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共 2980 条记录，以下是411-420 订阅

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On the Detection of Silent Data Corruptions in HPC applications Using Redundant Multi-threading

On the Detection of Silent Data Corruptions in HPC Applicati...

引用

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

作者： Perez, Diego Ropars, Thomas Meneses, Esteban Costa Rica Inst Technol Sch Comp San Jose Costa Rica Univ Grenoble Alpes CNRS Grenoble INP Grenoble France Costa Rica Natl High Technol Ctr Adv Comp Lab San Jose Costa Rica

ISBN: (纸本)9783030715939;9783030715922

This paper studies the use of Redundant Multi-Threading (RMT) to detect Silent Data Corruptions in HPC applications. To understand if it can be a viable solution in an HPC context, we study two software optimizations to reduce RMT performance overhead by reducing the amount of data exchanged between the replicated threads. We conduct experiments with representative HPC workloads to measure the performance gains obtained through these optimizations, and the error detection coverage they achieve. In the best case, when running on a processor that features Simultaneous Multi-Threading, our results show that the overhead can be as low as 1.4x without significantly reducing the ability to detect data corruptions.

关键词： HPC Silent data corruptions Redundant multi-threading

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Demand-Driven Data Provisioning in Data Lakes 23

Demand-Driven Data Provisioning in Data Lakes

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23rd international conference on Information Integration and Web Intelligence, iiWAS 2021

作者： Stach, Christoph Bräcker, Julia Eichler, Rebecca Giebler, Corinna Mitschang, Bernhard Institute for Parallel and Distributed Systems University of Stuttgart Germany Institute of Biochemistry and Technical Biochemistry University of Stuttgart Germany

ISBN: (纸本)9781450395564

Data has never been as significant as it is today. It can be acquired virtually at will on any subject. Yet, this poses new challenges towards data management, especially in terms of storage (data is not consumed during processing, i. e., the data volume keeps growing), flexibility (new applications emerge), and operability (analysts are no IT experts). The goal has to be a demand-driven data provisioning, i. e., the right data must be available in the right form at the right time. Therefore, we introduce a tailorable data preparation zone for Data Lakes called BARENTS. It enables users to model in an ontology how to derive information from data and assign the information to use cases. The data is automatically processed based on this model and the refined data is made available to the appropriate use cases. Here, we focus on a resource-efficient data management strategy. BARENTS can be embedded seamlessly into established Big Data infrastructures, e. g., Data Lakes. © 2021 ACM.

关键词： Lakes

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Scalable and Flexible High-Performance In-Network processing of Hash Joins in distributed Databases 20

Scalable and Flexible High-Performance In-Network Processing...

引用

20th international conference on Field-Programmable Technology (ICFPT)

作者： Wirth, Johannes Hofmann, Jaco A. Thostrup, Lasse Binnig, Carsten Koch, Andreas Tech Univ Darmstadt Embedded Syst & Applicat Grp Darmstadt Germany Tech Univ Darmstadt Data Management Lab Darmstadt Germany

ISBN: (纸本)9781665420105

Programmable switches allow to offload specific processing tasks into the network and promise multi-Tbit /s throughput. One major goal when moving computation to the network is typically to reduce the volume of network traffic, and thus improve the overall performance. In this manner, programmable switches are increasingly used, both in research as well as in industry applications, for various scenarios, including statistics gathering, in-network consensus protocols, and more. However, the currently available programmable switches suffer from several practical limitations. One important restriction is the limited amount of available memory, making them unsuitable for stateful operations such as Hash Joins in distributed databases. In previous work, an FPGA-based In-Network Hash Join accelerator was presented, initially using DDR-DRAM to hold the state. In a later iteration, the hash table was moved to on-chip HBM-DRAM to improve the performance even further. However, while very fast, the size of the joins in this setup was limited by the relatively small amount of available HBM. In this work, we heterogeneously combine DDR-DRAM and HBM memories to support both larger joins and benefit from the far faster and more parallel HBM accesses. In this manner, we are able to improve the performance by a factor of 3x compared to the previous HBM-based work. We also introduce additional configuration parameters, supporting a more flexible adaptation of the underlying hardware architecture to the different join operations required by a concrete use-case.

关键词： HBM FPGA Hash Join INP In-Network processing

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Loupe: A Visualization Tool for High-Level Execution Plans in SystemDS 26th

Loupe: A Visualization Tool for High-Level Execution Plans i...

