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Power Characterization of Memory Intensive Applications: Analysis and Implications 1st

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Power Characterization of Memory Intensive Applications: Ana...

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

作者： Qiu, Yeliang Jiang, Congfeng Fan, Tiantian Wang, Yumei Zhang, Liangbin Wan, Jian Shi, Weisong Key Laboratory of Complex Systems Modeling and Simulation Ministry of Education Hangzhou Dianzi University Hangzhou310037 China School of Computer Science and Technology Hangzhou Dianzi University Hangzhou310037 China College of Big Data and Software Engineering Zhejiang Wanli University Ningbo China School of Information and Electronic Engineering Zhejiang University of Science and Technology Hangzhou310023 China Department of Computer Science Wayne State University DetroitMI48202 United States

ISBN: (纸本)9783030328122

DRAM is a significant source of server power consumption especially when the server runs memory intensive applications. Current power aware scheduling assumes that DRAM is as energy proportional as other components. However, the non-energy proportionality of DRAM significantly affects the power and energy consumption of the whole server system when running memory intensive applications. Thus good knowledge of server power characterization under memory intensive workloads can help better workload placement with power reduction. In this paper, we investigate the power characteristics of memory intensive applications on real rack servers of different generations. Through comprehensive analysis we find that (1) Server power consumption changes with workload intensity and concurrent execution threads. However, fully utilized memory systems are not the most energy efficient. (2) Powered memory modules of installed memory capacity, i.e. the memory capacity per processor core has significant impact on the application’s performance and server power consumption even if the memory system is not fully utilized. (3) Memory utilization is not always a good indicator for server power consumption when it is running memory intensive applications. Our experiments show that hardware configuration, workload types, as well as concurrently running threads have significant impact on a server’s energy efficiency when running memory intensive applications. Our findings presented in this paper provide useful insights and guidance to system designers, as well as data center operators for energy efficiency aware job scheduling and power reductions. © 2019, Springer Nature Switzerland AG.

关键词： Energy efficiency

Recognition of patient groups with sleep related disorders using bio-signal processing and deep learning

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arXiv 2021年

作者： Jarchi, Delaram Andreu-Perez, Javier Kiani, Mehrin Vysata, Oldrich Kuchynka, Jiri Prochazka, Ales Sanei, Saeid Smart Health Technologies Group School of Computer Science and Electronic Engineering University of Essex ColchesterCO4 3SQ United Kingdom Embedded and Intelligent Systems Laboratory School of Computer Science and Electronics University of Essex ColchesterCO4 3SQ United Kingdom Department of Computing and Control Engineering University of Chemistry and Technology in Prague Prague 6166 28 Czech Republic Department of Neurology Faculty of Medicine in Hradec Králové Charles University Hradec Králové500 05 Czech Republic Czech Institute of Informatics Robotics and Cybernetics Czech Technical University in Prague Prague 6160 00 Czech Republic School of Science and Technology Nottingham Trent University NottinghamNG11 8NS United Kingdom

Accurately diagnosing sleep disorders is essential for clinical assessments and treatments. Polysomnography (PSG) has long been used for detection of various sleep disorders. In this research, electrocardiography (ECG) and electromayography (EMG) have been used for recognition of breathing and movement-related sleep disorders. Bio-signal processing has been performed by extracting EMG features exploiting entropy and statistical moments, in addition to developing an iterative pulse peak detection algorithm using synchrosqueezed wavelet transform (SSWT) for reliable extraction of heart rate and breathing-related features from ECG. A deep learning framework has been designed to incorporate EMG and ECG features. The framework has been used to classify four groups: healthy subjects, patients with obstructive sleep apnea (OSA), patients with restless leg syndrome (RLS) and patients with both OSA and RLS. The proposed deep learning framework produced a mean accuracy of 72% and weighted F1 score of 0.57 across subjects for our formulated four-class problem. © 2021, CC BY-NC-ND.

关键词： Wavelet transforms

Voronoi-based efficient surrogate-assisted evolutionary algorithm for very expensive problems

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arXiv 2019年

作者： Tong, Hao Huang, Changwu Liu, Jialin Yao, Xin Shenzhen Key Laboratory of Computational Intelligence University Key Laboratory of Evolving Intelligent Systems of Guangdong Province Department of Computer Science and Engineering Southern University of Science and Technology Shenzhen518055 China

