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检索条件"任意字段=23rd International Conference on Distributed Computing and Networking, ICDCN 2022"

共 140 条记录，以下是101-110 订阅

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Construction of Smart Pension System for Urban and Rural Residents Based on Big Data Mining Algorithm 2

Construction of Smart Pension System for Urban and Rural Res...

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2nd IEEE international conference on distributed computing and Electrical Circuits and Electronics, ICDCECE 2023

作者： Zhao, Jing Xi'an Fanyi University International Business School Shaanxi China

ISBN: (纸本)9798350347456

Population aging is a social problem that every country cannot avoid in the process of modernization. China has rapidly stepped into the threshold of an aging society. With the deepening of the aging of the population and with the increasing demand for diversified services for the elderly, there is an urgent need to provide multi-level, high-quality elderly care(ec) services for the elderly. There is a large gap in human resources and other resources in the traditional pension industry. The pension industry needs to develop. Zhiyi pension has opened a new realm of the pension model. It cannot only provide a variety of life assistance, Rehabilitation care, psychological comfort and social support services for the elderly through intelligent decision-making and control, but also always protect the safety of the elderly and meet the multi-level and diversified service The needs of urban and rural elderly. We should play the leading comprehensive role of innovation driving role of information technology, accelerate the development of smart health care industry, improve people's livelihood and cultivate new economic drivers, On the 23rd of 2017, the Ministry of Industry, Information Technology and Information Technology, the Ministry of Civil Affairs and the Health and Family Planning Commission jointly issued the Action Plan for the Development of Smart ec Industry (2017-2022), which explicitly mentioned the year 2022, It will "basically form a smart health care industry system covering the whole life cycle. So far, the Smart Pension (SM) industry has completed its overall layout and entered the practical level. © 2023 IEEE.

关键词： Big data

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Assembly code translation from ARM64 to RISC-V

Assembly code translation from ARM64 to RISC-V

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

作者： Tatsuya Tsuchiya Kanemitsu Ootsu Takashi Yokota Shun Kojima Graduate School of Regional Development and Creativity Utsunomiya University Utsunomiya Tochigi Japan

ISBN: (纸本)9798350396386

SIMD instruction-based processing is effective in speeding up highly data-parallel processing such as image recognition. However, processing using the SIMD instruction set requires reconstructed the machine code when changing the number of computing units. Therefore, we have developed RISC-V with vector extensions, a scalable vector extension that can change the number of concurrent parallel operations without changing the machine code, referring to the ARM SVE. There is a problem that RISC-V vectorized machine code cannot be generated due to the lack of a compiler that supports vector extensions. The solution to this problem is to use the assembly code generated by the vectorization compiler for ARM SVE to generate assembly code for RISC-V with vector extensions. By assembling it, we generate RISC-V vectorization machine code. As the first step of implementation, we implement an assembly code translator that performs assembly code translation between the basic instruction set from ARM64 to RISC-V. Since the translated RISC-V program could be executed, it was confirmed that the translation proposed in this study was performed properly.

关键词： Codes Reduced instruction set computing Image recognition Artificial intelligence Image reconstruction Software engineering

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international conference on Architectural Support for Programming Languages and Operating Systems - ASPLOS

International Conference on Architectural Support for Progra...

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23rd international conference on Architectural Support for Programming Languages and Operating Systems, ASPLOS 2018

ISBN: (纸本)9781450349116

The proceedings contain 56 papers. The topics discussed include: hardware multithreaded transactions;Slim NoC: a low-diameter on-chip network topology for high energy efficiency and scalability;skyway: connecting managed heaps in distributed big data systems;espresso: brewing Java for more non-volatility with non-volatile memory;enhancing cross-ISA DBT through automatically learned translation rules;Gloss: seamless live reconfiguration and reoptimization of stream programs;filtering translation bandwidth with virtual caching;automatic matching of legacy code to heterogeneous APIs: an idiomatic approach;understanding and auto-adjusting performance-sensitive configurations;CALOREE: learning control for predictable latency and low energy;Darwin: a genomics co-processor provides up to 15,000x acceleration on long read assembly;liquid silicon-Monona: a reconfigurable memory-oriented computing fabric with scalable multi-context support;exploiting dynamic thermal energy harvesting for reusing in smartphone with mobile applications;quantum computing is getting real: architecture, PL, and OS roles in closing the gap between quantum algorithms and machines;DAMN: overhead-free IOMMU protection for networking;statistical reconstruction of class hierarchies in binaries;FCatch: automatically detecting time-of-fault bugs in cloud systems;and LTRF: enabling high-capacity register files for GPUs via hardware/software cooperative register prefetching.

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MESHMd1- a middleware for self-organization in ad hoc networks

MESHMd1- a middleware for self-organization in ad hoc networ...

