remote Sensing Wageningen University&ResearchWageningen 6708the Netherlands Department of Civil Engineering Toronto Metropolitan UniversityTorontoON M5B 2K3Canada School of Geography and Env

The advent of information and communication technology and the Internet of Things have led our society toward a digital *** proliferation of personal computers,smartphones,intelligent autonomous sensors,and pervasive network interactions with individuals have gradually shifted human activities from offline to online and from in person to *** transformation has brought a series of challenges in a variety of fields,such as the dilemma of placelessness,some aspects of timelessness(no time relevance),and the changing relevance of distance in the field of geographic information science(GIscience).In the last two decades,“cyber thinking”in GIscience has received significant attention from different *** instance,human activities in“cyberspace”need to be reconsidered when coupled with the geographic space to observe the first law of geography.

关键词： computer cyber instance

A Field Programmable Process-In-Memory architecture Based on RRAM Technology

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A Field Programmable Process-In-Memory Architecture Based on...

International Conference on Mechanical, Control and computer Engineering (ICMCCE)

作者： Yuan Liang Longxiang Yin Ning Xu School of Computer Science and Technology Wuhan University of Technology Wuhan China State Key Laboratory of Computer Architecture Institute of Computing Technology Chinese Academy of Sciences Beijing China School of Information Engineering Wuhan University of Technology Wuhan China

Conventional reconfigurable architectures, e.g., Field-Programmable Gate Array (FPGA), are confronted with the inflexibility of the on-chip local memory architecture and the scarce memory resource, which result in the inflexibility to deal with the current memory-thirsty data-intensive applications. Emerging nonvolatile devices, such as resistive random-access memory (RRAM), can operate in a logic-in-memory way, i.e., they can act as both a nonvolatile memory element and a switch, and thus, have the potential to provide higher flexibility and data processing ability. We propose a field-programmable logic-in-memory architecture based on RRAM technology, which is a two-dimensional tile array, where each tile is an RRAM-based crossbar array and can be remolded into three basic modes including logic, interconnect and memory. We further develop a specific adaptive algorithm for placement and routing, which can well exploit the logic-in-memory architecture characteristics. Comparing with traditional FPGAs, our architecture shows 1.9x lower power consumption, 2.8x lower delay, and 5.6x higher performance.

关键词： Nonvolatile memory computer architecture Switches Routing Delays System-on-chip Reconfigurable architectures

Improving the performance of stochastic local search for maximum vertex weight clique problem using programming by optimization

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arXiv 2020年

作者： Chu, Yi Luo, Chuan Hoos, Holger H. Lin, Qingwei You, Haihang State Key Laboratory of Computer Architecture Institute of Computing Technology Chinese Academy of Sciences Beijing100190 China University of Chinese Academy of Sciences Beijing100049 China Microsoft Research China Leiden Institute of Advanced Computer Science Leiden University Leiden Netherlands

The maximum vertex weight clique problem (MVWCP) is an important generalization of the maximum clique problem (MCP) that has a wide range of real-world applications. In situations where rigorous guarantees regarding the optimality of solutions are not required, MVWCP is usually solved using stochastic local search (SLS) algorithms, which also define the state of the art for solving this problem. However, there is no single SLS algorithm which gives the best performance across all classes of MVWCP instances, and it is challenging to effectively identify the most suitable algorithm for each class of MVWCP instances. In this work, we follow the paradigm of Programming by Optimization (PbO) to develop a new, flexible and highly parametric SLS framework for solving MVWCP, combining, for the first time, a broad range of effective heuristic mechanisms. By automatically configuring this PbO-MWC framework, we achieve substantial advances in the state-of-the-art in solving MVWCP over a broad range of prominent benchmarks, including two derived from real-world applications in transplantation medicine (kidney exchange) and assessment of research excellence. Copyright © 2020, The Authors. All rights reserved.

