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Image parallel Processing by Using MapReduce

Image Parallel Processing by Using MapReduce

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Information Science, parallel and distributed systems (ISPDS), international Conference on

作者： Lei Chen Xuebo Zhang Liting Sun School of Spacing Information Spacing Engineering University Beijing China

ISBN: (纸本)9781665417396

In recent years, big data processing platform Hadoop and parallel computing model MapReduce have achieved good results in mass data processing. However, since Hadoop does not design the input data streams for image files and MapReduce does not define an interface to match the data types of image files, the technology that running MapReduce program on Hadoop cluster to process massive image files in parallel is not mature. In addition, as image characteristic data (such as pixel intensive) is often skewness, image parallel processing on heterogeneous Hadoop cluster triggers a large data transfer and load imbalance among nodes, so that reducing the efficiency of MapReduce job. To tackle aforementioned problems, this paper carried out the following work, (1) The Hadoop platform is optimized to efficiently process the massive image; (2) The MapReduce model is extended and the interface is designed to support parallel image processing. (3) A new partition, namely PIHA, is proposed to mitigate data skew in heterogeneous Hadoop 3.2.0. Experiments show that the method proposed in this paper can effectively improve the efficiency of parallel image processing using MapReduce on Hadoop.

关键词： Information science Image processing Computational modeling distributed databases Telecommunication traffic parallel processing Streaming media

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parallel implementation of nearest feature line and rectified nearest feature line segment classifiers using openmp 17th

Parallel implementation of nearest feature line and rectifie...

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17th international symposium on distributed Computing and Artificial Intelligence, DCAI 2020

作者： Uribe-Hurtado, Ana-Lorena Villegas-Jaramillo, Eduardo-José Orozco-Alzate, Mauricio Departamento de Informática y Computación Universidad Nacional de Colombia - Sede Manizales Kilómetro 7 vía al Magdalena Manizales170003 Colombia

ISBN: (纸本)9783030530358

parallelizing computationally expensive classification algorithms, such as the Rectified Nearest Feature Line Segment (RNFLS), remains a task for expert programmers due to the complexity involved in rewriting the application and the required knowledge of the available hardware and tools. A simple parallel implementation of the Nearest Feature Line (NFL) and the RNFLS algorithms using OpenMP over multicore architectures is presented. Both non-parametric classifiers are used in the classification of datasets that contain few samples. The training and testing evaluation technique is used, with a 70–30 ratio respectively and seven datasets from the UCI repository, to verify the speedup and the accuracy of the classifier. Results and experiments derived from the parallel execution of both algorithms, calculating the average of 20 repetitions on a small architecture of 6 physical cores and 12 ones with multi-threading, show that accelerations of up to 21 times can be achieved with NFL and up to 13 times with RNFLS. © The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG 2021.

关键词： Classification (of information)

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Compositional Model Checking of Consensus Protocols via Interaction-Preserving Abstraction

Compositional Model Checking of Consensus Protocols via Inte...

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Reliable distributed systems

作者： Xiaosong Gu Wei Cao Yicong Zhu Xuan Song Yu Huang Xiaoxing Ma State Key Laboratory for Novel Software Technology Nanjing University Nanjing China Alibaba Group Hangzhou China

ISBN: (数字)9781665497534

ISBN: (纸本)9781665497541

Consensus protocols are widely used in building reliable distributed software systems and their correctness is of vital importance. TLA+ is a lightweight formal specification language which enables precise specification of system design and exhaustive checking of the design without any human effort. The features of TLA+ make it widely used in the specification and model checking of consensus protocols, both in academia and in industry. However, the application of TLA+ is limited by the state explosion problem in model checking. Though compositional model checking is essential to tame the state explosion problem, existing compositional checking techniques do not sufficiently consider the characteristics of TLA+. In this work, we propose the Interaction-Preserving Abstraction (IPA) framework, which leverages the features of TLA+ and enables practical and efficient compositional model checking of consensus protocols specified in TLA+. In the IPA framework, system specification is partitioned into multiple modules, and each module is divided into the internal part and the interaction part. The basic idea of the interaction-preserving abstraction is to omit the internal part of each module, such that another module cannot distinguish whether it is interacting with the original module or the coarsened abstract one. We apply the IPA framework to the compositional checking of the TLA+ specifications of two consensus protocols Raft and parallelRaft. Raft is a consensus protocol which was originally developed in academia and then widely used in industry. parallelRaft is the replication protocol in PolarFS, the distributed file system for the commercial database Alibaba PolarDB. We demonstrate that the IPA framework is easy to use in realistic scenarios and at the same time significantly reduces the model checking cost.

