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Efficient in-place update with grouped and pipelined data transmission in erasure-coded storage systems

FUTURE GENERATION COMPUTER SYSTEMS-THE INTERNATIONAL JOURNAL OF ESCIENCE 2017年 69卷 24-40页

作者： Pei, Xiaoqiang Wang, Yijie Ma, Xingkong Xu, Fangliang Natl Univ Def Technol Natl Key Lab Parallel & Distributed Proc Coll Comp Changsha 410073 Hunan Peoples R China

distributed storage systems usually adopt erasure coding to achieve better tradeoff between the space efficiency and the data reliability. In-place updates are often used to overwrite the existing data rather than append the new data so as to ensure the data access efficiency. However, existing in-place update approaches either introduce significant I/O overhead or cause low update efficiency in erasure-coded storage systems due to the consistent update of parity blocks. In this paper, we propose a grouped and pipelined update scheme based on erasure codes, called Group-U, which comprises four key design features. (1) It groups the data nodes to complete the data transmission and dynamically adjusts the group size according to the update workload. (2) It pipelines the data transmission and distributes the update computation to all the participating nodes to improve the update efficiency. (3) It adopts the in-time update for data nodes and lazy-update for parity nodes to further reduce the update overhead. (4) It adjusts the occasion triggering the update to be compatible with the node failure. We design and implement Group-U on our Raid distributed Storage System (RDFS) and conduct testbed experiments on different update schemes under various parameter settings. The analysis and experimental results show that Group-U consumes 22% increase of update overhead compared with PUM and achieves 46% reduction of update overhead compared with PDP-P and PUS. Furthermore, Group-U achieves 69%, 34% and 21% reduction of update time on average compared with PUM, PDP-P and PUS respectively. (C) 2016 Elsevier B.V. All rights reserved.

关键词： In-place Update Grouping In-time Lazy-update

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MoNuSAC2020: A Multi-Organ Nuclei Segmentation and Classification Challenge

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IEEE TRANSACTIONS ON MEDICAL IMAGING 2021年第12期40卷 3413-3423页

