scholarly journals OPTIMIZATION OF COMPUTING PROCESS OF THE ONBOARD COMPUTING SYSTEMS

2018 ◽  
Vol 22 (2) ◽  
pp. 6-17
Author(s):  
S. V. Nazarov
Keyword(s):  
Parallel Computing ◽  
High Efficiency ◽  
Computing System ◽  
Computing Systems ◽  
Parallel Calculations ◽  
Mobile Objects ◽  
Computing Process ◽  
Special Software ◽  
Long Time ◽  
Onboard Equipment

Relevance of scope of parallel calculations was realized for a long time at the solution of complex scientific and technical challenges, as in connection with low reliability and productivity of computers, and in connection with emergence of the multiprocessor systems and multinuclear processors. The technology of ensuring reliability and high efficiency on the basis of parallel calculations naturally became prevailing in the onboard computing systems (OCS). Now such systems find broad application in aircraft and space equipment, and also in land and water mobile objects. Efficiency of performance of objectives, safety, operational suitability and some other important qualities of mobile objects considerably are defined by ability of the onboard computing system to carry out the functions. Development of the onboard equipment is characterized by constant increase in number of the solved tasks and increase of their complexity, expansion of intellectual and adaptive opportunities. It inevitably leads to complication of BVS, its operating system and the special software. For the period of the solution of the majority of the tasks assigned to BVS rigid temporary restrictions are imposed. Performance of these of the requirement results in need of the organization of parallel computing processes. In this work set of mathematical models, formulations of the tasks and approaches to their decision allowing to construct the schedule of parallel computing process for realization of the information and connected tasks on the multiprocessor onboard computing systems is presented. Models of sets of the solved tasks in the form of the loaded count and in a yarusno-parallel form, the solution of tasks on purposes of the solved tasks to processors and algorithm of drawing up the schedule of parallel computing process are given.

Efficient Parallel Computer Simulation of the Motion Behaviors of Closed-Loop Multibody Systems

2007 ◽  
Author(s):  
Shanzhong Duan ◽  
Andrew Ries
Keyword(s):  
Parallel Computing ◽  
Multibody Systems ◽  
Closed Loop ◽  
High Efficiency ◽  
Equations Of Motion ◽  
Parallel Computer ◽  
Constraint Forces ◽  
Computing Systems ◽  
Closed Loops

This paper presents an efficient parallelizable algorithm for the computer-aided simulation and numerical analysis of motion behaviors of multibody systems with closed-loops. The method is based on cutting certain user-defined system interbody joints so that a system of independent multibody subchains is formed. These subchains interact with one another through associated unknown constraint forces fc at the cut joints. The increased parallelism is obtainable through cutting joints and the explicit determination of associated constraint forces combined with a sequential O(n) method. Consequently, the sequential O(n) procedure is carried out within each subchain to form and solve the equations of motion while parallel strategies are performed between the subchains to form and solve constraint equations concurrently. For multibody systems with closed-loops, joint separations play both a role of creation of parallelism for computing load distribution and a role of opening a closed-loop for use of the O(n) algorithm. Joint separation strategies provide the flexibility for use of the algorithm so that it can easily accommodate the available number of processors while maintaining high efficiency. The algorithm gives the best performance for the application scenarios for n>>1 and n>>m, where n and m are number of degree of freedom and number of constraints of a multibody system with closed-loops respectively. The algorithm can be applied to both distributed-memory parallel computing systems and shared-memory parallel computing systems.

On the topological compatibility of parallel tasks and computing systems

2021 ◽  
Author(s):  
A. F. Zadorozhny ◽  
V. A. Melent’ev
Keyword(s):  
Parallel Computing ◽  
Comparative Analysis ◽  
Computing System ◽  
Topological Model ◽  
Present Contribution ◽  
Computing Systems ◽  
Parallel Tasks ◽  
Different Types ◽  
Types Of Information

The aspects of topological compatibility of parallel computing systems and tasks are investigated in the present contribution. Based on the original topological model of parallel computations and on the unconventional graph description by its projections, the introduction of appropriate indexes is proposed and elucidated. On the example of hypercubic computing system (CS) and tasks with ring and star information topologies, we demonstrate the determination of indexes and their use in a comparative analysis of the applicability of interconnect with a given topology to solve the tasks with the same and different types of information topologies.