关键词： Industries File systems distributed databases Model checking Software systems Explosions Consensus protocol

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Hybrid Parameter Update: Alleviating Imbalance Impacts for distributed Deep Learning

Hybrid Parameter Update: Alleviating Imbalance Impacts for D...

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IEEE international Conference on High Performance Computing and Communications (HPCC)

作者： Hongliang Li Dong Xu Zhewen Xu Xiang Li College of Computer Science and Technology Jilin University Changchun China Key Laboratory of Symbolic Computation and Knowledge Engineering of the Ministry of Education Changchun China

Nowadays, data parallelism has been widely applied to train large datasets on distributed deep learning clusters, but it has suffered from costly global parameter updates at batch barriers. Performance imbalance among worker instances, introduced by uneven workload partitioning or biased resource allocation, can cause straggly workers, which can lead to severe impacts on both training speed and result accuracy. This paper studies the issue focusing on the tradeoff between training speed and result accuracy. We propose Cooperate Grouping parallel (CGP), a hybrid parameter update solution that allows the flexibility of both synchronous and asynchronous update schemes. We introduce a novel Cooperate Worker Grouping Problem (CWGP) that seeks a task grouping configuration that leads to maximum model accuracy and holds customized training speed guarantees. We propose an evolution-based Pareto local searching algorithm to compute efficient worker grouping configurations. Comprehensive evaluation results are presented to demonstrate the effectiveness of CGP under both persistent and fluctuant imbalances. The proposed method alleviates the imbalance impacts without introducing extra adjustment over-heads.

关键词： Training Deep learning Computational modeling Focusing distributed databases parallel processing Resource management

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SparkLeBLAST: Scalable parallelization of BLAST Sequence Alignment Using Spark 20

SparkLeBLAST: Scalable Parallelization of BLAST Sequence Ali...

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20th IEEE/ACM international symposium on Cluster, Cloud and Internet Computing (CCGrid)

作者： Youssef, Karim Feng, Wu-chun Virginia Tech Dept Comp Sci Blacksburg VA 24061 USA

ISBN: (纸本)9781728160955

The exponential growth of genomic data presents challenges in analyzing and computing on such biological data at scale. While NCBI's BLAST is a widely used pairwise sequence alignment tool, it does not scale to large datasets that are hundreds of gigabytes (GB) in size. To address this scalability problem, mpiBLAST emerged and became widely used, enabling scaling to 65,536 processes. However, mpiBLAST suffers from being tightly coupled with a specific implementation of BLAST, rendering it difficult to upgrade with the ever-evolving NCBI BLAST code. To address this shortcoming, recent parallel BLAST tools, such as SparkBLAST, consist of wrappers that are decoupled from the BLAST code but suffer from poor scalability with large sequence databases. Thus, there does not exist any parallel BLAST tool that can simultaneously address the issues of performance, scalability, programmability, and upgradability. To address this void, we propose SparkLeBLAST, a parallel BLAST tool that leverages our performance modeling and the Spark framework to deliver the performance and scalability of mpiBLAST and the ease of programming and upgradability of SparkBLAST, respectively. Ultimately, SparkLeBLAST delivers a 10x speedup relative to the state-of-the-art SparkBLAST and nearly a 2x speedup relative to the latest version of mpiBLAST.

关键词： scalable genome analysis BLAST Spark distributed computing parallel computing bioinformatics sequence alignment mpiBLAST SparkBLAST

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distributed Application Of The Four-Switch Buck-Boost Converter To Maximize Power Extraction In Photovoltaic Modules

Distributed Application Of The Four-Switch Buck-Boost Conver...

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IEEE international symposium on Power Electronics for distributed Generation systems (PEDG)

作者： Luís R. Côrtes Aniel S. de Morais Daniel P. Carvalho Fernando L. Tofoli Laura Ribeiro Adjeferson C. Gomes Federal University of Uberlândia Uberlândia Brazil Federal University of São João Del-Rei São João Del-Rei Brazil Santa Cruz State University Ilhéus Brazil

This work presents the application of a distributed control approach to maximize the power extraction from photovoltaic (PV) modules based on a dc-dc four-switch synchronous buck-boost converter (FSBBC). The outputs of several converters are connected to series and parallel arrays, thus allowing obtaining maximum power point tracking (MPPT) in an individual manner, regardless of the temperature and/or irradiance conditions.