作者： Verma, Ruchika Kumar, Neeraj Patil, Abhijeet Kurian, Nikhil Cherian Rane, Swapnil Graham, Simon Quoc Dang Vu Zwager, Mieke Raza, Shan E. Ahmed Rajpoot, Nasir Wu, Xiyi Chen, Huai Huang, Yijie Wang, Lisheng Jung, Hyun Brown, G. Thomas Liu, Yanling Liu, Shuolin Jahromi, Seyed Alireza Fatemi Khani, Ali Asghar Montahaei, Ehsan Baghshah, Mahdieh Soleymani Behroozi, Hamid Semkin, Pavel Rassadin, Alexandr Dutande, Prasad Lodaya, Romil Baid, Ujjwal Baheti, Bhakti Talbar, Sanjay Mahbod, Amirreza Ecker, Rupert Ellinger, Isabella Luo, Zhipeng Dong, Bin Xu, Zhengyu Yao, Yuehan Lv, Shuai Feng, Ming Xu, Kele Zunair, Hasib Ben Hamza, Abdessamad Smiley, Steven Yin, Tang-Kai Fang, Qi-Rui Srivastava, Shikhar Mahapatra, Dwarikanath Trnavska, Lubomira Zhang, Hanyun Narayanan, Priya Lakshmi Law, Justin Yuan, Yinyin Tejomay, Abhiroop Mitkari, Aditya Koka, Dinesh Ramachandra, Vikas Kini, Lata Sethi, Amit Case Western Reserve Univ Dept Biomed Engn Cleveland OH 44106 USA Univ Alberta Dept Comp Sci Edmonton AB T6G 2R3 Canada Alberta Machine Intelligence Inst Edmonton AB T5J 3B1 Canada Indian Inst Technol Dept Elect Engn Mumbai 400076 Maharashtra India HBNI Tata Mem Ctr ACTREC Dept Pathol Mumbai 400012 Maharashtra India Univ Warwick Dept Comp Sci Coventry CV4 7AL W Midlands England Univ Groningen Dept Pathol NL-9712 Groningen Netherlands Shanghai Jiao Tong Univ Inst Image Proc & Pattern Recognit Dept Automat Shanghai 200240 Peoples R China NCI Adv Biomed Comp Sci Frederick Natl Lab Canc Res Frederick MD 21702 USA Anhui Univ Dept Elect Engn & Automat Hefei 230039 Peoples R China Sharif Univ Technol Dept Comp Engn Tehran 113658639 Iran Sharif Univ Technol Dept Elect Engn Tehran 113658639 Iran Xperience AI Nizhnii Novgorod 603155 Russia SGGS Inst Engn & Technol Ctr Excellence Signal & Image Proc Nanded 431606 India Med Univ Vienna Inst Pathophysiol & Allergy Res A-1090 Vienna Austria TissueGnostics GmbH Dept Res & Dev Vienna Austria DeepBlue Technol Shanghai Co Ltd Shanghai 200336 Peoples R China Guangdong Univ Technol Sch Automat Guangzhou 510006 Peoples R China Tongji Univ Sch Elect & Informat Engn Shanghai 200092 Peoples R China Natl Univ Def Technol Natl Key Lab Parallel & Distributed Proc Changsha 410073 Peoples R China Concordia Univ Concordia Inst Informat Syst Engn Montreal PQ H4B 1R6 Canada Jensen Hughes Site Risk Management Engineer Liverpool NY 13212 USA Natl Univ Kaohsiung Dept Comp Sci & Informat Engn Kaohsiung 811 Taiwan Incept Inst Artificial Intelligence Abu Dhabi U Arab Emirates Slovak Univ Technol Bratislava Fac Informat & Informat Technol Bratislava 81107 Slovakia Inst Canc Res Ctr Evolut & Canc London SW7 3RP England Inst Canc Res Div Mol Pathol London SW7 3RP England Onward Hlth Hyderabad 500032 India

Detecting various types of cells in and around the tumor matrix holds a special significance in characterizing the tumor micro-environment for cancer prognostication and research. Automating the tasks of detecting, segmenting, and classifying nuclei can free up the pathologists' time for higher value tasks and reduce errors due to fatigue and subjectivity. To encourage the computer vision research community to develop and test algorithms for these tasks, we prepared a large and diverse dataset of nucleus boundary annotations and class labels. The dataset has over 46,000 nuclei from 37 hospitals, 71 patients, four organs, and four nucleus types. We also organized a challenge around this dataset as a satellite event at the International Symposium on Biomedical Imaging (ISBI) in April 2020. The challenge saw a wide participation from across the world, and the top methods were able to match inter-human concordance for the challenge metric. In this paper, we summarize the dataset and the key findings of the challenge, including the commonalities and differences between the methods developed by various participants. We have released the MoNuSAC2020 dataset to the public.

关键词： Annotations Image segmentation Tumors Computer architecture Training Task analysis Semantics Multi-organ dataset nucleus classification computational pathology instance segmentation panoptic quality

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JointCloud: A Cross-Cloud Cooperation Architecture for Integrated Internet Service Customization 37

JointCloud: A Cross-Cloud Cooperation Architecture for Integ...

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37th IEEE International Conference on distributed Computing Systems (ICDCS)

作者： Wang, Huaimin Shi, Peichang Zhang, Yiming Natl Univ Def Technol Natl Key Lab Parallel & Distributed Proc Changsha Hunan Peoples R China

ISBN: (纸本)9781538617915

Cloud computing has completely changed the economics of IT industry. Recently, the new form of "shared global economy" requires cloud services to be collaboratively provisioned by different cloud providers in a Geo-distributed manner, which brings severe challenges in service performance and cost. To address this problem, in this paper we propose JointCloud, a cross-cloud cooperation architecture for integrated Internet service customization. JointCloud borrows the idea from airline alliances and aims at empowering the cooperation among multiple clouds to provide efficient cross-cloud services. Joint Cloud focuses not only on the vertical integration of cloud resources but also on the horizontal cooperation among different cloud vendors. This paper describes the concept and architecture of JointCloud, as well as the initial design of the key components, namely, communication, storage, and computation.