Very expensive problems are very common in practical system that one fitness evaluation costs several hours or even days. Surrogate assisted evolutionary algorithms (SAEAs) have been widely used to solve this crucial problem in the past decades. However, most studied SAEAs focus on solving problems with a budget of at least ten times of the dimension of problems which is unacceptable in many very expensive real-world problems. In this paper, we employ Voronoi diagram to boost the performance of SAEAs and propose a novel framework named Voronoi-based efficient surrogate assisted evolutionary algorithm (VESAEA) for very expensive problems, in which the optimization budget, in terms of fitness evaluations, is only 5 times of the problems dimension. In the proposed framework, the Voronoi diagram divides the whole search space into several subspace and then the local search is operated in some potentially better subspace. Additionally, in order to trade off the exploration and exploitation, the framework involves a global search stage developed by combining leave-one-out cross-validation and radial basis function surrogate model. A performance selector is designed to switch the search dynamically and automatically between the global and local search stages. The empirical results on a variety of benchmark problems demonstrate that the proposed framework significantly outperforms several state-of-art algorithms with extremely limited fitness evaluations. Besides, the efficacy of Voronoi-diagram is furtherly analyzed, and the results show its potential to optimize very expensive problems. Copyright © 2019, The Authors. All rights reserved.

关键词： Graphic methods

A new hypervolume-based evolutionary algorithm for many-objective optimization

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TechRxiv

TechRxiv 2019年

作者： Shang, Ke Ishibuchi, Hisao Shenzhen Key Laboratory of Computational Intelligence University Key Laboratory of Evolving Intelligent Systems of Guangdong Province Department of Computer Science and Engineering Southern University of Science and Technology Shenzhen518055 China

In this paper, a new hypervolume-based evolutionary multi-objective optimization algorithm (EMOA), namely R2HCA-EMOA (R2-based Hypervolume Contribution Approximation EMOA), is proposed for many-objective optimization. The core idea of the algorithm is to use an R2 indicator variant to approximate the hypervolume contribution. The basic framework of the proposed algorithm is the same as SMS-EMOA. In order to make the algorithm computationally efficient, a utility tensor structure is introduced for the calculation of the R2 indicator variant. Moreover, a normalization mechanism is incorporated into R2HCA-EMOA to enhance the performance of the algorithm. Through experimental studies, R2HCA-EMOA is compared with three hypervolume-based EMOAs and several other state-of-the-art EMOAs on 5-, 10- and 15-objective DTLZ, WFG problems and their minus versions. Our results show that R2HCA-EMOA is more efficient than the other hypervolume-based EMOAs, and is superior to all the compared state-of-the-art EMOAs. © 2019, CC BY.

关键词： Evolutionary algorithms

A Simple Yet Effective Approach to Robust Optimization Over Time

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arXiv 2019年

作者： Adam, Lukáš Yao, Xin Shenzhen Key Laboratory of Computational Intelligence University Key Laboratory of Evolving Intelligent Systems of Guangdong Province Department of Computer Science and Engineering Southern University of Science and Technology Shenzhen518055 China

Robust optimization over time (ROOT) refers to an optimization problem where its performance is evaluated over a period of future time. Most of the existing algorithms use particle swarm optimization combined with another method which predicts future solutions to the optimization problem. We argue that this approach may perform subpar and suggest instead a method based on a random sampling of the search space. We prove its theoretical guarantees and show that it significantly outperforms the state-of-the-art methods for ROOT. Copyright © 2019, The Authors. All rights reserved.

关键词： Particle swarm optimization (PSO)

Levelling the playing field: A comprehensive comparison of visual place recognition approaches under changing conditions

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arXiv 2019年

作者： Zaffar, Mubariz Khaliq, Ahmad Ehsan, Shoaib Milford, Michael McDonald-Maier, Klaus Embedded and Intelligent Systems Laboratory in Computer Science and Electronic Engineering department University of Essex Colchester United Kingdom Australian Centre for Robotic Vision and School of Electrical Engineering and Computer Science Queensland University of Technology Brisbane Australia

In recent years there has been significant improvement in the capability of Visual Place Recognition (VPR) methods, building on the success of both hand-crafted and learnt visual features, temporal filtering and usage of semantic scene information. The wide range of approaches and the relatively recent growth in interest in the field has meant that a wide range of datasets and assessment methodologies have been proposed, often with a focus only on precision-recall type metrics, making comparison difficult. In this paper we present a comprehensive approach to evaluating the performance of 10 state-of-the-art recently-developed VPR techniques, which utilizes three standardized metrics: (a) Matching Performance b) Matching Time c) Memory Footprint. Together this analysis provides an up-to-date and widely encompassing snapshot of the various strengths and weaknesses of contemporary approaches to the VPR problem. The aim of this work is to help move this particular research field towards a more mature and unified approach to the problem, enabling better comparison and hence more progress to be made in future research. Copyright © 2019, The Authors. All rights reserved.

