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检索条件"任意字段=International Conference on Parallel and Distributed Processing Techniques and Applications"

共 11317 条记录，以下是1061-1070 订阅

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GPU based Near Data processing for Image processing with Pattern Aware Data Allocation and Prefetching 19

GPU based Near Data Processing for Image Processing with Pat...

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Proceedings of the international conference on parallel Architectures and Compilation techniques

作者： Jungwoo Choi Boyeal Kim Ji-Ye Jeon Hyuk-Jae Lee Euicheol Lim Chae Eun Rhee Seoul National University SKHynix Inha University

ISBN: (纸本)9781728136134

This paper proposes a GPU-based Near-data-processing (NDP) architecture as well as a well-matched programming model considering both the characteristics of image applications and NDP constraints. First, data allocation to the processing unit is handled to keep the data locality considering the memory access pattern. Second, this predictable allocation enables to design a compact but efficient NDP architecture. By applying a prefetcher that leverages the pattern aware data allocation, the number of active warps and on-chip SRAM size of NDP are significantly reduced. This allows to satisfy the NDP constraints and increases the opportunity to integrate more processing units on a memory logic die. The evaluation results for various image processing benchmarks show that the proposed NDP GPU improves the performance compared to the baseline GPU.

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Scheduling Task-parallel applications in Dynamically Asymmetric Environments 20

Scheduling Task-parallel Applications in Dynamically Asymmet...

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49th international conference on parallel processing (ICPP)

作者： Chen, Jing Soomro, Pirah Noor Abduljabbar, Mustafa Manivannan, Madhavan Pericas, Miquel Chalmers Univ Technol Gothenburg Sweden

ISBN: (纸本)9781450388689

Shared resource interference is observed by applications as dynamic performance asymmetry. Prior art has developed approaches to reduce the impact of performance asymmetry mainly at the operating system and architectural levels. In this work, we study how application-level scheduling techniques can leverage moldability (i.e. flexibility to work as either single-threaded or multithreaded task) and explicit knowledge on task criticality to handle scenarios in which system performance is not only unknown but also changing over time. Our proposed task scheduler dynamically learns the performance characteristics of the underlying platform and uses this knowledge to devise better schedules aware of dynamic performance asymmetry, hence reducing the impact of interference. Our evaluation shows that both criticality-aware scheduling and parallelism tuning are effective schemes to address interference in both shared and distributed memory applications.

关键词： Interference awareness Task scheduling Asymmetry

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Reliability on distributed System Considering Transmission Lines

Reliability on Distributed System Considering Transmission L...

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IEEE international conference on Power and Energy applications (ICPEA)

作者： Yishuang Hu Yimin Bo School of Information and Electrical Engineering Hangzhou City University Hangzhou China College of Electrical Engineering Zhejiang University Hangzhou China

ISBN: (纸本)9781665487627

Considering the highly penetration of distributed generators, the distribution system has been extensively studied in recent year. However, traditional reliability evaluation algorithms require huge computational resources when the number of distributed generators is large. Therefore, in this paper, similar to series-parallel systems, a distributed seriesparallel system is defined to release the computational burden. In this system, the interaction between the basic components and distributed components is concerned and the structure between these components is described as a series-parallel component. The uniqueness structures of distributed series-parallel system have been taken into account and two analytical reliability assessment techniques in terms of two scenarios are defined. The relationships between series-parallel components are quantified and the distributed generators are equivalent as ordinal generators. The comparisons on accuracy and efficiency of the proposed methods with traditional methods are demonstrated. Case studies are conducted to verify the proposed models.

关键词： Power transmission lines Computational modeling Generators Numerical models Computational efficiency Reliability

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A Hybrid Algorithm Based on PSO Algorithm and Chi-Squared Distribution for Tasks Consolidation in Cloud Computing Environment

A Hybrid Algorithm Based on PSO Algorithm and Chi-Squared Di...

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international conference on Cloud Technologies and applications (CloudTech)

作者： Hind Mikram Said El Kafhali Youssef Saadi Hassan First University of Settat Faculty of Sciences and Techniques IR2M Laboratory Settat Morocco Faculty of Sciences and Techniques DSSE Laboratory Sultan Moulay Slimane University Beni Mellal Morocco

In order to maximize the effectiveness and performance of cloud computing systems, this study focuses on addressing the challenges of workload balancing and resource utilization in cloud scheduling. Workload balancing plays a crucial role in ensuring that computing workloads are evenly distributed across available resources, thereby reducing the likelihood of resource constraints and enhancing system performance. On the other hand, resource utilization aims to utilize processing power, memory, and network bandwidth to their fullest capacity, resulting in improved efficacy and cost-effectiveness of the cloud infrastructure. To tackle these challenges, we propose a novel optimization technique called CHPSO (Chi-squared Particle Swarm Optimization) in this context. The proposed algorithm demonstrates its effectiveness in optimizing resource utilization compared to other algorithms such as PSO (Particle Swarm Optimization) and CS (Cuckoo Search).

