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检索条件"任意字段=International Symposium on Databases in Parallel and Distributed Systems"

共 9684 条记录，以下是411-420 订阅

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An In-Situ Sliding Window Approximate Inner-Product Scheme Based on parallel distributed Arithmetic for Ultra-Low Power Fault-Tolerant Applications

An In-Situ Sliding Window Approximate Inner-Product Scheme B...

引用

IEEE international Midwest symposium on Circuits and systems (MWSCAS)

作者： Rizk, Dominick Rizk, Rodrigue Rizk, Frederic Kumar, Ashok Univ Louisiana Lafayette Ctr Adv Comp Studies Lafayette LA 70504 USA

ISBN: (纸本)9781665424615

Approximate computing (AC) provides an efficient solution for reducing power, area, and complexity of digital systems. When backed with distributed arithmetic (DA), AC leverages the ability to implement ultra-efficient inner-product units in terms of area, power, and delay. Such units can be used in any inherently resilient application. This paper presents a novel scheme of approximate inner-product based on parallel DA for low-power fault-tolerant applications backed with a novel in-situ sliding window algorithm. Our model eliminates the need for an explicit error correction scheme, which further reduces the overhead while improving the accuracy. The experimental results show that our model achieves a state-of-the-art performance in terms of power delay product (PDP), area power product (APP) with a reduction of 39.26% and 48.83%, respectively.

关键词： Approximate computing distributed arithmetic DSP inner-product LUT memory VLSI SOP

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Automatic aggregation of subtask accesses for nested OpenMP-style tasks 34

Automatic aggregation of subtask accesses for nested OpenMP-...

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34th IEEE international symposium on Computer Architecture and High Performance Computing (SBAC-PAD)

作者： Shaaban, Omar Aguilar, Jimmy Beltran, Vicenc Carpenter, Paul Ayguade, Eduard Labarta Mancho, Jesus Barcelona Supercomp Ctr Barcelona Spain

ISBN: (数字)9781665451550

ISBN: (纸本)9781665451550

Task-based programming is a high performance and productive model to express parallelism. Tasks encapsulate work to be executed across multiple cores or offloaded to GPUs, FPGAs, other accelerators or other nodes. In order to maintain parallelism and afford maximum freedom to the scheduler, the task dependency graph should be created in parallel and well in advance of task execution. A key limitation with OpenMP and OmpSs-2 tasking is that a task cannot be created until all its accesses and its descendents' accesses are known. Current approaches to work around this limitation either stop task creation and execution using a taskwait or they substitute "fake" accesses known as sentinels. This paper proposes the auto clause, which indicates that the task may create subtasks that access unspecified memory regions or it may allocate and return memory at addresses that are of course not yet known. Unlike approaches using taskwaits, there is no interruption to the concurrent creation and execution of tasks, maintaining parallelism and the scheduler's ability to optimize load balance and data locality. Unlike existing approaches using sentinels, all tasks can be given a precise specification of their own data accesses, so that a single mechanism is used to control task ordering, program data transfers on distributed memory and optimize data locality, e.g. on NUMA systems. The auto clause also provides an incremental path to develop programs with nested tasks, by removing the need for every parent task to have a complete specification of the accesses of its descendent tasks. This is redundant information that can be time consuming and error-prone to describe. We present a straightforward runtime implementation that achieves a 1.4 times speedup for n-body with OmpSs-2@Cluster task offloading to 32 nodes and <4% slowdown for three benchmarks with task offloading to 8 nodes. All code is open source.

关键词： HPC Runtime systems OpenMP OmpSs-2 Task parallelism distributed Computing

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Scheduling and Allocation of Disaggregated Memory Resources in HPC systems

Scheduling and Allocation of Disaggregated Memory Resources ...

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

作者： Jie Li George Michelogiannakis Brandon Cook John Shalf Yong Chen Texas Tech University USA Berkeley Lab USA

ISBN: (数字)9798350364606

ISBN: (纸本)9798350364613

Disaggregated architectures that separate memory and compute resources have gained interest recently [1]. Past research has mainly examined the effects of remote memory on jobs and various implementation architectures. However, there's a gap in understanding the optimal size of local and remote memory and in developing scheduling policies to improve both application performance, job queuing time, and memory utilization. This paper focuses on three areas using a data-driven approach: examining the scale of memory disaggregation and its tradeoffs, assessing the need for unique policies for remote memory management and their effect on system performance, and investigating job scheduling policies for specific goals in a memory-disaggregated HPC system.

关键词： distributed processing Processor scheduling System performance Conferences Memory management Memory architecture Resource management

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A distributed Ledger with Trust-Based Data Aggregation for a Lightweight IoT Network 17

A Distributed Ledger with Trust-Based Data Aggregation for a...

