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检索条件"任意字段=International Conference on Parallel Distributed and Grid Computing"

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EQC: Ensembled Quantum computing for Variational Quantum Algorithms 22

EQC: Ensembled Quantum Computing for Variational Quantum Alg...

引用

49th IEEE/ACM Annual international Symposium on Computer Architecture (ISCA)

作者： Stein, Samuel Wiebe, Nathan Ding, Yufei Bo, Peng Kowalski, Karol Baker, Nathan Ang, James Li, Ang Pacific Northwest Natl Lab Richland WA 99354 USA Univ Calif Santa Barbara Santa Barbara CA USA Univ Toronto Toronto ON Canada

ISBN: (纸本)9781450386104

Variational quantum algorithm (VQA), which is comprised of a classical optimizer and a parameterized quantum circuit, emerges as one of the most promising approaches for harvesting the power of quantum computers in the noisy intermediate scale quantum (NISQ) era. However, the deployment of VQAs on contemporary NISQ devices often faces considerable system and time-dependant noise and prohibitively slow training speeds. On the other hand, the expensive supporting resources and infrastructure make quantum computers extremely keen on high utilization. In this paper, we propose a virtualized way of building up a quantum backend for variational quantum algorithms: rather than relying on a single physical device which tends to introduce ever-changing device-specific noise with less reliable performance as time-since-calibration grows, we propose to constitute a quantum ensemble, which dynamically distributes quantum tasks asynchronously across a set of physical devices, and adjusts the ensemble configuration with respect to machine status. In addition to reduced machine-dependant noise, the ensemble can provide significant speedups for VQA training. With this idea, we build a novel VQA training framework called EQC - a distributed gradient-based processor-performance-aware optimization system - that comprises: (i) a system architecture for asynchronous parallel VQA cooperative training;(ii) an analytical model for assessing the quality of a circuit output concerning its architecture, transpilation, and runtime conditions;(iii) a weighting mechanism to adjust the quantum ensemble's computational contribution according to the systems' current performance. Evaluations comprising 500K times' circuit evaluations across 10 IBMQ NISQ devices using a VQE and a QAOA applications demonstrate that EQC can attain error rates very close to the most performant device of the ensemble, while boosting the training speed by 10.5x on average (up to 86x and at least 5.2x). EQC is available at

关键词： Quantum computing Variational Quantum Algorithms distributed computing

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Proactive Failure Recovery for Stateful NFV 26

Proactive Failure Recovery for Stateful NFV

引用

26th IEEE international conference on parallel and distributed Systems (IEEE ICPADS)

作者： Huang, Zhenyi Huang, Huawei Sun Yat Sen Univ Sch Data & Comp Sci Guangzhou Peoples R China

ISBN: (纸本)9781728190747

Network Function Virtualization (NFV) technology is viewed as a significant component of both the fifth-generation (5G) communication networks and edge computing. In this paper, through reviewing the state-of-the-art work on applying NFV to edge computing, we identify that an urgent research challenge is to provide the proactive failure recovery mechanism for the stateful NFV. To realize such proactive failure recovery, we propose a prediction-based algorithm for redeploying the stateful NFV instances in real-time when network failures occur. The proposed algorithm is based on relax and rounding technique. The theoretical performance guarantee is also analyzed rigorously. Simulation results show that the proposed failure recovery algorithm outperforms the reactive-manner baselines significantly in terms of redeployment latency.

