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Incentive-Scheduling Algorithms to Provide Green Computational Data Center

SN computer science 2021年第4期2卷 252页

作者： Haruna, Ahmed Abba Jung, Low Tan Arputharaj, Vijay Muhammad, L.J. College of Computer Science and Engineering University of Hafr Al Batin Al Jamiah Hafar Al Batin 39524d Saudi Arabia High Performance Computing Cloud Centre Universiti Teknologi PETRONAS Seri Iskandar Perak 32610 Malaysia Faculty of Science and Information Technology Skyline University Nigeria No. 2 Zaria Road Kano Nigeria Mathematics and Computer Science Department Federal University Kashere Gombe State Kashere Nigeria

The increased computational loads in grid servers are dissipating more heat to eventually amplifies the cooling demand in the data center (DC). This can lead to more submitted jobs missing their job completion deadlines. Unfortunately, the conventional DC job scheduling approaches do not provide compensation to the resource users on their submitted jobs that missed the deadlines. The absence of compensation may dissuade users from submitting jobs to the DCs. While the free air-cooling strategy is used elsewhere, it is not generally applicable in tropical countries such as Malaysia. A constant artificial cooling (air conditioning) is needed to sustain the DC long-hour operation. To solve this issue in the tropical region, green incentive-scheduling algorithms were devised to significantly save the cooling electricity consumption cost in DC and to be able to compensate users (as an incentive) for their submitted jobs that missed the job completion deadline(s). However, there is a need to further analyse the performance of the proposed green incentive-scheduling algorithms using a different benchmark traces file and to conduct a cost-saving comparison analysis between the green incentive-scheduling and the non-green incentive-scheduling algorithms. Therefore, this paper's authors observed and analysed the performance comparison of the proposed green incentive-based job scheduling algorithms using a benchmark traces file and conducted a cost-saving comparison analysis between the green incentive and the non-green incentive-based scheduling algorithms. Ultimately, the green incentive-scheduling algorithms are to show their impact on reducing the DC operation costs (e.g. electricity bills for cooling) and to provide fair compensation to grid users for a win–win situation. © 2021, The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd.

关键词： Data center Green computing Incentive-based job scheduling algorithm Server Temperature

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An Annotation-free Restoration Network for Cataractous Fundus Images

arXiv

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arXiv 2022年

作者： Li, Heng Liu, Haofeng Hu, Yan Fu, Huazhu Zhao, Yitian Miao, Hanpei Liu, Jiang The Department of Computer Science and Engineering Southern University of Science and Technology Shenzhen518055 China The Cixi Institute of Biomedical Engineering Ningbo Institute of Industrial Technology Chinese Academy of Sciences Ningbo315201 China The Institute of High Performance Computing Agency for Science Technology and Research Singapore Singapore Research Institute of Trustworthy Autonomous Systems Department of Computer Science and Engineering Southern University of Science and Technology Shenzhen518055 China

Cataracts are the leading cause of vision loss worldwide. Restoration algorithms are developed to improve the readability of cataract fundus images in order to increase the certainty in diagnosis and treatment for cataract patients. Unfortunately, the requirement of annotation limits the application of these algorithms in clinics. This paper proposes a network to annotation-freely restore cataractous fundus images (ArcNet) so as to boost the clinical practicability of restoration. Annotations are unnecessary in ArcNet, where the high-frequency component is extracted from fundus images to replace segmentation in the preservation of retinal structures. The restoration model is learned from the synthesized images and adapted to real cataract images. Extensive experiments are implemented to verify the performance and effectiveness of ArcNet. Favorable performance is achieved using ArcNet against state-of-the-art algorithms, and the diagnosis of ocular fundus diseases in cataract patients is promoted by ArcNet. The capability of properly restoring cataractous images in the absence of annotated data promises the proposed algorithm outstanding clinical practicability. Copyright © 2022, The Authors. All rights reserved.

关键词： Restoration

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Lane marking detection algorithm based on high-precision map and multisensor fusion

Lane marking detection algorithm based on high-precision map...