Parallel Computing for Mining Association Rules in Distributed P2P Networks

2011 ◽  
pp. 47-62
Author(s):  
Huiwei Guan
Keyword(s):  
Data Mining ◽  
Parallel Computing ◽  
Distributed Computing ◽  
Parallel Algorithm ◽  
Association Rules ◽  
Distributed Databases ◽  
Computing System ◽  
Computing Systems ◽  
P2p Computing ◽  
P2p Systems

Distributed computing and Peer-to-Peer (P2P) systems have emerged as an active research field that combines techniques which cover networks, distributed computing, distributed database, and the various distributed applications. Distributed Computing and P2P systems realize information systems that scale to voluminous information on very large numbers of participating nodes. Data mining on large distributed databases is a very important research area. Recently, most work for mining association rules focused on a single machine or client-server network model. However, this traditional approach does not satisfy the requirements from the large distributed databases and applications in a P2P computing system. Two important challenges are raised, one is how to implement data mining for large distributed databases in P2P computing systems, and the other is how to develop parallel data mining algorithms and tools for the distributed P2P computing systems to improve the efficiency. In this chapter, a parallel association rule mining approach in a P2P computing system is designed and implemented, which satisfies the distribution of the P2P computing system well and makes parallel computing become true. The performance and comparison of the parallel algorithm with the sequential algorithm is analyzed and evaluated, which presents the parallel algorithm features consistent implementation, higher performance, and fine scalable ability.

Parallel Computing for Mining Association Rules in Distributed P2P Networks

Data Mining ◽  
2013 ◽  
pp. 107-124
Author(s):  
Huiwei Guan
Keyword(s):  
Data Mining ◽  
Parallel Computing ◽  
Distributed Computing ◽  
Parallel Algorithm ◽  
Association Rules ◽  
Distributed Databases ◽  
Computing System ◽  
Computing Systems ◽  
P2p Computing ◽  
P2p Systems

Distributed computing and Peer-to-Peer (P2P) systems have emerged as an active research field that combines techniques which cover networks, distributed computing, distributed database, and the various distributed applications. Distributed Computing and P2P systems realize information systems that scale to voluminous information on very large numbers of participating nodes. Data mining on large distributed databases is a very important research area. Recently, most work for mining association rules focused on a single machine or client-server network model. However, this traditional approach does not satisfy the requirements from the large distributed databases and applications in a P2P computing system. Two important challenges are raised, one is how to implement data mining for large distributed databases in P2P computing systems, and the other is how to develop parallel data mining algorithms and tools for the distributed P2P computing systems to improve the efficiency. In this chapter, a parallel association rule mining approach in a P2P computing system is designed and implemented, which satisfies the distribution of the P2P computing system well and makes parallel computing become true. The performance and comparison of the parallel algorithm with the sequential algorithm is analyzed and evaluated, which presents the parallel algorithm features consistent implementation, higher performance, and fine scalable ability.

scholarly journals Dynamic memristor-based reservoir computing for high-efficiency temporal signal processing

2021 ◽  
Vol 12 (1) ◽  
Author(s):  
Yanan Zhong ◽  
Jianshi Tang ◽  
Xinyi Li ◽  
Bin Gao ◽  
He Qian ◽  
...  
Keyword(s):  
High Efficiency ◽  
Time Series Prediction ◽  
Computing System ◽  
Critical Parameters ◽  
Reservoir Computing ◽  
Hénon Map ◽  
Computing Systems ◽  
Henon Map ◽  
The Road ◽  
Training Cost

AbstractReservoir computing is a highly efficient network for processing temporal signals due to its low training cost compared to standard recurrent neural networks, and generating rich reservoir states is critical in the hardware implementation. In this work, we report a parallel dynamic memristor-based reservoir computing system by applying a controllable mask process, in which the critical parameters, including state richness, feedback strength and input scaling, can be tuned by changing the mask length and the range of input signal. Our system achieves a low word error rate of 0.4% in the spoken-digit recognition and low normalized root mean square error of 0.046 in the time-series prediction of the Hénon map, which outperforms most existing hardware-based reservoir computing systems and also software-based one in the Hénon map prediction task. Our work could pave the road towards high-efficiency memristor-based reservoir computing systems to handle more complex temporal tasks in the future.

scholarly journals Novel semi-analytical optoelectronic modeling based on homogenization theory for realistic plasmonic polymer solar cells

