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3rd IEEE international conference on Integrated Circuits, Technologies and applications, ICTA 2020

作者： Zhou, Hongtao Dou, Runjiang Yu, Shuangming Qi, Nan Liu, Jian Wu, Nanjian Liu, Liyuan State Key Laboratory of Superlattics and Microstructures Institute of Semiconductors Chinese Academy of Science Beijing China

ISBN: (纸本)9781728180328

Harris corner detection is a common operation in the field of image processing, but suffering from computation complexity in embedded system. The current method of corner detection is to process one pixel in one clock cycle. With the improvement of image acquisition method in modern society, higher processing speed requirements have been proposed for the implementation of corner detection. This paper presents a new pixel-level parallelism FPGA implementation for Harris corner detection, which double the processing speed or more with almost no decreasing in accuracy. It is implemented on FPGA in the high-speed camera, and the experimental results show that the implementation is suitable for real-time applications. © 2020 IEEE.

关键词： Field programmable gate arrays (FPGA)

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EQueue: Elastic Lock-Free FIFO Queue for Core-to-Core Communication on Multi-Core Processors

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IEEE ACCESS 2020年 8卷 98729-98741页

作者： Wang, Junchang Tian, Yangfeng Fu, Xiong Nanjing Univ Posts & Telecommun Sch Comp Sci Nanjing 210003 Peoples R China Jiangsu Key Lab Big Data Secur & Intelligent Proc Nanjing 210003 Peoples R China Beijing Univ Posts & Telecommun Sch Comp Sci Beijing 100876 Peoples R China

In recent years, the number of CPU cores in a multi-core processor keeps increasing. To leverage the increasing hardware resource, programmers need to develop parallelized software programs. One promising approach to parallelizing high-performance applications is pipeline parallelism, which divides a task into a serial of subtasks and then maps these subtasks to a group of CPU cores, making the communication scheme between the subtasks running on different cores a critical component for the parallelized programs. One widely-used implementation of the communication scheme is software-based, lock-free first-in-first-out queues that move data between different subtasks. The primary design goal of the prior lock-free queues was higher throughput, such that the technique of batching data was heavily used in their enqueue and dequeue operations. Unfortunately, a lock-free queue with batching heavily depends on the assumption that data arrive at a constant rate, and the queue is in an equilibrium state. Experimentally we found that the equilibrium state of a queue rarely happens in real, high-performance use cases (e.g., 10Gbps & x002B;network applications) because data arriving rate fluctuates sharply. As a result, existing queues suffer from performance degradation when used in real applications on multi-core processors. In this paper, we present EQueue, a lock-free queue to handle this robustness issue in existing queues. EQueue is lock-free, efficient, and robust. EQueue can adaptively (1) shrink its queue size when data arriving rate is low to keep its memory footprint small to utilize CPU cache better, and (2) enlarge its queue size to avoid overflow when data arriving rate is in burstiness. Experimental results show that when used in high-performance applications, EQueue can always perform an enqueue/dequeue operation in less than 50 CPU cycles, which outperforms FastForward and MCRingBuffer, two state-of-the-art queues, by factors 3 and 2, respectively.

关键词： parallel processing Task analysis Multicore processing Pipelines Robustness Throughput Licenses Lock-free queue pipeline parallelism multi-core processors

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Remote Controlling distributed parallel Computing System over the Cloud (RCDPCSC)

Remote Controlling Distributed Parallel Computing System ove...

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international conference on Engineering Technology and their applications (IICETA)

作者： Subhi R. M. Zeebaree Culture and Student Activities Center Duhok Polytechnic University

ISBN: (数字)9781728182315

ISBN: (纸本)9781728182322

parallel distributed processing approaches are relatively new. A distributed cloud is the application of cloud computing technologies to interconnect data and applications served from multiple geographic locations. A distributed information technology (IT) means that something is shared among multiple systems that may also be in the same or different locations. The amount of data and exhausted time to process them and regulate the expected results efficiently and in as lowest as possible time has been expanded enormous. Therefore, it is essential to prepare approaches to enhance parallel processing techniques via cloud computing.

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An improved K-means distributed clustering algorithm based on spark parallel computing framework 3

An improved K-means distributed clustering algorithm based o...