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Fast parallel Multiple Access distributed Arithmetic (FPMA-DA) Reconfigurable FIR Filter

Fast Parallel Multiple Access Distributed Arithmetic (FPMA-D...

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international conference on Microelectronics, ICM

作者： Ahmed H.A. Bayoumi Sameh A. Ibrahim Hossam A.H. Fahmy Electronics and Communication Department Ain Shams University Si-Vision Company Cairo Egypt Electronics and Communication Department Ain Shams University Cairo Egypt Electronics and Communication Department Cairo University Cairo Egypt

A finite impulse response (FIR) filter is an influential block in various signal processing applications. The complexity in VLSI implementation of FIR filters is dominated by the number of multiply and accumulate (MAC) operations. distributed Arithmetic (DA) is an alternative technique where the $MAC$ operations can be replaced by a series of memory elements and addition operations. FIR filters based on DA are computationally efficient because of the high degree of mechanization involved in the implementation of MAC operations using DA. Many reconfigurable and non reconfigurable FIR filter architectures can be developed using DA. This paper reviews the direct FIR filter implementation and compares it to the existing FIR filter architectures based on DA and the proposed fast parallel multiple access FPMA DA FIR filter different architectures. The FPMA DA FIR filter can achieve $ 26\%$ better than direct implementation in terms of power consumption numbers and $36\%$ better in terms of area numbers at high computation complexity, and can operate at a maximum frequency 120MHz higher than the direct FIR filter implementation.

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Building Agile Workflow Microservice System for HPC applications Based on Fast-start OSv 27

Building Agile Workflow Microservice System for HPC Applicat...

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27th IEEE international conference on parallel and distributed Systems, ICPADS 2021

作者： Hu, Qiang Ma, Lixian Shao, En Tan, Guangming Institute of Computing Technology CAS State Key Laboratory of Computer Architecture Beijing China University of Chinese Academy of Science Beijing China

ISBN: (纸本)9781665408783

The advances of containers have significantly promoted the development of microservice architecture. This architecture splits a monolithic application into multiple independent components and the container orchestrator manages these components by the container in the cloud environment. The feasibility of deploying high performance computing(HPC) applications as microservices has been proven, but the existing container orchestrator incurs a large performance overhead as there is interference between different containers on the same physical host. In this paper, we design an agile workflow microservice system for HPC applications with fast-start OSv. We consider improving HPC workflow performance from two aspects: single OSv startup time optimization and workflow orchestration optimization. For single OSv startup time optimization, we design a fast-start OSv by analyzing the process of OSv startup and finding an optimization by modifying OSv source code. In this way, we get nearly 50% improvement of startup time. For workflow orchestration optimization, we propose four optimization techniques to speed up the execution of workflow by jointly considering OSv and workflow features, namely: node fusion, node merge, image preload, boot delay. Furthermore, we utilize our fast startup OSv to design an orchestration system for efficiently building an agile HPC workflow microservice by Kubevirt. Our experimental results optimization microservice system reduces the execution time by 30% compared with the original deployment with docker. © 2021 IEEE.

关键词： Containers

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applications of Artificial Intelligence-Based Communication Systems in Rail Transit

Applications of Artificial Intelligence-Based Communication ...

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Internet of Things, Automation and Artificial Intelligence (IoTAAI), international conference on

作者： Yuming Fu Lanzhou Institute of Technology Lanzhou China

ISBN: (数字)9798350386974

ISBN: (纸本)9798350386981

This paper explores the integration of Artificial Intelligence (AI) into communication systems within urban rail transit, emphasizing the development of a Train-to-Train (TT) communication-based train control system that deviates from traditional centralized control methods. By utilizing the capabilities of Deep Reinforcement Learning (DRL) and neural network architectures, the proposed system enhances operational efficiency, safety, and reliability of train operations by allowing autonomous and distributed decision-making directly on trains. The application of a multi-layer fully connected neural network enables sophisticated data processing and decision-making through deep learning techniques, addressing the challenges of resource allocation in dense urban transit scenarios. Simulation results validate the effectiveness of the AI-driven approach, demonstrating significant improvements in the quality of service (QoS), spectral efficiency, and system throughput under varying train densities. The study confirms that AI can play a crucial role in advancing the intelligence and efficiency of rail transit systems, offering a scalable solution for future developments in urban transportation.

关键词： Rails Spectral efficiency Neural networks Decision making Transportation Quality of service Data processing Safety Internet of Things Resource management

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parallel and Batch Multiple Replica Auditing Protocol for Edge Computing

Parallel and Batch Multiple Replica Auditing Protocol for Ed...