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作者： Yao, Haichang Chen, Chen Liu, Shangdong Li, Kui Ji, Yimu Huang, Guangyan Wang, Ruchuan School of Computer Science Nanjing University of Posts and Telecommunications Nanjing China School of Computer and Software Nanjing Institute of Industry Technology Nanjing China Industrial Software Engineering Technology Research and Development Center of Jiangsu Education Department Nanjing Institute of Industry Technology Nanjing China Jiangsu HPC and Intelligent Processing Engineer Research Center Nanjing China Institute of High Performance Computing and Bigdata Nanjing University of Posts and Telecommunications Nanjing China School of Information Technology Deakin University Melbourne Australia

In case of sharp road illumination changes, bad weather such as rain, snow or fog, wear or missing of the lane marking, the reflective water stain on the road surface, the shadow obstruction of the tree, and mixed lane markings and other signs, missing detection or wrong detection will occur for the traditional lane marking detection algorithm. In this manuscript, a lane marking detection algorithm based on high-precision map and multisensor fusion is proposed. The basic principle of the algorithm is to use the centimeter-level high-precision positioning combined with high-precision map data to complete the detection of lane markings. In the process of generating high-precision maps or in the uncovered areas of high-precision maps, LIDAR (LIght Detection And Ranging) is used to estimate the curvature of the road to assist in lane marking detection. The experimental results show that the algorithm has lower false detection rate in case of bad road conditions, and the algorithm is robust. © 2020 John Wiley & Sons, Ltd.

关键词： Roads and streets

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Accelerating Matrix Diagonalization through Decision Transformers with Epsilon-Greedy Optimization

arXiv

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arXiv 2024年

作者： Bhatta, Kshitij Zollicoffer, Geigh Bhattarai, Manish Romero, Phil Negre, Christian F.A. Niklasson, Anders M.N. Adedoyin, Adetokunbo Department of Mechanical and Aerospace Engineering University of Virginia CharlottesvilleVA22904 United States School of Mathematics Georgia Institute of Technology AtlantaGA30332 United States High Performance Computing Division Los Alamos National Laboratory NM87545 United States Theoretical Division Los Alamos National Laboratory NM87545 United States Computational Science Division Los Alamos National Laboratory NM87545 United States

This paper introduces a novel framework for matrix diagonalization, recasting it as a sequential decision-making problem and applying the power of Decision Transformers (DTs). Our approach determines optimal pivot selection during diagonalization with the Jacobi algorithm, leading to significant speedups compared to the traditional max-element Jacobi method. To bolster robustness, we integrate an epsilon-greedy strategy, enabling success in scenarios where deterministic approaches fail. This work demonstrates the effectiveness of DTs in complex computational tasks and highlights the potential of reimagining mathematical operations through a machine learning lens. Furthermore, we establish the generalizability of our method by using transfer learning to diagonalize matrices of smaller sizes than those trained. Copyright © 2024, The Authors. All rights reserved.

关键词： Reinforcement learning

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Sparsification and Optimization for Energy-Efficient Federated Learning in Wireless Edge Networks

Sparsification and Optimization for Energy-Efficient Federat...

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GLOBECOM 2022 - 2022 IEEE Global Communications Conference

作者： Lei Lei Yaxiong Yuan Yang Yang Yu Luo Lina Pu Symeon Chatzinotas School of Information and Communications Engineering Xi’ an Jiaotong University China Interdisciplinary Centre for Security Reliability and Trust (SnT) University of Luxembourg Luxembourg Competence Center for High Performance Computing Fraunhofer ITWM Germany Department of Electrical and Computer Engineering Mississippi State University USA Department of Computer Science University of Alabama USA

ISBN: (纸本)9781665435413

Federated Learning (FL), as an effective decentral-ized approach, has attracted considerable attention in privacy-preserving applications for wireless edge networks. In practice, edge devices are typically limited by energy, memory, and computation capabilities. In addition, the communications be-tween the central server and edge devices are with constrained resources, e.g., power or bandwidth. In this paper, we propose a joint sparsification and optimization scheme to reduce the energy consumption in local training and data transmission. On the one hand, we introduce sparsification, leading to a large number of zero weights in sparse neural networks, to alleviate devices' computational burden and mitigate the data volume to be uploaded. To handle the non-smoothness incurred by sparsification, we develop an enhanced stochastic gradient descent algorithm to improve the learning performance. On the other hand, we optimize power, bandwidth, and learning parameters to avoid communication congestion and enable an energy-efficient transmission between the central server and edge devices. By collaboratively deploying the above two components, the numerical results show that the overall energy consumption in FL can be significantly reduced, compared to benchmark FL with fully-connected neural networks.