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international conference on distributed computing Systems Workshop

作者： K. Herrmann Institute for Telecommunication Systems-iVS Berlin University of Technology Berlin Germany

The maturing domain of ad hoc networking still holds some major challenges on its way to commercial exploitation. The dynamic and decentralized nature of ad hoc networks calls for new paradigms in the design of distributed applications. Most notably, there is a need for self-organization mechanisms in the middleware layer to enable medium-scale and large-scale distributed applications. In this paper we present NESHMd1, a middleware platform for ad hoc networks that is built on mobile agents and tuple space technology. MESHMd1 provides basic building blocks for the implementation of self-organizing distributed applications for ad hoc networks. Unlike other middleware platforms, MESHMd1 puts a high emphasis on mobility awareness, decoupling and bio-inspired coordination primitives. We discuss the aspects of ad hoc networking and self-organization and derive the basic architecture of MESHMd1 before we present our prototype implementation.

关键词： Middleware Intelligent networks Ad hoc networks Mobile computing Computer network reliability Mobile communication Network servers Centralized control Large-scale systems Mobile agents

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Analyzing I/O Performance of a Hierarchical HPC Storage System for distributed Deep Learning 23rd

Analyzing I/O Performance of a Hierarchical HPC Storage Sy...

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23rd international conference on Parallel and distributed computing, Applications, and Technologies, PDCAT 2022

作者： Fukai, Takaaki Sato, Kento Hirofuchi, Takahiro Tokyo Japan RIKEN Center for Computational Science Kobe Japan

ISBN: (纸本)9783031299261

Deep learning is a vital technology in our lives today. Both the size of training datasets and neural networks are growing to tackle more challenging problems with deep learning. distributed deep neural network (DDNN) training technique is necessary to train a model with large datasets and networks. For large-scale DDNN training, HPC clusters are excellent computation environments. I/O performance is critical in large-scale DDNN on HPC clusters because it is becoming a bottleneck. Most flagship-class HPC clusters have hierarchical storage systems. It is necessary to quantify the performance improvement effect of the hierarchical storage system on the workloads to design future HPC storage systems. This study demonstrates the quantitative performance analysis of the hierarchical storage system for DDNN workload in a flagship-class supercomputer. Our analysis shows how much performance improvement and storage volume increment will be required to achieve the performance goal. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Deep neural networks

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The Impact of Importance-Aware Dataset Partitioning on Data-Parallel Training of Deep Neural Networks 23rd

The Impact of Importance-Aware Dataset Partitioning on Data-...

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23rd IFIP WG 6.1 international conference on distributed Applications and Interoperable Systems (DAIS) Held as Part of the 18th international Federated conference on distributed computing Techniques (DisCoTec)

作者： Sheikholeslami, Sina Payberah, Amir H. Wang, Tianze Dowling, Jim Vlassov, Vladimir KTH Royal Inst Technol Stockholm Sweden Hopsworks AB Stockholm Sweden

ISBN: (纸本)9783031352591;9783031352607

Deep neural networks used for computer vision tasks are typically trained on datasets consisting of thousands of images, called examples. Recent studies have shown that examples in a dataset are not of equal importance for model training and can be categorized based on quantifiable measures reflecting a notion of "hardness" or "importance". In this work, we conduct an empirical study of the impact of importance-aware partitioning of the dataset examples across workers on the performance of data-parallel training of deep neural networks. Our experiments with CIFAR-10 and CIFAR-100 image datasets show that data-parallel training with importance-aware partitioning can perform better than vanilla data-parallel training, which is oblivious to the importance of examples. More specifically, the proper choice of the importance measure, partitioning heuristic, and the number of intervals for dataset repartitioning can improve the best accuracy of the model trained for a fixed number of epochs. We conclude that the parameters related to importance-aware data-parallel training, including the importance measure, number of warmup training epochs, and others defined in the paper, may be considered as hyperparameters of data-parallel model training.

关键词： Data-parallel training Example importance distributed deep learning

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OLAP performance of distributed PostgreSQL and MongoDB on OpenStack. Preliminary Results on Smaller Scale Factors 23

OLAP performance of distributed PostgreSQL and MongoDB on Op...

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23rd RoEduNet conference: networking in Education and Research, RoEduNet 2024

作者： Fotache, Marin Badea, Catalina Cluci, Marius-Iulian Pinzaru, Ciprian Esanu, Codrin-Stefan Rusu, Octavian Alexandru Ioan Cuza University of Iasi Dept.of Accounting Information Systems and Statistics Iasi Romania Alexandru Ioan Cuza University of Iasi Iasi Romania Alexandru Ioan Cuza University of Iasi S.C. HVM SRL Iasi Romania Alexandru Ioan Cuza University of Iasi Faculty of Physics Iasi Romania

ISBN: (纸本)9798331540388

Traditional relational or SQL database servers and document/JSON database servers are part of state-of-The-Art data architectures. Comparing the database query (OLAP) performance of relational and document data stores is challenging because of myriads of options in data modeling, query features, data distribution, processing distribution, etc. Based on the TPC-H benchmark tools, this paper presents the initial findings of converting the relational TPC-H schema deployed in a PostgreSQL/Citus cluster into a denormalized JSON schema deployed in a MongoDB cluster and subsequently mapping a 296-query set from SQL to MongoDB Aggregation Framework. The success of each query execution within a 10-minute timeout was collected for both PostgreSQL and MongoDB in six scenarios defined by two small-scale factors (0.01 and 0.1 GB) and three values of nodes (3,6 and 9) for data distribution and processing. Results show that the database server is associated with the success of query execution. © 2024 IEEE.