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2020 3rd international Symposium on Big Data and Applied Statistics, ISBDAS 2020

作者： Lu, Xin Lu, Huanghuang Yuan, Jiao Wang, Xun School of Software University of Electronic Science and Technology of China Chengdu Sichuan610054 China China Railway Eryuan Engineering Group Co.Ltd Chengdu Sichuan610031 China

Traditional K-means distributed clustering algorithm has many problems in clustering big data, such as unstable clustering results, poor clustering results and low execution efficiency. In this paper, a density based initial clustering center selection method is proposed to improve the K-means distributed clustering algorithm. The algorithm uses the sample density, the distance between clusters and the cluster compact density, defines the product of the three as the difference weight density, and finds the sample point with the maximum difference weight density as the initial cluster center, so as to solve the problem of randomness and low quality of initial cluster center selection. At the same time, this paper uses spark parallel computing framework to implement the improved algorithm to further improve the processing performance of the algorithm in big data *** experimental results show that the improved k-means distributed clustering algorithm based on spark parallel computing framework has higher execution efficiency, accuracy and good stability in big data clustering analysis. © Published under licence by IOP Publishing Ltd.

关键词： Big data

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Minimizing Self-adaptation Overhead in parallel Stream processing for Multi-cores 25th

Minimizing Self-adaptation Overhead in Parallel Stream Proce...

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25th international conference on parallel and distributed Computing (Euro-Par)

作者： Vogel, Adriano Griebler, Dalvan Danelutto, Marco Fernandes, Luiz Gustavo Pontificia Univ Catolica Rio Grande do Sul Sch Technol Porto Alegre RS Brazil Univ Pisa Dept Comp Sci Pisa Italy Tres de Maio Fac SETREM Lab Adv Res Cloud Comp LARCC Tres De Maio Brazil

ISBN: (纸本)9783030483401;9783030483395

Stream processing paradigm is present in several applications that apply computations over continuous data flowing in the form of streams (e.g., video feeds, image, and data analytics). Employing self-adaptivity to stream processing applications can provide higher-level programming abstractions and autonomic resource management. However, there are cases where the performance is suboptimal. In this paper, the goal is to optimize parallelism adaptations in terms of stability and accuracy, which can improve the performance of parallel stream processing applications. Therefore, we present a new optimized self-adaptive strategy that is experimentally evaluated. The proposed solution provided high-level programming abstractions, reduced the adaptation overhead, and achieved a competitive performance with the best static executions.

关键词： parallel programming Stream parallelism Self-adaptive parallelism Autonomic computing

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Data-Centric distributed Computing on Networks of Mobile Devices 26th

Data-Centric Distributed Computing on Networks of Mobile Dev...

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26th international conference on parallel and distributed Computing (Euro-Par)

作者： Sanches, Pedro Silva, Joao A. Teofilo, Antonio Paulino, Herve Univ NOVA Lisboa Fac Ciencias & Tecnol NOVA Lab Comp Sci & Informat Dept Informat Caparica Portugal Inst Politecn Lisboa ISEL ADEETC Lisbon Portugal

ISBN: (纸本)9783030576752;9783030576745

In the last few years, we have seen a significant increase both in the number and capabilities of mobile devices, as well as in the number of applications that need more and more computing and storage resources. Currently, in order to deal with this growing need for resources, applications make use of cloud services. This raises some problems, namely high latency, considerable use of energy and bandwidth, and the unavailability of connectivity infrastructures. Given this context, for some applications it makes sense to do part, or all, of the computations locally on the mobile devices themselves. In this paper we present OREGANO, a framework for distributed computing on mobile devices, capable of processing batches or streams of data generated on mobile device networks, without requiring centralized services. Contrary to current state-of-the-art, where computations and data are sent to worker mobile devices, OREGANO performs computations where the data is located, significantly reducing the amount of exchanged data.

关键词： Mobile devices distributed computing Edge computing

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A distributed Business-Aware Storage Execution Environment Towards Large-Scale applications 20th

A Distributed Business-Aware Storage Execution Environment T...

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20th international conference on Algorithms and Architectures for parallel processing (ICA3PP)

作者： Jiang, Feng Cheng, Yongyang Dong, Changkun Hui, Zhao Yan, Ruibo China Telecom Cloud Comp Corp Beijing Peoples R China Beijing Univ Posts & Telecommun State Key Lab Networking & Switching Technol Beijing Peoples R China

ISBN: (纸本)9783030602390;9783030602383

The rapid development of distributed storage technology has attracted growing attention from both industry and academia. Although distributed system has been studied for many years, however, it is widely applied to engineering practice since the rise of cloud computing recently. Cloud storage constructs a storage resource pool with massive common storage devices through networks, which could be allocated to authorized users on demand. In this paper, we present a distributed business-aware storage execution environment towards large-scale applications. Initially, we present the overall of our proposed storage execution environment and different data delivery models according to corresponding business needs. Furthermore, we design a globally shared and cross-regional deployed metadata service to minimize the access delay of metadata. Finally, the approach presented in this paper has been validated to be effective through a series of evaluation experiments and actual use cases.