关键词： Wireless communication Training Energy consumption Federated learning Neural networks Bandwidth Energy efficiency

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Anomalous strain effect on the thermal conductivity of low-buckled two-dimensional silicene

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National science Review 2021年第9期8卷 23-31页

作者： Bin Ding Xiaoyan Li Wuxing Zhou Gang Zhang Huajian Gao Institute of High Performance Computing A*STAR Centre for Advanced Mechanics and Materials Applied Mechanics Laboratory Department of Engineering Mechanics Tsinghua University School of Materials Science and Engineering & Hunan Provincial Key Laboratory of Advanced Materials for New Energy Storage and Conversion Hunan University of Science and Technology School of Mechanical and Aerospace Engineering College of Engineering Nanyang Technological University

The thermal conductivity of two-dimensional materials, such as graphene, typically decreases when tensile strain is applied, which softens their phonon modes. Here, we report an anomalous strain effect on the thermal conductivity of monolayer silicene, a representative low-buckled two-dimensional (LB-2D)material. Reax FF-based molecular dynamics simulations are performed to show that biaxially stretched monolayer silicene exhibits a remarkable increase in thermal conductivity, by as much as 10 times the freestanding value, with increasing applied strain in the range of [0, 0.1], which is attributed to increased contributions from long-wavelength phonons. A further increase in strain in the range of [0.11, 0.18] results in a plateau of the thermal conductivity in an oscillatory manner, governed by a unique dynamic bonding behavior under extreme loading. This anomalous effect reveals new physical insights into the thermal properties of LB-2D materials and may provide some guidelines for designing heat management and energy conversion devices based on such materials.

关键词： low-buckled two-dimensional materials strain effect thermal conductivity bonding dynamics

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从四方到立方:高性能太阳能电池的钙钛矿相结构演变

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science Bulletin 2023年第2期68卷 141-145,M0003页

作者：魏旗梁昊 Yuki Haruta Makhsud Saidaminov 米启兮 Michael Saliba 崔光磊宁志军 School of Physical Science and Technology ShanghaiTech UniversityShanghai 201210China High Performance Computing Department National Supercomputing Center in ShenzhenShenzhen 200441China Department of Chemistry and Department of Electrical&Computer Engineering and Centre for Advanced Materials and Related Technologies(CAMTEC) University of VictoriaVictoria V8P 5C2Canada Institute for Photovoltaics University of StuttgartStuttgart 70569Germany Qingdao Industrial Energy Storage Research Institute Qingdao Institute of Bioenergy and Bioprocess TechnologyChinese Academy of SciencesQingdao 266101China

Crystal structure, which is generally determined by the composition, ionic radius and valence state of constituent elements, plays a significant role in the material’s fundamental properties such as optical, electronic, and mechanical characters. The manipulation of the crystal structure through compositional engineering, and understanding the relationship between crystal structure and properties are essential for the development of materials.

关键词：太阳能电池结构演变钙钛矿相

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A Novel Human-Based Meta-Heuristic Algorithm: Dragon Boat Optimization

arXiv

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arXiv 2023年

作者： Li, Xiang Lan, Long Lahza, Husam Yang, Shaowu Wang, Shuihua Yang, Wenjing Liu, Hengzhu Zhang, Yudong College of Computer Science and Technology National University of Defense Technology Changsha410073 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 Department of Information Technology Faculty of Computing and Information Technology King Abdulaziz University Jeddah21589 Saudi Arabia Department of Biological Sciences Xi’an Jiaotong-Liverpool University Jiangsu Suzhou215123 China School of Computing and Mathematical Sciences University of Leicester LeicesterLE1 7RH United Kingdom School of Computer Science and Engineering Southeast University Jiangsu Nanjing210096 China Department of Mathematical Sciences University of Liverpool LiverpoolL69 3BX United Kingdom