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Zahra Arefinia ◽  
Dip Prakash Samajdar
Keyword(s):  
Solar Cells ◽  
Active Layer ◽  
Analytical Modeling ◽  
High Efficiency ◽  
Polymer Solar Cells ◽  
Scattered Light ◽  
Homogenization Theory ◽  
Computational Time ◽  
Coupled Equations ◽  
Long Time

AbstractNumerical-based simulations of plasmonic polymer solar cells (PSCs) incorporating a disordered array of non-uniform sized plasmonic nanoparticles (NPs) impose a prohibitively long-time and complex computational demand. To surmount this limitation, we present a novel semi-analytical modeling, which dramatically reduces computational time and resource consumption and yet is acceptably accurate. For this purpose, the optical modeling of active layer-incorporated plasmonic metal NPs, which is described by a homogenization theory based on a modified Maxwell–Garnett-Mie theory, is inputted in the electrical modeling based on the coupled equations of Poisson, continuity, and drift–diffusion. Besides, our modeling considers the effects of absorption in the non-active layers, interference induced by electrodes, and scattered light escaping from the PSC. The modeling results satisfactorily reproduce a series of experimental data for photovoltaic parameters of plasmonic PSCs, demonstrating the validity of our modeling approach. According to this, we implement the semi-analytical modeling to propose a new high-efficiency plasmonic PSC based on the PM6:Y6 PSC, having the highest reported power conversion efficiency (PCE) to date. The results show that the incorporation of plasmonic NPs into PM6:Y6 active layer leads to the PCE over 18%.

scholarly journals On Performance of Sparse Fast Fourier Transform Algorithms Using the Aliasing Filter

Electronics ◽  
2021 ◽  
Vol 10 (9) ◽  
pp. 1117
Author(s):  
Bin Li ◽  
Zhikang Jiang ◽  
Jie Chen
Keyword(s):  
Fourier Transform ◽  
Fast Fourier Transform ◽  
High Efficiency ◽  
Theory And Practice ◽  
Long Time ◽  
And Performance ◽  
Binary Tree Search ◽  
The One ◽  
Sparse Fast Fourier Transform ◽  
L1 And L2

Computing the sparse fast Fourier transform (sFFT) has emerged as a critical topic for a long time because of its high efficiency and wide practicability. More than twenty different sFFT algorithms compute discrete Fourier transform (DFT) by their unique methods so far. In order to use them properly, the urgent topic of great concern is how to analyze and evaluate the performance of these algorithms in theory and practice. This paper mainly discusses the technology and performance of sFFT algorithms using the aliasing filter. In the first part, the paper introduces the three frameworks: the one-shot framework based on the compressed sensing (CS) solver, the peeling framework based on the bipartite graph and the iterative framework based on the binary tree search. Then, we obtain the conclusion of the performance of six corresponding algorithms: the sFFT-DT1.0, sFFT-DT2.0, sFFT-DT3.0, FFAST, R-FFAST, and DSFFT algorithms in theory. In the second part, we make two categories of experiments for computing the signals of different SNRs, different lengths, and different sparsities by a standard testing platform and record the run time, the percentage of the signal sampled, and the L0, L1, and L2 errors both in the exactly sparse case and the general sparse case. The results of these performance analyses are our guide to optimize these algorithms and use them selectively.

scholarly journals Evaluating the E-Health Cloud Computing Systems Adoption in Taiwan’s Healthcare Industry

Life ◽  
2021 ◽  
Vol 11 (4) ◽  
pp. 310
Author(s):  
Shih-Chia Chang ◽  
Ming-Tsang Lu ◽  
Tzu-Hui Pan ◽  
Chiao-Shan Chen
Keyword(s):  
Decision Making ◽  
Cloud Computing ◽  
Multiple Criteria Decision Making ◽  
Cloud Service ◽  
Computing System ◽  
Healthcare Industry ◽  
Computing Systems ◽  
Factors Affecting ◽  
Cloud Computing System ◽  
Health Cloud

Although the electronic health (e-health) cloud computing system is a promising innovation, its adoption in the healthcare industry has been slow. This study investigated the adoption of e-health cloud computing systems in the healthcare industry and considered security functions, management, cloud service delivery, and cloud software for e-health cloud computing systems. Although numerous studies have determined factors affecting e-health cloud computing systems, few comprehensive reviews of factors and their relations have been conducted. Therefore, this study investigated the relations between the factors affecting e-health cloud computing systems by using a multiple criteria decision-making technique, in which decision-making trial and evaluation laboratory (DEMATEL), DANP (DEMATEL-based Analytic Network Process), and modified VIKOR (VlseKriterijumska Optimizacija I Kompromisno Resenje) approaches were combined. The intended level of adoption of an e-health cloud computing system could be determined by using the proposed approach. The results of a case study performed on the Taiwanese healthcare industry indicated that the cloud management function must be primarily enhanced and that cost effectiveness is the most significant factor in the adoption of e-health cloud computing. This result is valuable for allocating resources to decrease performance gaps in the Taiwanese healthcare industry.