关键词： NFV Failure Recovery Edge computing

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Speed-up Single Shot Detector on GPU with CUDA

Speed-up Single Shot Detector on GPU with CUDA

引用

Summer Virtual conference on Software Engineering, Artificial Intelligence, Networking and parallel/distributed computing (SNPD-Summer), ACIS international

作者： Chenyu Wang Toshi Endo Takahiro Hirofuchi Tsutomu Ikegami Tokyo Institute of Technology & National Institute of Advanced Industrial Science and Technology Tokyo Japan Tokyo Institute of Technology Tokyo Japan National Institute of Advanced Industrial Science and Technology Tokyo Japan

ISBN: (纸本)9798350396386

Nowadays, most of the current research on object detection is to improve the whole framework, in order to improve the accuracy of detection, but another problem of object detection is the detection speed. The more complex the architecture, the slower the speed. This time, we implemented a Single Shot Multibox Detector(SSD) using GPU with *** have improved the object detection speed of SSD, which is one of the most regularly used object detection frameworks. The most time-consuming part, the VGG16 network, is rephrased by using cuDNN, which is made faster by about 9%. The second time-consuming part is post-processing, where non-maximum-suppression (NMS) is performed. We accelerated NMS by implementing our new algorithms that are suitable for GPUs, which is about 52% faster than the original PyTorch version [11]. We also ported those parts that were originally executed on the CPU to the GPU. In total, our GPU-accelerated SSD can detect objects 22.5% faster than the original version. We demonstrate that using GPUs to accelerate existing frameworks is a viable approach.

关键词： Graphics processing units Object detection Detectors Computer architecture parallel algorithms Artificial intelligence Software engineering

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Modeling and topology analysis of S-SCDN supply area based on graph database

Modeling and topology analysis of S-SCDN supply area based o...

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2021 international conference on Power System Technology, POWERCON 2021

作者： Tian, Yanning Zhang, Jianmin Shen, Qiang Li, Gang Chen, Lei Yu, Huanlei Li, Ting Hangzhou Dianzi University School of Automation Hangzhou China State Grid Shaoxing Electric Supply Company Shaoxing China

ISBN: (纸本)9781665407373

In order to realize the mutual promotion between the energy management system and the traditional monitoring system under the development of active distribution network and ensure the high flexibility and reliability of urban distribution network under normal operation and fault operation, active distribution network needs new grid graph model, algorithm and parallel software computing platform. This paper proposes a modeling method of 10kV distribution network based on the combination of graph database and S-SCDN concept. Firstly, the PMIS data of the actual power grid are processed and converted into S-SCDN data. Then, the S-SCDN data are modeled in a reasonable graph database, and a generic switching station model is designed to complete the dynamic topology analysis of the S-SCDN supply area. The S-SCDN modeling based on graph database is compared with relational database modeling of traditional distribution network through case simulation data import, and the superiority of the model is verified. © 2021 IEEE.

关键词： Graph Databases

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Smart distributed DataSets for Stream Processing 27th

Smart Distributed DataSets for Stream Processing

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27th international European conference on parallel and distributed computing (Euro-Par)

作者： Lopes, Tiago Coimbra, Miguel Veiga, Luis INESC ID Lisboa Lisbon Portugal Univ Lisbon Inst Super Tecn Lisbon Portugal

ISBN: (纸本)9783030856656;9783030856649

There is an ever-increasing amount of devices getting connected to the internet, and so is the volume of data that needs to be processed - the Internet-of-Things (IoT) is a good example of this. Stream processing was created for the sole purpose of dealing with high volumes of data, and it has proven itself time and time again as a successful approach. However, there is still a necessity to further improve scalability and performance on this type of system. This work presents SDD4STREAMING, a solution aimed at solving these specific issues of stream processing engines. Although current engines already implement scalability solutions, time has shown those are not enough and that further improvements are needed. SDD4STREAMING employs an extension of a system to improve resource usage, so that applications use the resources they need to process data in a timely manner, thus increasing performance and helping other applications that are running in parallel in the same system.

关键词： Engines

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Adaptive Cooperative distributed Compressed Sensing for Edge Devices: A Multiagent Deep Reinforcement Learning Approach

Adaptive Cooperative Distributed Compressed Sensing for Edge...