(Aim) Dragon Boat Racing, a popular aquatic folklore team sport, is traditionally held during the Dragon Boat Festival. Inspired by this event, we propose a novel human-based meta-heuristic algorithm called dragon boat optimization (DBO) in this paper. (Method) It models the unique behaviors of each crew member on the dragon boat during the race by introducing social psychology mechanisms (social loafing, social incentive). Throughout this process, the focus is on the interaction and collaboration among the crew members, as well as their decision-making in different situations. During each iteration, DBO implements different state updating strategies. By modelling the crew's behavior and adjusting the state updating strategies, DBO is able to maintain high-performance efficiency. (Results) We have tested the DBO algorithm with 29 mathematical optimization problems and 2 structural design problems. (Conclusion) The experimental results demonstrate that DBO is competitive with state-of-the-art meta-heuristic algorithms as well as conventional methods. © 2023, CC BY.

关键词： Structural optimization

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Large-Scale Direct Numerical Simulations of Turbulence Using GPUs and Modern Fortran

arXiv

引用

arXiv 2022年

作者： Karp, Martin Massaro, Daniele Jansson, Niclas Hart, Alistair Wahlgren, Jacob Schlatter, Philipp Markidis, Stefano Department of Computer Science KTH Royal Institute of Technology Stockholm Sweden Engineering Mechanics KTH Royal Institute of Technology Stockholm Sweden PDC Centre for High Performance Computing KTH Royal Institute of Technology Stockholm Sweden United Kingdom

We present our approach to making direct numerical simulations of turbulence with applications in sustainable shipping. We use modern Fortran and the spectral element method to leverage and scale on supercomputers powered by the Nvidia A100 and the recent AMD Instinct MI250X GPUs, while still providing support for user software developed in Fortran. We demonstrate the efficiency of our approach by performing the world’s first direct numerical simulation of the flow around a Flettner rotor at Re=30’000 and its interaction with a turbulent boundary layer. We present one of the first performance comparisons between the AMD Instinct MI250X and Nvidia A100 GPUs for scalable computational fluid dynamics. Our results show that one MI250X offers performance on par with two A100 GPUs and has a similar power efficiency. Copyright © 2022, The Authors. All rights reserved.

关键词： Numerical models

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A review of uncertainty estimation and its application in medical imaging

Meta-Radiology

引用

Meta-Radiology 2023年第1期1卷

作者： Zou, Ke Chen, Zhihao Yuan, Xuedong Shen, Xiaojing Wang, Meng Fu, Huazhu National Key Laboratory of Fundamental Science on Synthetic Vision Sichuan University Chengdu 610065 China College of Intelligence and Computing Tianjin University Tianjin 300350 China Department of Mathematics Sichuan University Chengdu 610065 China Institute of High Performance Computing (IHPC) Agency for Science Technology and Research (A∗STAR) Singapore 138632 Singapore The College of Computer Science Sichuan University Chengdu 610065 China

The use of AI systems in healthcare for the early screening of diseases is of great clinical importance. Deep learning has shown great promise in medical imaging, but the reliability and trustworthiness of AI systems limit their deployment in real clinical scenes, where patient safety is at stake. Uncertainty estimation plays a pivotal role in producing a confidence evaluation along with the prediction of the deep model. This is particularly important in medical imaging, where the uncertainty in the model's predictions can be used to identify areas of concern or to provide additional information to the clinician. In this paper, we review the various types of uncertainty in deep learning, including aleatoric uncertainty and epistemic uncertainty. We further discuss how they can be estimated in medical imaging. More importantly, we review recent advances in deep learning models that incorporate uncertainty estimation in medical imaging. Finally, we discuss the challenges and future directions in uncertainty estimation in deep learning for medical imaging. We hope this review will ignite further interest in the community and provide researchers with an up-to-date reference regarding applications of uncertainty estimation models in medical imaging. © 2023 The Authors

关键词： Deep learning Medical image analysis Uncertainty estimation

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