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26th international conference on Database Systems for Advanced applications (DASFAA)

作者： Xu, Zhizhen Chen, Zihao Xu, Chen East China Normal Univ Shanghai Engn Res Ctr Big Data Management Sch Data Sci & Engn Shanghai Peoples R China Sci & Technol Parallel & Distributed Proc Lab PDL Changsha Peoples R China

ISBN: (纸本)9783030731991;9783030732004

The declarative programming language in SystemDS simplifies users to implement machine learning algorithms. It is able to generate execution jobs on different data processing engines including MapReduce and Spark. The GUI in data processing engines typical visualizes the low-level execution process (e.g., rdD transformation in Spark). However, the low-level description in Spark GUI does not show the relationship between DML operations and rdD primitives. In this work, we propose Loupe, a tool to visualize high-level execution plans in SystemDS to ease users to understand the execution process. This paper introduces the design of the tool and demonstrates a visualization case.

关键词： SystemDS Execution plan Visualization

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Research on distributed parallel Eclat Optimization Algorithm 3

Research on Distributed Parallel Eclat Optimization Algorith...

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3rd international conference on Artificial Intelligence and Big Data (ICAIBD)

作者： Huang Qiufeng Li Qiang Huang Shiya Chen Yingcong Southwest Univ Sci & Technol Sch Informat Engn Mianyang Sichuan Peoples R China

ISBN: (数字)9781728197418

ISBN: (纸本)9781728197418

Frequent Itemset Mining (FIM) is the core of tasks such as association rules and sequential pattern mining. With the increasing amount of data, traditional FIM algorithms become inefficient due to excessive resource requirements or high communication costs. In this paper, the Eclat algorithm in the frequent itemset mining algorithm is taken as the research point, and the parallel Eclat optimization algorithm BPEclat (Balanced parallel Eclat) based on Spark is proposed to solve the performance shortcoming of Eclat algorithm in serial processing large-scale data. The algorithm is improved and optimized from many aspects: combining the pre-pruning and post-pruning depth pruning strategies to reduce the calculation of irrelevant itemset, compressing the candidate set size;using the prefix term to divide the data set, and using the range partitioning idea to balance the calculations node load, improve the parallel computing power of the algorithm. The experimental results show that the proposed BPEclat algorithm reduces the candidate set size by 25.3% and the time consumption by 32.5%. Therefore, it is possible to process massive amounts of data more efficiently and reliably, and has good scalability and universality.

关键词： frequent itemsets bit storage spark parallel computing

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DCSA: The Deployment Mechanism of Chained Serverless applications in JointCloud Environment

DCSA: The Deployment Mechanism of Chained Serverless Applica...

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IEEE international conference on Joint Cloud Computing (JCC)

作者： Yaojie Li Peichang Shi Jianfei Liu Rui Li Fei Gao Penghui Ma Dong Xie Guodong Yi National Key Laboratory of Parallel and Distributed Processing College of Computer Science National University of Defense Technology Changsha China Xiangjiang Lab Changsha China School of Advanced Interdisciplinary Studies Hunan University Of Technology and Business Changsha China

ISBN: (数字)9798350387339

ISBN: (纸本)9798350387346

Serverless computing, comprised of Function as a Service (FaaS) and Backend as a Service (BaaS), has garnered widespread attention owning to its features such as maintenance-free operations, pay-per-use pricing, and automatic scalability. However, practical usage encounters several challenges: 1) The diversity of user applications makes comprehensive performance evaluation difficult, as benchmark and application tests only reflect performance under specific conditions and cannot fully capture users’ actual experiences across different serverless platforms. 2) Disparities in performance and costs across different serverless platforms make it challenging to achieve optimal performance and cost efficiency through single-cloud deployment, thereby underutilizing the advantages of each platform. 3) Vendor lock-in issues restrict the migration of user applications and exacerbate dependence on a single cloud *** address these challenges, this paper proposes a collaborative mechanism, referred to as DCSA, which integrates FaaS and storage services to achieve automatic cross-cloud deployment of user applications while considering both performance and cost comprehensively. Firstly, we adapt the interfaces of different serverless platforms, effectively reducing the complexity of cross-cloud deployment. Secondly, we develop cost and latency models for the cross-cloud deployment of chained serverless applications and propose a deployment scheduling algorithm that simultaneously considers both latency and cost. Finally, we conduct experiments to evaluate the performance of the proposed algorithm. Results demonstrate that our method can effectively reduce latency (up to 2.3%) and lower costs (up to 9.9%).