关键词： cloud computing JointCloud shared economy service-oriented computing

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A Real-time Aware Routing Strategy in Smart City Environments

A Real-time Aware Routing Strategy in Smart City Environment...

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IEEE International Conference on Sensing, Communication, and Networking (SECON Workshops)

作者： Xia, YuSheng Su, JinShu Chen, RongMao Liu, YaPing Chen, Shuhui Natl Univ Def Technol Coll Comp Changsha Hunan Peoples R China Natl Univ Def Technol Natl Key Lab Parallel & Distributed Proc Changsha Hunan Peoples R China Cent S Univ Informat Secur & Big Data Res Inst Changsha Hunan Peoples R China

ISBN: (纸本)9781538652411

In future smart cities, the proliferation and mobility of smart devices will bring many challenges to efficient communication for the city-wide networking, including frequent link fluctuation and disconnection. GSTAR (Generalized storage-aware routing) has been a robust routing protocol that utilizes in-network storage to address these challenges. Unfortunately, the cache-and-forward mechanism adopted by GSTAR usually results in extra delay for storing real-time data, and thus makes it not applicable for some particular real-time applications, which crucially require low latency. Motivated by this problem, we propose a real-time aware strategy to reduce the total delay of time-sensitive data for real-time applications. Also, we specifically analyze the threshold to store and forward via mathematical modeling and access the rationality of associated parameters. Through NS3-based simulation, we show that our designed real-time aware strategy can decrease the average delay of time-sensitive data and prevent the block of time-insensitive data compared with the other strategies.

关键词： GSTAR Wireless Real-Time Modeling

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Cloud-Based Knowledge Sharing in Cooperative Robot Tracking of Multiple Targets with Deep Neural Network 24th

Cloud-Based Knowledge Sharing in Cooperative Robot Tracking ...

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24th International Conference on Neural Information processing (ICONIP)

作者： Bao, Hui Wang, HuaiMin Ding, Bo Shang, SuNing Natl Univ Def Technol Natl Key Lab Parallel & Distributed Proc Changsha 410073 Peoples R China

ISBN: (纸本)9783319701363;9783319701356

Cooperative robot tracking of multiple targets plays an important role in many realistic robot applications. In order to minimize the time during which any target is not tracked, target trading among robots at runtime is a common phenomenon. After a period of successful tracking, the robot can gain a lot of knowledge about the target details, for example, the appearance changes caused by motion and illumination. However, the accumulated knowledge is dropped simply in existing research while robots trading targets, which makes each robot has to learn the knowledge of target details from scratch. The absence of knowledge sharing heavily influences the tracking accuracy in practice. In this paper, we propose a novel approach named Cloudroid Tracking which enables knowledge sharing through the support of the back-end cloud infrastructure. Our approach adopts the deep neural network (DNN) and its online tuning mechanisms to enable the knowledge accumulation. The dynamic connection of multiple DNNs on the cloud infrastructure and multiple robots is enabled. No matter how the target changes, the robot can connect to the corresponding neural network which is responsible for a specific target. The experimental results on both open dataset and real robots show that our approach can promote the accuracy for robot tracking significantly.

关键词： Cooperative robot tracking of multiple targets Deep neural network Cloud robotic architecture

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Enabling Imagination: Generative Adversarial Network-Based Object Finding in Robotic Tasks 24th

Enabling Imagination: Generative Adversarial Network-Based O...