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17th international symposium on Wireless Communication systems (ISWCS)

作者： Deshprabhu, Tanmayee Coon, Justin P. Badiu, Mihai-Alin Univ Oxford Dept Engn Sci Oxford OX1 3PJ England

ISBN: (纸本)9781728174327

This paper addresses the need for a secure data management framework for lightweight Internet of Things (IoT) networks. Existing approaches rely on capable nodes or external cloud technology to carry out key functions for the lightweight IoT nodes, such as data processing, storage or routing. Instead, this paper considers a fully lightweight network or an existing network where the more capable nodes have failed. We propose a novel data management framework using a lightweight distributed ledger for IoT networks. This includes a trust-based data aggregation protocol in which data from untrustworthy nodes is not disregarded, but is instead utilised to strengthen the aggregation result. MATLAB simulations show that the proposed aggregation scheme infers data with high accuracy for both trustworthy and untrustworthy networks. Neutral networks exhibit a higher error rate but the maximum error rate decreases for larger networks.

关键词： Wireless communication Wireless sensor networks Error analysis distributed ledger Simulation distributed databases Data aggregation

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Optimization of key and value storage in vector map rendering 19

Optimization of key and value storage in vector map renderin...

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19th IEEE international symposium on parallel and distributed Processing with Applications (IEEE ISPA)

作者： Jing, Feng Jiang, Ying Chen, Shengbin Lv, Xirong He, Jiayi State Grid Shanxi Elect Power Co Taiyuan Peoples R China Xiamen Great Power Geo Informat Technol Co Ltd Xiamen Peoples R China Xiamen Univ Technol Sch Comp & Informat Engn Xiamen Peoples R China

ISBN: (纸本)9781665435741

With the continuous development of data-centric Internet applications, data management solutions for massive data have gradually become a new research direction in vector map rendering. The introduction of distributed "cloud computing" technology has continuously improved the storage and management of massive data. Supported by a large number of distributed systems, it provides normal services to the outside world, as well as fast data location services. The system has good performance in data storage efficiency, node load balancing and system scalability, and has an efficient data positioning mechanism. The basic idea of the system design draws on the hash algorithm and the consistent hash algorithm, and improves the scalability of the basic algorithm. Through the three-tier layout: front-end server layer, consistent hash layer, data node layer, to maximize the consistency, availability, efficiency and scalability of the system.

关键词： distributed storage system consistent hash key value

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High-Performing Training Large-Scale Binary Nonlinear SVMs Using Mean Decision Rule Method with Smart Sampling 8

High-Performing Training Large-Scale Binary Nonlinear SVMs U...

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8th international Conference on Information Technology and Nanotechnology, ITNT 2022

作者： Kurbakov, Mikhail Kopylov, Andrey Seredin, Oleg Sulimova, Valentina Tula State Univerity Laboratory of Cognitive Technologies and Simulation Systems Tula Russia

ISBN: (数字)9781665485715

ISBN: (纸本)9781665485715

In spite of multiple investigations in the field of large-scale SVM tasks, the problem of their solution is still actual. Previously, we proposed the kernel-based mean decision rules method with smart sampling (SS-KMDR). According to experiments, it provides the possibility to obtain results close in quality to the reference ones, but for much less time compared to traditional methods. In this paper, we present a new high-performance implementation (HP-SS-KMDR), which allows us to further speed-up computations through the development of efficient parallel algorithms and modern technologies for parallel and distributed computing. Besides, experiments show that in a number of cases the proposed HP-SS-KMDR method allows us to increase the recognition quality compared to the sequential version due to the possibility to process more data in less time. © 2022 IEEE.

关键词： Support vector machines Training Program processors distributed databases Computational efficiency Character recognition Task analysis

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A distributed Stealth Address Generation Protocol for Threshold Signatures

A Distributed Stealth Address Generation Protocol for Thresh...

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international symposium on parallel and distributed Processing with Applications, ISPA

作者： Yujue Wang Lin Zhong Jun Du Yudi Zou Kevin He Andrew Zhang Hangzhou Innovation Institute of Beihang University Hangzhou China Sinohope Technology Holdings Limited China Vistra Corporate Service Centre Bitlayer Labs Ltd

ISBN: (数字)9798331509712

ISBN: (纸本)9798331509729

Stealth addresses protect recipient identity privacy in blockchain systems by allowing a sender to derive a stealth address using the recipient’s public key, with the receiver deriving a corresponding one-time private key. In threshold signatures, where distributed recipient entities only hold private key shares and the full private key never appears, this becomes challenging. To address this, we propose a distributed stealth address generation (DSAG) protocol for threshold signatures. In our approach, distributed sender entities derive the stealth address, and distributed recipient entities derive corresponding one-time private key shares using Lagrange interpolation. The correctness of these shares is verified by ensuring that they satisfy the necessary Lagrange interpolation and discrete logarithm relationships. Our protocol avoids bilinear mappings and relies solely on elliptic curve exponentiations, leading to higher efficiency. Experimental tests demonstrate that our scheme instance takes just 4 milliseconds confirming its practicality.