关键词： Maximum power point trackers Photovoltaic systems Fuzzy logic Decentralized control Power electronics distributed power generation Voltage control

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A Lattice Linear Predicate parallel Algorithm for the Housing Market Problem 1

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23rd international symposium on Stabilization, Safety, and Security of distributed systems, SSS 2021

作者： Garg, Vijay K. Department of Electrical and Computer Engineering University of Texas at Austin AustinTX78712 United States

ISBN: (数字)9783030910815

ISBN: (纸本)9783030910808

It has been shown that Lattice Linear Predicate (LLP) algorithm solves many combinatorial optimization problems such as the shortest path problem, the stable marriage problem and the market clearing price problem. In this paper, we give an LLP algorithm for the Housing Market problem. The Housing Market problem is a one-sided matching problem with n agents and n houses. Each agent has an initial allocation of a house and a totally ordered preference list of houses. The goal is to find a matching between agents and houses such that no strict subset of agents can improve their outcome by exchanging houses with each other rather than going with the matching. Gale’s celebrated Top Trading Cycle algorithm to find the matching requires O(n2) time. Our parallel algorithm has expected time complexity O(nlog 2n) with and expected work complexity of O(n2log n). © 2021, Springer Nature Switzerland AG.

关键词： Houses

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r3d3: Optimized Query Compilation on GPUs

r3d3: Optimized Query Compilation on GPUs

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international symposium on Code Generation and Optimization (CGO)

作者： Alexander Krolik Clark Verbrugge Laurie Hendren McGill University Montreal Canada

The following topics are dealt with: program compilers; multiprocessing systems; parallel processing; graphics processing units; optimisation; optimising compilers; microprocessor chips; program diagnostics; storage m... 详细信息

ISBN: (数字)9781728186139

ISBN: (纸本)9781728186146

关键词： Runtime databases Query processing Memory management Graphics processing units Data transfer Optimization

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HCW 2022 Keynote Speaker: Heterogeneous Computing for Scientific Machine Learning

HCW 2022 Keynote Speaker: Heterogeneous Computing for Scient...

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IEEE international symposium on parallel and distributed Processing Workshops and Phd Forum (IPDPSW)

作者： Laurent White Advanced Micro Devices Inc.

ISBN: (数字)9781665497473

ISBN: (纸本)9781665497480

More than ever, the semiconductor industry is asked to answer society's call for more computing capacity and capability, which are driven by rapid digitalization, the widespread adoption of artificial intelligence, and the ever-increasing need for high-fidelity scientific simulations. While facing high demand, the supply of computing capability is being technically challenged by the slowdown of Moore's law and the need for high energy efficiency. This tug-of-war has now pushed the industry towards domain-specific accelerators, perhaps likely past the point of no return. The mix of general-purpose CPUs and high-end GPGPUs, which has pervaded data centers over the past few years, is likely to be expanded to a much richer set of application-specific accelerators, including AI engines, reconfigurable hardware, and even perhaps quantum, annealing, and neuromorphic devices. While acceleration and better efficiency may be enabled by using domain-specific accelerators for selected workloads, a much more holistic (i.e., system-wide) approach will have to be adopted to achieve significant performance gains for complex applications that consist of a variety of workloads where each could benefit from a specific accelerator. As an important example, scientific computing, which increasingly incorporates AI training and inference kernels in a tightly-integrated fashion, provides a rich and exciting laboratory for addressing the challenges of efficiently using highly-heterogeneous systems and for ultimately realizing their promises. Those challenges include co-designing the application, which requires domain experts to collaborate with other experts across the stack for workload mapping and data orchestration, and also adopting a decentralized strategy that embeds processing units where the data need them. Finally, the early experience of those co-design efforts should help the industry devise a longer-term strategy for developing programming models that would relieve application e

关键词： Industries distributed processing Conferences Training Scientific computing Quantum annealing Programming

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Research Progress of Intelligent Monitoring and Control in Aquaponics

Research Progress of Intelligent Monitoring and Control in A...

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Information Science, parallel and distributed systems (ISPDS), international Conference on

作者： Zhixin Ke Qian Zhou College of Information and Electrical Engineering China Agricultural University Beijing China

ISBN: (纸本)9781665417396

Aquaponics is essentially a compound agricultural farming model that combines aquaculture and hydroponics, which is used to improve resource utilization and promote sustainable development. It can be used to solve food and environmental problems in the world. The environment of aquaponics is sensitive. As aquatic organisms and plants are cultivated at the same time, aquaponics has stricter requirements on the environment. Internet of Things (IoT) technology has made many successful applications in modern agriculture, especially in realizing intelligent environmental monitoring and control, which is important for the further development of aquaponics. In recent years, many studies have focused on using IoT technology to guide the construction of aquaponic system. This paper describes the composition of aquaponic system in detail. The complexity of this system needs intelligent means to ensure its normal operation by enumerating and explaining the important parameters of aquaponics, as well as the intelligent monitoring and control strategies of aquaponic water environment in literatures. The key information is summarized to support the further promotion of automatic aquaponics solutions.

关键词： Microprocessors distributed databases Process control Sensor systems and applications Real-time systems Organisms Internet of Things

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