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24th International Conference on Neural Information processing (ICONIP)

作者： Che, Huimin Hu, Ben Ding, Bo Wang, Huaimin Natl Univ Def Technol Natl Key Lab Parallel & Distributed Proc Coll Comp Changsha Peoples R China

ISBN: (纸本)9783319701363;9783319701356

The skill to find objects in a real world situation is important for mobile robots. Existing works of robotic vision-based object finding is based on the traditional training and classification paradigm, which means that a robot can only detect objects with the fixed and pre-trained classification labels. It is of great challenge for robots to find an untrained object, even if a complex description of the object has been given. In this paper, we proposed a vision-based object detection approach for robotic finding names Generative Search. It is inspired by the object detection model that when an unfamiliar object needs to be found through a complex description, human would "imagine" the object in his or her brain and then find the object which is mostly like the imagined object profile. By adopting a Generative Adversarial Network (GAN), our approach enables the robot to generate the object virtually according to the given description. Then, we use pre-trained deep neural networks to match the generated image with images in the robotic vision. At the implementation level, we adopt the cloud robotic architecture to promote the algorithm efficiency. The experiments on both open datasets and real robotic scenarios have proved the significant promotion of object finding accuracy when a robot searching an unfamiliar object with a complex description.

关键词： Robotic object finding GAN Image matching

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Cloudroid: A Cloud Framework for Transparent and QoS-aware Robotic Computation Outsourcing 10

Cloudroid: A Cloud Framework for Transparent and QoS-aware R...

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10th IEEE International Conference on Cloud Computing (CLOUD)

作者： Hu, Ben Wang, Huaimin Zhang, Pengfei Ding, Bo Che, Huimin Natl Univ Def Technol Natl Key Lab Parallel & Distributed Proc Changsha 410073 Hunan Peoples R China

ISBN: (纸本)9781538619933

Many robotic tasks require heavy computation, which can easily exceed the robot's onboard computer capability. A promising solution to address this challenge is outsourcing the computation to the cloud. However, exploiting the potential of cloud resources in robotic software is difficult, because it involves complex code modification and extensive (re) configuration procedures. Moreover, quality of service (QoS) such as timeliness, which is critical to robot's behavior, have to be considered. In this paper, we propose a transparent and QoS-aware software framework called Cloudroid for cloud robotic applications. This framework supports direct deployment of existing robotic software packages to the cloud, transparently transforming them into Internet-accessible cloud services. And with the automatically generated service stubs, robotic applications can outsource their computation to the cloud without any code modification. Furthermore, the robot and the cloud can cooperate to maintain the specific QoS property such as request response time, even in a highly dynamic and resource-competitive environment. We evaluated Cloudroid based on a group of typical robotic scenarios and a set of software packages widely adopted in real-world robot practices. Results show that robots capability can be enhanced significantly without code modification and specific QoS objectives can be guaranteed. In certain tasks, the "cloud + robot" setup shows improved performance in orders of magnitude compared with the robot native setup.

关键词： Cloud robotics Platform as a service Computation outsourcing Quality of service

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Optimizing OpenCL Implementation of Deep Convolutional Neural Network on FPGA 1

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14th IFIP WG 10.3 International Conference on Network and parallel Computing (NPC)

作者： Qiao, Yuran Shen, Junzhong Huang, Dafei Yang, Qianming Wen, Mei Zhang, Chunyuan Natl Univ Def Technol Coll Comp Natl Key Lab Parallel & Distributed Proc Changsha Hunan Peoples R China