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19th IEEE international conference on Pervasive computing and Communications (IEEE PerCom)

作者： Sekine, Masatoshi Ikada, Satoshi Oki Elect Ind Co Ltd Innovat Promot Ctr Saitama Japan

ISBN: (纸本)9781665404242

We propose a lightweight and adaptive distributed compressed sensing (DCS) with multi-sensor collaboration based on multiagent deep reinforcement learning (LADICS-MARL). To efficiently acquire data generated by sensor nodes deployed over a wide area and for long periods, we previously proposed a lightweight and adaptive compressed sensing method based on deep learning for edge devices, called LACSLE that changes the compression ratio in real-time according to the data between one sender and one receiver using pre-trained deep learning model. LADICS-MARL is an extension for multiple senders and one receiver and supports DCS through which multiple compressed data are simultaneously reconstructed. Multiple sensor nodes cooperate based on multiagent reinforcement learning to estimate the optimal compression ratio for all senders according to each corresponding data, as well as the transmitted compressed data from other sensor nodes. In addition, a gateway optimizes the combination of groups where some compressed data are reconstructed simultaneously. A performance evaluation using acceleration data from multiple sensor terminals acquired on a bridge suggests that the multiagent-based LADICS-MARL can reconstruct original data from less compressed data compared to the single-agent-based LACSLE under the threshold of reconstruct error.

关键词： distributed compressed sensing multiagent deep reinforcement learning sensor data edge device

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A distributed Architecture and Virtual Synchronous Machine Control of Onboard Charger for the Electric Vehicle

A Distributed Architecture and Virtual Synchronous Machine C...

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international conference on Power Electronics Systems and Applications (ICPESA)

作者： Yonghui Liu Jiapeng Li Zhao Xu Hongxin Li Lijuan Fan Zhuo Cheng Department of Electrical Engineering The Hong Kong Polytechnic University Hong Kong China Department of Electrical Engineering Xi'an Jiaotong University Xi'an China Department of Electrical Engineering The Hong Kong Polytechnic University Hong Kong China Southern Power Grid Guangzhou China

ISBN: (纸本)9781665486699

Electric vehicles (EVs) are thriving to alleviate environmental issues. Conventional two-stage onboard charger (OBC) in EV only contains one large-power DC/DC converter to connect the whole battery pack to the inverter. It requires dozens of battery cells to connect in parallel and then in series for charging. parallel connection causes circulating current among batteries, increasing the loss and safety risk and decreasing the battery life. Aimed at diminishing the circulating current by reducing parallel connections of battery cells, a distributed OBC architecture is proposed in this paper. It contains a bi-directional inverter and numerous paralleled bi-directional low-power DC/DC converters. The batteries are divided into multiple clusters with less paralleled cells to interface with those DC/DC converters, respectively. Furthermore, a novel virtual synchronous machine (VSM) control is proposed for the distributed OBC, enabling the OBC to provide inertia and frequency regulation to the grid and to serve as an emergency power supply in island mode. Compared to the conventional OBC, the distributed OBC under the proposed VSM control achieves higher fault tolerance, better power allocation, less circulating current among batteries, and less current impact on the batteries. Those priorities are finally verified by simulation results.

关键词： Fault tolerance Simulation Fault tolerant systems Electric vehicles Inverters Batteries Resource management

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Efficient and Systematic Partitioning of Large and Deep Neural Networks for parallelization 27th

Efficient and Systematic Partitioning of Large and Deep Neur...

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27th international European conference on parallel and distributed computing (Euro-Par)

作者： Wang, Haoran Li, Chong Tachon, Thibaut Wang, Hongxing Yang, Sheng Limet, Sebastien Robert, Sophie Huawei Technol Paris France Univ Orleans Orleans France

ISBN: (纸本)9783030856656;9783030856649

Deep neural networks (DNNs) are playing an increasingly important role in our daily life. Since the size of DNNs is continuously growing up, it is highly important to train them effectively by distributing computation on multiple connected devices. The efficiency of training depends on the quality of chosen parallelization strategy. Being able to find a good parallelization strategy for a DNN in a reasonable amount of time is not trivial. Previous research demonstrated the possibility to systematically generate good parallelization strategies. However, systematic partitioning still suffers from either a heavy preprocessing or poor quality of parallelization. In this paper, we take a purely symbolic analysis approach by leveraging the features of DNNs like dense tensor balanced computation. We propose the Flex-Edge Recursive Graph and the Double Recursive Algorithm, successfully limiting our parallelization strategy generation to a linear complexity with a good quality of parallelization strategy. The experiments show that our solution significantly reduces the parallelization strategy generation time from hours to seconds while maintaining the parallelization quality.