关键词： Performance evaluation Adaptation models Costs Scheduling algorithms Scalability Serverless computing Collaboration

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The 3-Set Tree Connectivity of the Folded Petersen Cube Networks 1

引用

3rd EAI international conference on Artificial Intelligence for Communications and Networks, AICON 2021

作者： Ge, Huifen Zhang, Shumin Li, He Ye, Chengfu School of Computer Qinghai Normal University Xining810008 China School of Mathematics and Statistics Qinghai Normal University Xining810008 China Academy of Plateau Science and Sustainability People’s Government of Qinghai Province Beijing Normal University Xining China

ISBN: (数字)9783030901998

ISBN: (纸本)9783030901981

The layout of the processors and links are represented a network structure in a distributed computer system. A graph is regarded as the topological structure of an interconnection network as usual, vertices and edges stand for processors and links between them, respectively. The internally disjoint Steiner trees of graphs are used in information engineering design and telecommunication networks. The -set tree connectivity (also is called generalized -connectivity), as an extension of the traditional connectivity, can provide for measuring the capability of connection arbitrary vertices in a network. The folded Petersen cube networks FPQn,k have good properties and can be modeled the topological structure of a massively parallel processing system. This article shows the 3-set tree connectivity of FPQn,k is n+ 3 k- 1. © 2021, ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering.

关键词： 3-set tree connectivity Cartesian product Folded Petersen cube networks Interconnection network

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Massively scalable image processing on the HPCC systems big data platform 20

Massively scalable image processing on the HPCC systems big ...

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3rd international conference on Software Engineering and Information Management, ICSIM 2020 - and its Workshop 2020 the 3rd international conference on Big Data and Smart Computing, ICBDSC 2020

作者： Hukkeri, Tanmay Sanjay Shobha, G. Phal, Shubham Milind Shetty, Jyothi Yatish, H.R. Mohammed, Naweed RV College of Engineering Bengaluru India LexisNexis Risk Solutions India

ISBN: (纸本)9781450376907

Today's fast-moving world sees an abundance of image data in everyday life. From messages to insurance claims to even judicial systems, image data plays a pivotal role in facilitating several critical Big Data applications. Some of these applications such as automatic license plate recognition (ALPR) use CCTV cameras to capture snapshots of traffic from real-time video, inadvertently resulting in the generation vast amounts of image data on a daily basis. This brings with it the herculean task of processing these images to extract the essential information as efficiently as possible. The conventional method of processing images in a sequential manner can be very time consuming on account of the vast multitude of images and the intensive computation involved in order to process these. distributed image processing seeks to provide a solution to this problem by splitting the computations involved across multiple nodes. This paper presents a novel framework to implement distributed image processing via OpenCV on HPCC Systems distributed node architecture, a set of high-performance computing clusters. The proposed approach when tested on the Indian License Plates Dataset was found to be 85 percent accurate. Additionally, a 30 percent decrease in computation time was observed when executed on a multi-node setup without any impact to accuracy. © 2020 Association for Computing Machinery.

关键词： Big data

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Practical parallelization of Scientific applications 28

Practical Parallelization of Scientific Applications

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28th Euromicro international conference on parallel, distributed and Network-Based processing (PDP)

作者： Cesare, Valentina Colonnelli, Iacopo Aldinucci, Marco Univ Turin Phys Dept Turin Italy Univ Turin Comp Sci Dept Turin Italy

ISBN: (纸本)9781728165820

This work aims at distilling a systematic methodology to modernize existing sequential scientific codes with a limited re-designing effort, turning an old codebase into modern code, i.e., parallel and robust code. We propose an automatable methodology to parallelize scientific applications designed with a purely sequential programming mindset, thus possibly using global variables, aliasing, random number generators, and stateful functions. We demonstrate the methodology by way of an astrophysical application, where we model at the same time the kinematic profiles of 30 disk galaxies with a Monte Carlo Markov Chain (MCMC), which is sequential by definition. The parallel code exhibits a 12 times speedup on a 48-core platform.

关键词： loop parallelism checkpointing scientific code open MP

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Deep Learning Platforms for High-Performance and distributed Computing Object Detection 3

Deep Learning Platforms for High-Performance and Distributed...

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3rd international conference on High Technology for Sustainable Development, HiTech 2020

作者： Lazarova, Milena Nakov, Ognyan Mihaylova, Elizabet Technical University of Sofia Dept. Computer Systems Sofia Bulgaria

ISBN: (纸本)9781728186511

Deep learning developed in the last decade and has been established as recent, modern and very promising technique with large potential to be successfully applied in various domains. Despite deep learning outperforms alternative techniques in providing solution to many different problems there are several concerns and limitations of the its usage, one of them being the computational complexity of the training process. The paper discusses the possibilities of taking advantage of high-performance and distributed computing for efficiently and effectively training deep learning models for computer vision applications. A comparison of deep learning platforms and frameworks utilized on high-performance computer systems is presented and their application for computer vision problem of object detection is evaluated based on a suggested parallel computation architectural model and utilization of RetinaNet model. © 2020 IEEE.

关键词： Computer vision

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