Companies today demand even more high quality services and infrastructure in the field of Industrial Internet IOT. There are several problems facing smart urbanization. Of special concern are secured energy demand sid...
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Companies today demand even more high quality services and infrastructure in the field of Industrial Internet IOT. There are several problems facing smart urbanization. Of special concern are secured energy demand side management. The IIoT makes ransomware, cyber attacks and other security threats open to industrial systems. IIoT will provide meaningful solutions to these problems through the amalgamation of big data analytics. That first author recommends a safe, reliable Demand Side Management engine for a smart cooperative client using IIoT based Big Data Analysis. A highly distributed framework to the theoretical engine for optimizing DSM across a Home Area Network. A payload authentication framework based on a lightweight handshake mechanism is used to improve the protection of this engine. In order to make users to control different server native resources energy efficiently, our theoretical method uses the portable utilizes of the Constrained Application Protocol. Moreover, data streams with parallel MapReduce processing are managed using big data analysis. In addition, we use Apache Spark with Apache Hadoop to check the theoretical engine to test our input transform match on stable datafile. The test results indicate that the architecture outlined provides useful insights into the intelligent social communities of the IIoT. (c) 2021 Elsevier Ltd. All rights reserved. Selection and peer-review under responsibility of the scientific committee of the 3rdinternationalconference on Materials, Manufacturing and Modelling.
The proceedings contain 13 papers. The special focus in this conference is on Benchmarking, Measuring, and Optimizing. The topics include: Artemis: An Automatic Test Suite Generator for Large Scale OLAP Database;OStor...
ISBN:
(纸本)9783030710576
The proceedings contain 13 papers. The special focus in this conference is on Benchmarking, Measuring, and Optimizing. The topics include: Artemis: An Automatic Test Suite Generator for Large Scale OLAP Database;OStoreBench: Benchmarking distributed Object Storage Systems Using Real-World Application Scenarios;ConfAdvisor: An Automatic Configuration Tuning Framework for NoSQL Database Benchmarking with a Black-box Approach;Optimization of the Himeno Benchmark for SX-Aurora TSUBASA;parallel Sorted Sparse Approximate Inverse Preconditioning Algorithm on GPU;preface;characterizing the Sharing Behavior of applications Using Software Transactional Memory;ComScribe: Identifying Intra-node GPU Communication;a Benchmark of Ocular Disease Intelligent Recognition: One Shot for Multi-disease Detection;MAS3K: An Open Dataset for Marine Animal Segmentation;benchmarking Blockchain Interactions in Mobile Edge Cloud Software Systems.
With the rapid progress of tomography systems, a need has emerged for innovation that can provide high accuracy measurements, fast acquisition rate, and low-cost robust systems for multi-phase flow and medical instrum...
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With the rapid progress of tomography systems, a need has emerged for innovation that can provide high accuracy measurements, fast acquisition rate, and low-cost robust systems for multi-phase flow and medical instruments. The main objective of this work is to design and implement a 3D Electrical Impedance Tomography (EIT) system to effectively monitor a multi-phase flow which consists of an annular flow comprising of an air-core of unknown diameter, surrounded by a liquid phase, such as oil and water mixture of high water-cut value. The system consists of an array of electrode sensors evenly distributed around the pipe confining the three-phase flow. Subsequently, a dedicated hardware module (GPU) is used to reconstruct these signals, captured from the sensors, into a 3D image of the flow by solving the forward-inverse problem. The annular flow is generated using a flow conditioner consisting of a swirl cage followed by a bluff body that generates vortices that push the high dense liquid phase to the outer side of the pipeline. The 3D EIT system is designed to accurately measure the gas liquid fraction and the liquid-liquid fraction and provide the 3D volume image of the flow. Hence, it can be utilized for pipeline and reservoirs characterization to achieve high efficiency, cost reduction, and safer production. The approach is to design a dedicated, highly parallel hardware architecture that can sustain the high computation complexity of the 3D EIT algorithm. Coincidentally, advanced software development tools are available to help effectively develop these parallel hardware algorithms. The main novelty is utilizing GPUs to outperform the existing real-time tomography hardware platforms, mainly based on Field Programmable Gate Array (FPGAs), which have the disadvantage of being less dense and feature lower precise arithmetic operations. Matrix multiplication and matrix inversion are considered the most intensive computation in Electric Impedance Tomography (EIT). T
The Memetic Algorithm (MA), introduced by Pablo Moscato in 1989, integrates Evolutionary Algorithms with local search methods, enhancing its effectiveness in solving complex optimization problems. This paper provides ...