关键词： Interpolation Privacy distributed processing Protocols Elliptic curves Public key Receivers Blockchains Security Computational complexity

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Using a Deployable Analysis Environment to Study Continental-Scale Phenology at High-Spatial Resolution

Using a Deployable Analysis Environment to Study Continental...

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IEEE international symposium on Geoscience and Remote Sensing (IGARSS)

作者： Francesco Nattino Emma Izquierdo-Verdiguier Pranav Chandramouli Meiert W. Grootes Ou Ku Fakhereh Alidoost Raúl Zurita-Milla Netherlands eScience Center Science Park 402 (Matrix III) Amsterdam The Netherlands Institute of Geomatics University of Natural Resources and Life Science (BOKU) Vienna Austria Faculty of Geo-Information Science and Earth Observation (ITC) University of Twente Enschede The Netherlands

ISBN: (数字)9798350360325

ISBN: (纸本)9798350360332

The increasing availability of large open datasets and computational resources offers unprecedented opportunities for new insight and discoveries. However, researchers are often faced with the challenge of running analyses at scale. We consider here an example from the domain of plant phenology and show how a Python-based deployable analysis environment leveraging Dask for parallel and distributed computing as well as Jupyter for interactive data exploration could be employed to run regression-based phenological models at high spatial resolution and continental scale.

关键词： Analytical models Computational modeling Phenology Geoscience and remote sensing distributed databases Data models Spatial resolution

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Quantum Convolutional Neural Network based on Variational Quantum Fuzzy Inference

Quantum Convolutional Neural Network based on Variational Qu...

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

作者： FangCong Zheng ZhaoLin Liu ShiBin Zhang School of Cyberspace Security Chengdu University of Information Technology Chengdu China

ISBN: (数字)9798350352719

ISBN: (纸本)9798350352726

In recent years, with the rapid development of quantum technology, quantum machine learning has attracted wide attention. In this paper, we propose a novel quantum machine learning model which introduces variational quantum gates in the quantum fuzzy inference circuits, and adjusts the variational gate parameters to automatically learn fuzzy rules for data classification through quantum machine learning training. Comparison results on the iris dataset show that our variational quantum fuzzy inference quantum convolutional neural network (VQFI-QCNN) outperforms classical multi layer perceptron (MLP) neural networks with a similar number of parameters, demonstrating the potential advantages of quantum. Due to the better results on the iris structured dataset, our VQFI-QCNN deserves further investigation, and as the number of quantum bits available for quantum computers increases, our method may also be applied in the future to efficiently classify structured data in mega data.

关键词： Fuzzy logic Training Iris Quantum computing Neural networks distributed databases Machine learning

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An Architecture and Prototype for Monitoring distributed Simulations of distributed systems

An Architecture and Prototype for Monitoring Distributed Sim...

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international Workshop/symposium on distributed Simulation and Real-Time Applications (DS-RT)

作者： Angelo Filaseta Danilo Pianini Angela Cortecchia Department of Computer Science and Engineering Alma Mater Studiorum—Università di Bologna Cesena Italy

ISBN: (数字)9798331527211

ISBN: (纸本)9798331527228

Designing and testing distributed systems can be a daunting task, especially when the system is composed of heterogeneous devices, it is expected to be robust to disruptions, and is meant to scale to a large number of nodes. In practice, this mandates the use of simulations to verify the system during development. Additionally, disruptions and disturbance patterns are hardly predictable, and the system’s behavior must thus be subject to test under widely varying conditions, typically implying a stochastic process and multiple simulation runs to assess whether the system meets its functional requirements. Often, running multiple simulations while a system is being developed is time- and resource-consuming, as results are visible and aggregated at termination, especially in cases in which simulations are distributed across multiple devices to speed up the computation. In this paper, we discuss a potential solution to monitor multiple distributed simulations, querying data efficiently while they progress, in order to provide early feedback to the developers. We present a general architecture for the system, discuss the technological opportunities and challenges, and provide a functional open-source prototype implementation based on a simulator used for several scientific exemplaries in the last decade.

关键词： Analytical models Runtime Computational modeling Prototypes distributed databases Computer architecture Data models Monitoring Tuning Context modeling

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