关键词： Structured Query Language

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Service Migration Algorithm for distributed Edge computing in 5G/6G Networks 23rd

Service Migration Algorithm for Distributed Edge Computing i...

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23rd international conference on Next Generation Wired/Wireless Networks and Systems (NEW2AN) / 16th conference on Internet of Things and Smart Spaces (RuSMART)

作者： Kuznetsov, Konstantin Kuzmina, Ekaterina Lapteva, Tatiana Volkov, Artem Muthanna, Ammar Aziz, Ahmed St Petersburg State Univ Telecommun Dept Telecommun Networks & Data Transmiss St Petersburg Russia

ISBN: (纸本)9783031609930;9783031609947

In recent years, the volumes of information processed and transmitted over the network are rapidly increasing, while the demand for speed and quality of transmitted data is also increasing. As a result, there is a need to use edge computing, which allows reducing network response time and more efficiently using bandwidth, as well as significantly improving the performance of the data transmission system. Article describes the application of edge computing in the form of transferring processes to an edge computing cluster, and presents the results of research on existing migration processes, full transfer time and downtime, identifying the optimal migration process for further implementation of the service migration automation algorithm. The following empirical research methods were used in the work: comparative evaluation method and experiment. The work considers the transfer of computational processes to an edge computing cluster;a comparative analysis of methods for automated service migration is carried out;an optimal migration strategy is identified;an algorithm for automating service migration is developed. Solutions related to automation of migration are excellent for use in the field of Internet of Things. They contribute to improving application performance and reducing response time. Automatic service migration allows increasing process efficiency and reducing system maintenance costs."

关键词： kubernetes K8s virtualization service migration edge computing

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Simulated performance evaluation of a hybrid MAC-layer protocol for dependable multi-channel distributed systems with hard real-time requirements

Simulated performance evaluation of a hybrid MAC-layer proto...

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international conference on distributed computing Systems Workshop

作者： S.A. Koubias Department of Electrical & Computer Engineering University of Patras Patras Greece

In this paper an efficient hybrid MAC-layer protocol for dependable multi-channel networking structures is presented and evaluated. This new protocol handles C sub-channels totally, that is c physically or frequency divided sub-channels for the information or data packets and one (1) request sub-channel, where the request packets are transmitted and (C=c+1). The proposed mechanism for the accessing of the request sub-channel slots is a TDMA-like scheme. An effective reservation mechanism based on the transmitted requests is used for the deterministic accessing of the slots of the information sub-channels, mainly at high loads. Additionally, the random access ALOHA-like protocol is selected for the dynamic accessing of the Free Information Slots (FISs) for low traffic conditions, when the stations transmit no requests. This combination offers bounded packet delay and high throughput-mean delay performance under mixed (asynchronous/synchronous) traffic. On the other hand, the proposed multi-channel networking structure offers high operational reliability due to the existed channel redundancy.

关键词： Real time systems Access protocols Delay distributed computing Communication system traffic control Availability Media Access Protocol Computational modeling Computer networks Frequency conversion

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Optimizing the Parallelism of Communication and Computation in distributed Training Platform 23rd

Optimizing the Parallelism of Communication and Computation ...

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23rd international conference on Algorithms and Architectures for Parallel Processing (ICA3PP)

作者： Hou, Xiang Yuan, Yuan Ma, Sheng Xu, Rui Wang, Bo Li, Tiejun Jiang, Wei Wu, Lizhou Zhang, Jianmin Natl Univ Def Technol Changsha Peoples R China

ISBN: (纸本)9789819708338;9789819708345

With the development of deep learning, DNN models have become more complex. Large-scale model parameters enhance the level of AI by improving the accuracy of DNN models. However, they also present more severe challenges to the hardware training platform for training a large model needs a lot of computing and memory resources, which can easily exceed the capacity of an accelerator. In addition, with the increasing demand for the accuracy of DNN models in academia and industry, the number of training iterations is also skyrocketing. In these backgrounds, more accelerators are integrated on a hierarchical platform to conduct distributed training. In distributed training platforms, the computation of the DNN model and the communication of the intermediate parameters are handled by different hardware modules, so their degree of parallelism profoundly affects the training speed. In this work, based on the widely used hierarchical Torus-Ring training platform and the Ring All-Reduce collective communication algorithm, we improve the speed of distributed training by optimizing the parallelism of communication and computation. Specifically, based on the analysis of the distributed training process, we schedule the computation and communication so that they execute simultaneously as much as possible. Finally, for data parallelism and model parallelism, we reduce the communication exposure time and the computation exposure time, respectively. Compared with the previous work, the training speed (including 5 training iterations) of the Resnet50 model and the Transformer model is increased by 23.77%-25.64% and 11.66%-12.83%.

关键词： distributed training simulator Collective communication operation Communication exposure time Computation exposure time

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