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IEEE international conference on Big Data and Cloud Computing (BdCloud)

作者： Yi Li Wenying Zheng Vijayakumar Pandi Md Zakirul Alam Bhuiyan C. Thamilarasi School of Computer Science Nanjing University of Information Science and Technology Nanjing China School of Computer Science and Technology Zhejiang Sci-Tech University Hangzhou China Department of Computer Science and Engineering University College of Engineering Tindivanam Anna University Chennai Melpakkam India Department of Computer and Information Sciences Fordham University Bronx NY USA Department of Electronics and communication engineering P.S.V College of Enfgineering and Technoogy Krishnagiri India

The edge computing architecture exposes data transmission and storage to unauthorized access, compromising data integrity. Users typically employ multiple data backups to ensure data reliability and availability, enhancing data redundancy and recovery capabilities. Nevertheless, verifying the integrity of multiple backup data under edge computing entails computational and communication overhead. In response, we present a parallel and batch multiple replica auditing protocol tailored for edge computing. Contributions include an efficient multiple replica construction method based on RSA, minimizing computational overhead, and a parallel data integrity verification approach that reduces communication overhead. A prototype implementation is evaluated in a real edge computing environment and compared to existing multiple replica auditing approaches.

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Training Data Reduction for Performance Models of Data Analytics Jobs in the Cloud

Training Data Reduction for Performance Models of Data Analy...

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9th IEEE international conference on Big Data (IEEE BigData)

作者： Will, Jonathan Arslan, Onur Bader, Jonathan Scheinert, Dominik Thamsen, Lauritz Tech Univ Berlin Berlin Germany Humboldt Univ Berlin Germany

ISBN: (纸本)9781665439022

distributed dataflow systems like Apache Flink and Apache Spark simplify processing large amounts of data on clusters in a data-parallel manner. However, choosing suitable cluster resources for distributed dataflow jobs in both type and number is difficult, especially for users who do not have access to previous performance metrics. One approach to overcoming this issue is to have users share runtime metrics to train context-aware performance models that help find a suitable configuration for the job at hand. A problem when sharing runtime data instead of trained models or model parameters is that the data size can grow substantially over time. This paper examines several clustering techniques to minimize training data size while keeping the associated performance models accurate. Our results indicate that efficiency gains in data transfer, storage, and model training can be achieved through training data reduction. In the evaluation of our solution on a dataset of runtime data from 930 unique distributed dataflow jobs, we observed that, on average, a 75% data reduction only increases prediction errors by one percentage point.

关键词： Scalable Data Analytics distributed Dataflows Resource Allocation Machine Learning Data Reduction

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Transparent Batchable Time-lock Puzzles and applications to Byzantine Consensus 26th

Transparent Batchable Time-lock Puzzles and Applications to ...

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26th IACR international conference on Practice and Theory of Public Key Cryptography (PKC)

作者： Srinivasan, Shravan Loss, Julian Malavolta, Giulio Nayak, Kartik Papamanthou, Charalampos Thyagarajan, Sri AravindaKrishnan Univ Maryland College Pk MD 20742 USA CISPA Helmholtz Ctr Informat Secur Saarbrucken Germany Max Planck Inst Secur & Privacy Bochum Germany Duke Univ Durham NC USA Yale Univ New Haven CT USA NTT Res Sunnyvale CA USA

ISBN: (纸本)9783031313677;9783031313684

Time-lock puzzles (TLP) are a fascinating type of cryptographic problem that is easy to generate, but takes a certain time to solve, even when arbitrary parallel speedup is allowed. TLPs have wide-ranging applications including fairness, round efficient computation, and more. To reduce the effort needed to solve large numbers of TLPs, prior work has proposed batching techniques to reduce the cost of solving. However, these proposals either require: (1) a trusted setup or (2) the puzzle size be linear in the maximum batch size, which implies setting an a priori bound on the maximum size of the batch. Any of these limitations restrict the utility of TLPs in decentralized and dynamic settings like permissionless blockchains. In this work, we demonstrate the feasibility and usefulness of a TLP that overcomes all the above limitations using indistinguishability obfuscation to show that there are no fundamental barriers to achieving such a TLP construction. As a main application of our TLP, we show how to improve the resilience of consensus protocols toward network-level adversaries in the following settings: (1) We show a generic compiler that boosts the resilience of a Byzantine broadcast protocol. as follows: if. is secure against t < n weakly adaptive corruptions, then the compiled protocol is secure against t < n strongly adaptive corruptions. Here, 'strong' refers to adaptively corrupting a party and deleting messages that it sent while still honest. Our compiler is round and communication preserving, and gives the first expected constant-round Byzantine broadcast protocol against a strongly adaptive adversary for the dishonest majority setting. (2) We adapt the Nakamoto consensus protocol to a weak model of synchrony where the adversary can adaptively create minority partitions in the network. Unlike prior works, we do not assume that all honest messages are delivered within a known upper bound on the message delay. This is the first work to show that it is possibl

关键词： Time-lock puzzles Batch solving distributed consensus Byzantine broadcast Mobile-sluggish faults

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