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ISBN:
(数字)9798350367492
ISBN:
(纸本)9798350367508
The Memetic Algorithm (MA), introduced by Pablo Moscato in 1989, integrates Evolutionary Algorithms with local search methods, enhancing its effectiveness in solving complex optimization problems. This paper provides a comprehensive survey of MA research published in 2019, reviewing 75 selected papers from an initial pool of 112 identified through Google Scholar. The selected papers were categorized into five types: optimization problems (40 papers), image processing (10 papers), parallelprocessing (5 papers), gene/DNA datasets (4 papers), and other applications (16 papers). The survey highlights MA’s versatility and effectiveness across various domains, particularly its potential for solving complex optimization problems. Key findings include the adaptability of MA for diverse applications, its ongoing relevance in addressing challenging issues, and promising opportunities for combining MA with other algorithms to enhance performance. The paper also emphasizes the significance of MA in fields such as image processing, where it improves pattern recognition and image enhancement, and in bioinformatics, where it optimizes gene selection and genetic algorithms. Despite the extensive study of MA, there remains a significant research gap in non-English literature, particularly in Bahasa, limiting accessibility for Indonesian researchers. This survey aims to bridge this gap by providing valuable insights and encouraging further exploration and application of MA to solve increasingly complex problems. It offers a comprehensive overview that underscores the importance of MA and its potential for future research and innovation.
Deep neural networks (DNNs) sustain high performance in today's data processingapplications. DNN inference is resource-intensive thus is difficult to fit into a mobile device. To solve this problem, recent advanc...
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ISBN:
(纸本)9781665449328
Deep neural networks (DNNs) sustain high performance in today's data processingapplications. DNN inference is resource-intensive thus is difficult to fit into a mobile device. To solve this problem, recent advances unleash DNN services using the edge computing paradigm. The existing approaches split a DNN into two parts and deploy the two partitions to computation nodes at two edge computing tiers. Nonetheless, these methods overlook collaborative device-edge-cloud computation resources. Besides, previous algorithms demand the whole DNN re-partitioning to adapt to computation resource changes and network dynamics. Moreover, for resource-demanding convolutional layers, prior works do not give a parallelprocessing strategy without loss of accuracy at the edge side. To tackle these issues, we propose D-3, a dynamic DNN decomposition system for synergistic inference without precision loss. The proposed system introduces a heuristic algorithm named horizontal partition algorithm to split a DNN into three parts. The algorithm partially adjust the partitions at run time according to processing time and network conditions. At the edge side, a vertical separation module separates feature maps into tiles that can be independently run on different edge nodes in parallel. Extensive quantitative evaluation of five popular DNNs illustrates that D-3 outperforms the stateof-the-art counterparts up to 3.4X in end-to-end DNN inference time and reduces backbone network communication overhead up to 3.68X.
For big data, the Evidential K-Nearest Neighbor (EK-NN) classifier is still impractical due to the restrictions of time and memory. In both the training and testing stage, searching for K closest neighbors requires in...
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ISBN:
(纸本)9783031178016;9783031178009
For big data, the Evidential K-Nearest Neighbor (EK-NN) classifier is still impractical due to the restrictions of time and memory. In both the training and testing stage, searching for K closest neighbors requires intensive quadratic computation and has to be repeated for each input sample. To address this issue, we propose a distributed EK-NN classifier, named Global Exact EK-NN, for fast processing with Apache Spark. We compare the proposed classifier, which can be scaled to 48 nodes (2688 cores) at a cluster named the Texas Advanced Computing Center Frontera, with several other parallel K-NN based algorithms over 4 large datasets. Our method is able to achieve state-of-the-art scaling efficiency and accuracy on the large datasets having more than 10 million samples.
Top-k dominance (TKD) query for incomplete datasets is a popular preference query for incomplete data, which analyzes the dominance relationships among objects in a dataset by a dominance method to reveal the top-k mo...