ISBN: (数字)9783319682105

ISBN: (纸本)9783319682105;9783319682099

Nowadays, the rapid growth of data across the Internet has provided sufficient labeled data to train deep structured artificial neural networks. While deeper structured networks bring about significant precision gains in many applications, they also pose an urgent demand for higher computation capacity at the expense of power consumption. To this end, various FPGA based deep neural network accelerators are proposed for higher performance and lower energy consumption. However, as a dilemma, the development cycle of FPGA application is much longer than that of CPU and GPU. Although FPGA vendors such as Altera and Xilinx have released OpenCL framework to ease the programming, tuning the OpenCL codes for desirable performance on FPGAs is still challenging. In this paper, we look into the OpenCL implementation of Convolutional Neural Network (CNN) on FPGA. By analysing the execution manners of a CPU/GPU oriented verision on FPGA, we find out the causes of performance difference between FPGA and CPU/GPU and locate the performance bottlenecks. According to our analysis, we put forward a corresponding optimization method focusing on external memory transfers. We implement a prototype system on an Altera Stratix V A7 FPGA, which brings a considerable 4.76x speed up to the original version. To the best of our knowledge, this implementation outperforms most of the previous OpenCL implementations on FPGA by a large margin.

关键词： Field programmable gate arrays (FPGA)

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Applying Detection Proposals to Visual Tracking for Scale and Aspect Ratio Adaptability

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INTERNATIONAL JOURNAL OF COMPUTER VISION 2017年第3期122卷 524-541页

作者： Huang, Dafei Luo, Lei Chen, Zhaoyun Wen, Mei Zhang, Chunyuan Natl Univ Def Technol Coll Comp Changsha Hunan Peoples R China Natl Univ Def Technol Natl Key Lab Parallel & Distributed Proc Changsha Hunan Peoples R China

The newly proposed correlation filter based trackers can achieve appealing performance despite their great simplicity and superior speed. However, this kind of object trackers is not born with scale and aspect ratio adaptability, thus resulting in suboptimal tracking accuracy. To tackle this problem, this paper integrates the class-agnostic detection proposal method, which is widely adopted in object detection area, into a correlation filter tracker. In the tracker part, optimizations such as feature integration, robust model updating and proposal rejection are applied for efficient integration. As for proposal generation, through integrating and comparing four detection proposal generators along with two baseline methods, the quality of detection proposals is found to have considerable influence on tracking accuracy. Therefore, as the most promising proposal generator, EdgeBoxes is chosen and further enhanced with background suppression. Evaluations are mainly performed on a challenging 50-sequence dataset (OTB50) and its two subsets, 28 sequences with significant scale variation and 14 sequences with obvious aspect ratio change. Among the trackers equipped with different proposal generators, state-of-the-art trackers and existing correlation filter variants, our proposed tracker reports the highest accuracy while running efficiently at an average speed of 20.4 frames per second. Additionally, numerical performance analysis in per-sequence manner and experiment results on VOT2014 dataset are also presented to enable deeper insights into our approach.

关键词： Visual tracking Detection proposal Scale and aspect ratio adaptability Correlation filter

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MALMM: A multi-array architecture for large-scale matrix multiplication on FPGA

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IEICE ELECTRONICS EXPRESS 2018年第10期15卷 20180286-20180286页

作者： Huang, You Shen, Junzhong Qiao, Yuran Wen, Mei Zhang, Chunyuan Natl Univ Def Technol Coll Comp Changsha 410073 Hunan Peoples R China Natl Univ Def Technol Natl Key Lab Parallel & Distributed Proc Changsha 410073 Hunan Peoples R China

Large-scale floating-point matrix multiplication is widely used in many scientific and engineering applications. Most existing works focus on designing a linear array architecture for accelerating matrix multiplication on FPGAs. This paper towards the extension of this architecture by proposing a scalable and highly configurable multi-array architecture. In addition, we present a work-stealing scheme to ensure the equality in the workload partition among multiple linear arrays. Furthermore, an analytical model is developed to determine the optimal parameters for matrix multiplication acceleration. Experiments on real-life convolutional neural networks (CNNs) show that we can obtain the optimal extension of the linear array architecture.

关键词： matrix multiplication field-programmable gate arrays (FPGAs) work-stealing

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