关键词： Semantics

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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Deep Learning in Digital Breast Tomosynthesis: Current Status, Challenges, and Future Trends

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MedComm

MedComm 2025年第6期6卷 e70247页

作者： Wang, Ruoyun Chen, Fanxuan Chen, Haoman Lin, Chenxing Shuai, Jincen Wu, Yutong Ma, Lixiang Hu, Xiaoqu Wu, Min Wang, Jin Zhao, Qi Shuai, Jianwei Pan, Jingye Oujiang Laboratory (Zhejiang Lab for Regenerative Medicine Vision and Brain Health) Wenzhou Institute University of Chinese Academy of Sciences Wenzhou China Wenzhou Medical University Wenzhou China UCSC Baskin School of Engineering University of California Santa Cruz CA United States Key Laboratory of Intelligent Treatment and Life Support for Critical Diseases of Zhejiang Province Wenzhou China Department of Anatomy Histology & Embryology School of Basic Medical Sciences Fudan University Shanghai China Department of Medicine Harvard Medical School and Brigham and Women's Hospital Boston MA United States Joint Research Centre on Medicine The Affiliated Xiangshan Hospital of Wenzhou Medical University Ningbo China Stony Brook University Stony Brook NY United States School of Computer Science and Software Engineering University of Science and Technology Liaoning Anshan China Department of Big Data in Health Science The First Affiliated Hospital of Wenzhou Medical University Wenzhou China Zhejiang Engineering Research Center for Hospital Emergency and Process Digitization Wenzhou China

The high-resolution three-dimensional (3D) images generated with digital breast tomosynthesis (DBT) in the screening of breast cancer offer new possibilities for early disease diagnosis. Early detection is especially important as the incidence of breast cancer increases. However, DBT also presents challenges in terms of poorer results for dense breasts, increased false positive rates, slightly higher radiation doses, and increased reading times. Deep learning (DL) has been shown to effectively increase the processing efficiency and diagnostic accuracy of DBT images. This article reviews the application and outlook of DL in DBT-based breast cancer screening. First, the fundamentals and challenges of DBT technology are introduced. The applications of DL in DBT are then grouped into three categories: diagnostic classification of breast diseases, lesion segmentation and detection, and medical image generation. Additionally, the current public databases for mammography are summarized in detail. Finally, this paper analyzes the main challenges in the application of DL techniques in DBT, such as the lack of public datasets and model training issues, and proposes possible directions for future research, including large language models, multisource domain transfer, and data augmentation, to encourage innovative applications of DL in medical imaging. © 2025 The Author(s). MedComm published by Sichuan International Medical Exchange & Promotion Association (SCIMEA) and John Wiley & Sons Australia, Ltd.

关键词： deep learning digital breast tomosynthesis early breast cancer screening medical image analysis public database

ANDROIDOFF:Offloading android application based on cost estimation

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Journal of systems and software 2019年 158卷 110418-000页

作者： Chen, Xing Chen, Jiaqing Liu, Bichun Ma, Yun Zhang, Ying Zhong, Hao College of Mathematics and Computer Science Fuzhou University Fuzhou350116 China Fujian Key Laboratory of Network Computing and Intelligent Information Processing Fuzhou350116 China School of Software Tsinghua University Beijing100084 China National Engineering Research Center of Software Engineering Peking University China Department of Computer Science and Engineering Shanghai Jiao Tong University Shanghai200240 China

Computation offloading is a promising way of improving the performance and reducing the battery power consumption, since it moves some time-consuming computation activities to nearby servers. Although various approaches have been proposed to support computation offloading, we argue that there is still sufficient space for improvements, since existing approaches cannot accurately estimate the execution costs. As a result, we find that their offloading plans are less optimized. To handle the problem, in this paper, given an Android application, we propose a novel approach, called ANDROIDOFF, that supports offloading at the granularity of objects. Supporting such capability is challenging due to the two reasons: (1) through dynamic execution, it is feasible to collect the execution costs of only partial methods, and (2) it is difficult to accurately estimate the execution costs of the remaining methods. To overcome the challenges, given an Android application, ANDROIDOFF first combines static and dynamic analysis to predict the execution costs of all its methods. After all the costs are estimated, ANDROIDOFF synthesizes an offloading plan, in which determines the offloading details. We evaluate ANDROIDOFF on a real-world application, with two mobile devices. Our results show that, compared with other approaches, ANDROIDOFF saves the response time by 8%–49% and reduces the energy consumption by 12%–49% on average for computation-intensive applications. © 2019 Elsevier Inc.

关键词： computation offloading