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ISBN:
(纸本)9783031112171;9783031112164
Top-k dominance (TKD) query for incomplete datasets is a popular preference query for incomplete data, which analyzes the dominance relationships among objects in a dataset by a dominance method to reveal the top-k most valuable information in the dataset. At present, in-depth research has been conducted on this topic, and efficient query algorithms based on various pruning strategies have been proposed, as well as optimization algorithms based on a distributed computing framework for processing large-scale datasets. With the advent of the information age, data update iterations are accelerated, and in the face of dynamically updated data, the traditional TKD query algorithm based on static data can no longer meet our needs, and an efficient algorithm based on the dynamically updated data set environment is needed. In this paper, we conduct an in-depth study on the TKD query problem for dynamically updated incomplete datasets, and propose a dynamic update parallel algorithm based on MapReduce framework. The algorithm utilizes the query results of historical datasets, avoids the repeated analysis of the dominant relationships between historical objects, optimizes the computation process, reduces the space occupation, and proves through experiments that the dynamic update algorithm has more obvious advantages compared with the traditional algorithm.
Based on big data analysis technology, Hadoop and Spark big data parallel computing framework and HDFS distributed file storage database, we can complete the construction of political science research application plat...
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ISBN:
(纸本)9781665416672
Based on big data analysis technology, Hadoop and Spark big data parallel computing framework and HDFS distributed file storage database, we can complete the construction of political science research application platform. The political science research application platform pays attention to data capture, data cleaning, analysis and mining, visual display and other operations on the public comments and related responses of the masses under the government open platform and the opinions and contents published by netizens under the media social platform, which are involved in public opinion surveys under the current network environment, so as to complete the description and analysis of political phenomena with the help of the unique advantages of big data technology, and realize the innovation of political science research methods and the expansion of research fields. At the same time, it also makes an innovative attempt for the scientific and standardized research of political science.
The introduction of stdpar in C++ introduces parallel algorithms into the standard library, which allows developers to easily harness the power of parallelism in their applications, leading to potential performance im...
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ISBN:
(数字)9798350369199
ISBN:
(纸本)9798350369205
The introduction of stdpar in C++ introduces parallel algorithms into the standard library, which allows developers to easily harness the power of parallelism in their applications, leading to potential performance improvements, scalability, increased productivity, and improved reproducibility in scientific computing tasks. At least theoretically, as its performance depends on the compiler in use. In this paper, we examine the usability of stdpar and demonstrate the significant differences between different stdpar compilers. To this end, we study a kernel matrix assembly algorithm and a BLAS level 3 SYMM function, both implemented in the parallel Least Squares Support Vector Machine (PLSSVM) library. The same code is compiled with an NVIDIA nvc++, gcc, Intel oneAPI icpx, and AdaptiveCpp compiler. We analyzed the code on an NVIDIA A30 GPU, an AMD MI210 GPU, and a dual-socket AMD EPYC 9274F CPU machine. First, we report surprisingly large runtime differences between the different stdpar compilers: up to 11 percent on the NVIDIA A30 GPU and even up to 335% on two AMD EPYC 9274F. Second, we show how stdpar enables energy reduction by a factor of up to 20.7 with the very same implementation by utilizing different hardware *** code, utility scripts, and documentation are all available on GitHub: https://***/SC-SGS/PLSSVM
In Computer Vision, open programming standards sod, as OpenVX have emerged to bring together portability and acceleration across devices. Unfortunately, achieving both goals on UPGAs remains a challenge because FPGAs ...
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ISBN:
(纸本)9781665414555
In Computer Vision, open programming standards sod, as OpenVX have emerged to bring together portability and acceleration across devices. Unfortunately, achieving both goals on UPGAs remains a challenge because FPGAs still require to adapt the code with proprietary extensions. Exclusively for Xilinx devices, the HiF1ip X open source library partially solves this problem by offering a clean C++ OpenVX API that offers the performance of proprietary extensions without exposing its complexity to programmer. While HiFlipVX enables portability within Xilins devices, portability between FPGA manufacturers remains an open challenge. This work extends the HiFlipVX's capabilities with a twofold goal: i) to support Intel FPGA devices with different memory conhgurations. and ii) to enable execution on FPGAs as discrete accelerators. To accomplish these goals, the proposed implementation combines two HIS programming models: C++, using Inters system of tasks that enables to coalesce nodes and reduce control overhead, and OpenCL, which provides efficient compute kernel nodes. On Intel FPGAs. compared with pure OpenCL implementations, the proposed implementation reduces kernel dispatch resources, saving up to 24% of ALUT resources for each kernel in a graph, and improves performance. Gains are 2.6x on overage for representative applications, such as Canny edge detector, or Census transform, compared with state-of-the-art frameworks.
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