scholarly journals A Survey on Machine-Learning Based Security Design for Cyber-Physical Systems

2021 ◽  
Vol 11 (12) ◽  
pp. 5458
Author(s):  
Sangjun Kim ◽  
Kyung-Joon Park
Keyword(s):  
Machine Learning ◽  
Physical System ◽  
Large Scale ◽  
Computing System ◽  
Future Research ◽  
Physical Security ◽  
Computing Systems ◽  
Physical Systems ◽  
Security Research ◽  
Simultaneous Control

A cyber-physical system (CPS) is the integration of a physical system into the real world and control applications in a computing system, interacting through a communications network. Network technology connecting physical systems and computing systems enables the simultaneous control of many physical systems and provides intelligent applications for them. However, enhancing connectivity leads to extended attack vectors in which attackers can trespass on the network and launch cyber-physical attacks, remotely disrupting the CPS. Therefore, extensive studies into cyber-physical security are being conducted in various domains, such as physical, network, and computing systems. Moreover, large-scale and complex CPSs make it difficult to analyze and detect cyber-physical attacks, and thus, machine learning (ML) techniques have recently been adopted for cyber-physical security. In this survey, we provide an extensive review of the threats and ML-based security designs for CPSs. First, we present a CPS structure that classifies the functions of the CPS into three layers: the physical system, the network, and software applications. Then, we discuss the taxonomy of cyber-physical attacks on each layer, and in particular, we analyze attacks based on the dynamics of the physical system. We review existing studies on detecting cyber-physical attacks with various ML techniques from the perspectives of the physical system, the network, and the computing system. Furthermore, we discuss future research directions for ML-based cyber-physical security research in the context of real-time constraints, resiliency, and dataset generation to learn about the possible attacks.

scholarly journals Flexible computation offloading in a fuzzy-based mobile edge orchestrator for IoT applications

2020 ◽  
Vol 9 (1) ◽  
Author(s):  
VanDung Nguyen ◽  
Tran Trong Khanh ◽  
Tri D. T. Nguyen ◽  
Choong Seon Hong ◽  
Eui-Nam Huh
Keyword(s):  
Cloud Computing ◽  
Augmented Reality ◽  
Mobile Devices ◽  
Computing System ◽  
Mobile User ◽  
Computation Offloading ◽  
Context Aware ◽  
Computing Systems ◽  
Iot Applications ◽  
Service Times

AbstractIn the Internet of Things (IoT) era, the capacity-limited Internet and uncontrollable service delays for various new applications, such as video streaming analysis and augmented reality, are challenges. Cloud computing systems, also known as a solution that offloads energy-consuming computation of IoT applications to a cloud server, cannot meet the delay-sensitive and context-aware service requirements. To address this issue, an edge computing system provides timely and context-aware services by bringing the computations and storage closer to the user. The dynamic flow of requests that can be efficiently processed is a significant challenge for edge and cloud computing systems. To improve the performance of IoT systems, the mobile edge orchestrator (MEO), which is an application placement controller, was designed by integrating end mobile devices with edge and cloud computing systems. In this paper, we propose a flexible computation offloading method in a fuzzy-based MEO for IoT applications in order to improve the efficiency in computational resource management. Considering the network, computation resources, and task requirements, a fuzzy-based MEO allows edge workload orchestration actions to decide whether to offload a mobile user to local edge, neighboring edge, or cloud servers. Additionally, increasing packet sizes will affect the failed-task ratio when the number of mobile devices increases. To reduce failed tasks because of transmission collisions and to improve service times for time-critical tasks, we define a new input crisp value, and a new output decision for a fuzzy-based MEO. Using the EdgeCloudSim simulator, we evaluate our proposal with four benchmark algorithms in augmented reality, healthcare, compute-intensive, and infotainment applications. Simulation results show that our proposal provides better results in terms of WLAN delay, service times, the number of failed tasks, and VM utilization.

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