关键词： distributed algorithm distributed machine learning Neural network partitioning

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Security and Performance Evaluation in Peer- To-Peer VoIP Communication

Security and Performance Evaluation in Peer- To-Peer VoIP Co...

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international Black Sea conference on Communications and Networking (BlackSeaCom)

作者： Gani SANLIOZ Mustafa Kara Muhammed Ali Aydin Department of Computer Engineering Istanbul University-Cerrahpasa Istanbul Turkey Department of Computer Engineering Air Force Academy National Defence University Istanbul Turkey

ISBN: (数字)9798350351859

ISBN: (纸本)9798350351866

The impact of virtual collaborations and digital transformation has led to a significant increase in the use of V oIP technologies. VoIP, which works on a hierarchical client-server architecture in order to adapt to the old circuit-switched technology, has gradually started to use a P2P architecture over time in parallel with the developments in this architecture. The lack of “peer admission” and “control in call flow” features in pure P2P architecture has resulted in vulnerabilities against some attacks, such as modifying, termination, eavesdropping, and SPIT. Although various studies have been conducted to mitigate these vulnerabilities in P2P systems on other platforms, the literature on P2P VoIP is limited. Studies using distributed blockchains were limited to authentication and could not eliminate the system's centralization by keeping SIP servers in their architecture. In this study, we proposed a P2P VoIP system and assessed the feasibility of managing user data via distributed blockchain technology. Our findings reveals that this approach can enhance security and decentralization in P2P V oIP. Furthermore, a thorough performance and security evaluation is conducted on the blockchain type, consensus algorithm, and several blockchain architectures that will be used to manage this data, offering a promising outlook for the future of P2P VoIP.

关键词： Performance evaluation Consensus algorithm Blockchains Peer-to-peer computing Maintenance Security Servers

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AS-cast: Lock Down the Traffic of Decentralized Content Indexing at the Edge 22nd

AS-cast: Lock Down the Traffic of Decentralized Content I...

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22nd international conference on Algorithms and Architectures for parallel Processing, ICA3PP 2022

作者： Lebre, Adrien Nédelec, Brice Van Kempen, Alexandre Nantes Université École Centrale Nantes IMT Atlantique CNRS INRIA LS2N UMR 6004 Nantes44000 France Qarnot Computing 40/42 rue Barbès Montrouge92120 France

ISBN: (纸本)9783031226762

Although the holy grail to store and manipulate data in Edge infrastructures is yet to be found, state-of-the-art approaches demonstrated the relevance of replication strategies that bring content closer to consumers: The latter enjoy better response time while the volume of data passing through the network decreases overall. Unfortunately, locating the closest replica of a specific content requires indexing every live replica along with its location. Relying on remote services for such a aim enters in contradiction with the properties of Edge infrastructures as locating replicas may effectively take more time than actually downloading content. At the opposite, maintaining such an index at every node would prove overly costly in terms of memory and traffic. In this paper, we propose a decentralized implementation of content indexing called AS-cast. Using AS-cast, every node only indexes its closest replica;and all connected nodes with a similar index compose a partition. AS-cast is (i) efficient, for it uses partitions to lock down the traffic generated by its operations to relevant nodes, yet it (ii) guarantees that every node eventually acknowledges its closest replica despite concurrent operations. Our prototype, implemented on PeerSim, shows that AS-cast scales well in terms of generated messages and termination time. As such, AS-cast can constitute a novel building block for geo-distributed services in need of efficient resource sharing in the vicinity of regions. © 2023, Springer Nature Switzerland AG.

关键词： Locks (fasteners)

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