关键词： Cloud computing Object-oriented storage distributed system Large scale applications High-performance architecture

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3rd Workshop on Education for High Performance Computing (EduHiPC - 2021)

3rd Workshop on Education for High Performance Computing (Ed...

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IEEE international conference on High Performance Computing Workshops (HiPCW)

作者： Sheikh Ghafoor Sushil K. Prasad

ISBN: (纸本)9781665410380

High Performance Computing (HPC) and, in general, parallel and distributed Computing (PDC) is ubiquitous. Every computing device, from a smartphone to a supercomputer, relies on parallel processing. Compute clusters of multicore and manycore processors (CPUs and GPUs) are routinely used in many subdomains of computer science, such as data science, parallel machine learning and high performance computing. Therefore, it is important for every computing professional (and especially every programmer) to understand how parallelism and distributed computing affect problem solving. It is essential for educators to impart a range of PDC and HPC skills and knowledge at multiple levels within the curriculum of Computer Science (CS), Computer Engineering (CE), and related disciplines such as computational data science. Software industry and research laboratories require people with these skills, more so now. Therefore, they now engage in extensive on-the-job training. Additionally, rapid changes in hardware platforms, languages, and programming environments increasingly challenge educators to decide what to teach and how to teach it, in order to prepare students for careers that are increasingly likely to involve PDC and HPC. EduHiPC aims to provide a forum that brings together academia, industry, government, and non-profit organizations - especially from India, its vicinity, and Asia - for exploring and exchanging experiences and ideas about the inclusion of high-performance, parallel, and distributed computing into undergraduate and graduate curriculum of Computer Science, Computer Engineering, Computational Science, Computational Engineering, and computational courses for STEM and business and other non-STEM disciplines.

关键词： High Performance Computing distributed computing PDC gene computer science Curriculum Nonprofit Organizations Education educators supercomputer Smartphone COMPUTER ENGINEERING Laboratory Research Multi-core processors HIGH-PERFORMANCE career

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Development and Operation of Elastic parallel Tree Search applications Using TASKWORK 1

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9th international conference on Cloud Computing and Services Science (CLOSER)

作者： Kehrer, Stefan Blochinger, Wolfgang Reutlingen Univ Parallel & Distributed Comp Grp Alteburgstr 150 D-72762 Reutlingen Germany

ISBN: (数字)9783030494322

ISBN: (纸本)9783030494315;9783030494322

Cloud resources can be dynamically provisioned according to application-specific requirements and are payed on a per-use basis. This gives rise to a new concept for parallel processing: Elastic parallel computations. However, it is still an open research question to which extent parallel applications can benefit from elastic scaling, which requires resource adaptation at runtime and corresponding coordination mechanisms. In this work, we analyze how to address these system-level challenges in the context of developing and operating elastic parallel tree search applications. Based on our findings, we discuss the design and implementation of TASKWORK, a cloud-aware runtime system specifically designed for elastic parallel tree search, which enables the implementation of elastic applications by means of higher-level development frameworks. We show how to implement an elastic parallel branch-and-bound application based on an exemplary development framework and report on our experimental evaluation that also considers several benchmarks for parallel tree search.

关键词： Cloud computing parallel computing Task parallelism Elasticity Branch-and-bound

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Control of Multiple PV Integrated parallel Inverters for Microgrid applications 3

Control of Multiple PV Integrated Parallel Inverters for Mic...

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3rd IEEE international conference on Power, Instrumentation, Control and Computing, PICC 2020

作者： Singh, Yashi Singh, Bhim Mishra, Sukumar Indian Institute of Technology Delhi Department of Electrical Eng. New Delhi110016 India

ISBN: (纸本)9781728175904

To enhance the accessibility and reliability for a distributed generation system (DGS), a grid-tied photovoltaic (PV) generation system based on multiple parallel connected PV-inverters is developed for microgrid application in this work. This microgrid is designed to operate in the grid tied mode (GTM) as well as a standalone mode (SAM) and vice-versa. Here, a grid-forming inverter (GFI) technology is utilized for system with high penetration of renewables. The single stage PV-battery interfaced GFI inverter in parallel with a PV array supported inverters is considered to operate in the GTM as well as in (SAM) of operation. In SAM, the grid forming inverter maintains constant voltage and frequency across the load The control strategies of each unit are independent and using multi-loop controllers with switching logic. In the GTM, each PV panel in DGS is interfaced with an inverter with independent maximum power point tracking (MPPT) to harvest maximum power. The battery supports the PV units during peak load periods and maintains the power balance in the microgrid. Controlled charging and discharging profile of the battery is achieved by a DC-DC bidirectional converter (DBC). The control strategy of microgrid is verified with simulated results in detail. © 2020 IEEE.

关键词： Solar panels

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