关键词： Local search (optimization)

SPECBOX: A label-based transparent speculation scheme against transient execution attacks

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arXiv 2021年

作者： Tang, Bowen Wu, Chenggang Wang, Zhe Jia, Lichen Yew, Pen-Chung Cheng, Yueqiang Zhang, Yinqian Wang, Chenxi Xu, Guoqing Harry State Key Laboratory of Computer Architecture Institute of Computing Technology Chinese Academy of Sciences China University of the Chinese Academy of Sciences China University of Minnesota Twin Cities United States NIO Security Research United States Southern University of Science and Technology China University of California Los Angeles United States

Speculative execution techniques have been a cornerstone of modern processors to improve instruction-level parallelism. However, recent studies showed that this kind of techniques could be exploited by attackers to leak secret data via transient execution attacks, such as Spectre. Many defenses are proposed to address this problem, but they all face various challenges: (1) Tracking data flow in the instruction pipeline could comprehensively address this problem, but it could cause pipeline stalls and incur high performance overhead;(2) Making side effect of speculative execution imperceptible to attackers, but it often needs additional storage components and complicated data movement operations. In this paper, we propose a label-based transparent speculation scheme called SPECBOX. It dynamically partitions the cache system to isolate speculative data and non-speculative data, which can prevent transient execution from being observed by subsequent execution. Moreover, it uses thread ownership semaphores to prevent speculative data from being accessed across cores. In addition, SPECBOX also enhances the auxiliary components in the cache system against transient execution attacks, such as hardware prefetcher. Our security analysis shows that SPECBOX is secure and the performance evaluation shows that the performance overhead on SPEC CPU 2006 and PARSEC-3.0 benchmarks is small. Copyright © 2021, The Authors. All rights reserved.

关键词： Digital storage

Fusion-Catalyzed Pruning for Optimizing Deep Learning on Intelligent Edge Devices

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arXiv 2020年

作者： Li, Guangli Ma, Xiu Wang, Xueying Liu, Lei Xue, Jingling Feng, Xiaobing State Key Laboratory of Computer Architecture Institute of Computing Technology Chinese Academy of Sciences Beijing100190 China University of Chinese Academy of Sciences Beijing100190 China College of Computer Science and Technology Jilin University Changchun130012 China School of Computer Science and Engineering University of New South Wales SydneyNSW2052 Australia

The increasing computational cost of deep neural network models limits the applicability of intelligent applications on resource-constrained edge devices. While a number of neural network pruning methods have been proposed to compress the models, prevailing approaches focus only on parametric operators (e.g., convolution), which may miss optimization opportunities. In this paper, we present a novel fusion-catalyzed pruning approach, called FUPRUNER, which simultaneously optimizes the parametric and non-parametric operators for accelerating neural networks. We introduce an aggressive fusion method to equivalently transform a model, which extends the optimization space of pruning and enables non-parametric operators to be pruned in a similar manner as parametric operators, and a dynamic filter pruning method is applied to decrease the computational cost of models while retaining the accuracy requirement. Moreover, FUPRUNER provides configurable optimization options for controlling fusion and pruning, allowing much more flexible performance-accuracy trade-offs to be made. Evaluation with state-of-the-art residual neural networks on five representative intelligent edge platforms, Jetson TX2, Jetson Nano, Edge TPU, NCS, and NCS2, demonstrates the effectiveness of our approach, which can accelerate the inference of models on CIFAR-10 and ImageNet datasets. Copyright © 2020, The Authors. All rights reserved.

关键词： Economic and social effects

BigNeuron: a resource to benchmark and predict performance of algorithms for automated tracing of neurons in light microscopy datasets

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Nature methods 2023年第6期20卷 824-835页

作者： Linus Manubens-Gil Zhi Zhou Hanbo Chen Arvind Ramanathan Xiaoxiao Liu Yufeng Liu Alessandro Bria Todd Gillette Zongcai Ruan Jian Yang Miroslav Radojević Ting Zhao Li Cheng Lei Qu Siqi Liu Kristofer E Bouchard Lin Gu Weidong Cai Shuiwang Ji Badrinath Roysam Ching-Wei Wang Hongchuan Yu Amos Sironi Daniel Maxim Iascone Jie Zhou Erhan Bas Eduardo Conde-Sousa Paulo Aguiar Xiang Li Yujie Li Sumit Nanda Yuan Wang Leila Muresan Pascal Fua Bing Ye Hai-Yan He Jochen F Staiger Manuel Peter Daniel N Cox Michel Simonneau Marcel Oberlaender Gregory Jefferis Kei Ito Paloma Gonzalez-Bellido Jinhyun Kim Edwin Rubel Hollis T Cline Hongkui Zeng Aljoscha Nern Ann-Shyn Chiang Jianhua Yao Jane Roskams Rick Livesey Janine Stevens Tianming Liu Chinh Dang Yike Guo Ning Zhong Georgia Tourassi Sean Hill Michael Hawrylycz Christof Koch Erik Meijering Giorgio A Ascoli Hanchuan Peng Institute for Brain and Intelligence Southeast University Nanjing China. Microsoft Corporation Redmond WA USA. Tencent AI Lab Bellevue WA USA. Computing Environment and Life Sciences Directorate Argonne National Laboratory Lemont IL USA. Kaya Medical Seattle WA USA. University of Cassino and Southern Lazio Cassino Italy. Center for Neural Informatics Structures and Plasticity Krasnow Institute for Advanced Study George Mason University Fairfax VA USA. Faculty of Information Technology Beijing University of Technology Beijing China. Beijing International Collaboration Base on Brain Informatics and Wisdom Services Beijing China. Nuctech Netherlands Rotterdam the Netherlands. Janelia Research Campus Howard Hughes Medical Institute Ashburn VA USA. Department of Electrical and Computer Engineering University of Alberta Edmonton Alberta Canada. Ministry of Education Key Laboratory of Intelligent Computation and Signal Processing Anhui University Hefei China. Paige AI New York NY USA. Scientific Data Division and Biological Systems and Engineering Division Lawrence Berkeley National Lab Berkeley CA USA. Helen Wills Neuroscience Institute and Redwood Center for Theoretical Neuroscience UC Berkeley Berkeley CA USA. RIKEN AIP Tokyo Japan. Research Center for Advanced Science and Technology (RCAST) The University of Tokyo Tokyo Japan. School of Computer Science University of Sydney Sydney New South Wales Australia. Texas A&M University College Station TX USA. Cullen College of Engineering University of Houston Houston TX USA. Graduate Institute of Biomedical Engineering National Taiwan University of Science and Technology Taipei Taiwan. National Centre for Computer Animation Bournemouth University Poole UK. PROPHESEE Paris France. Department of Neuroscience Columbia University New York NY USA. Mortimer B. Zuckerman Mind Brain Behavior Institute Columbia University New York NY USA. Department of Computer Science Northern Illinois Universit

BigNeuron is an open community bench-testing platform with the goal of setting open standards for accurate and fast automatic neuron tracing. We gathered a diverse set of image volumes across several species that is representative of the data obtained in many neuroscience laboratories interested in neuron tracing. Here, we report generated gold standard manual annotations for a subset of the available imaging datasets and quantified tracing quality for 35 automatic tracing algorithms. The goal of generating such a hand-curated diverse dataset is to advance the development of tracing algorithms and enable generalizable benchmarking. Together with image quality features, we pooled the data in an interactive web application that enables users and developers to perform principal component analysis, t-distributed stochastic neighbor embedding, correlation and clustering, visualization of imaging and tracing data, and benchmarking of automatic tracing algorithms in user-defined data subsets. The image quality metrics explain most of the variance in the data, followed by neuromorphological features related to neuron size. We observed that diverse algorithms can provide complementary information to obtain accurate results and developed a method to iteratively combine methods and generate consensus reconstructions. The consensus trees obtained provide estimates of the neuron structure ground truth that typically outperform single algorithms in noisy datasets. However, specific algorithms may outperform the consensus tree strategy in specific imaging conditions. Finally, to aid users in predicting the most accurate automatic tracing results without manual annotations for comparison, we used support vector machine regression to predict reconstruction quality given an image volume and a set of automatic tracings.

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