The Component Diagnosability of General Networks

The processor failures in a multiprocessor system have a negative impact on its distributed computing efficiency. Because of the rapid expansion of multiprocessor systems, the importance of fault diagnosis is becoming increasingly prominent. The [Formula: see text]-component diagnosability of [Formula: see text], denoted by [Formula: see text], is the maximum number of nodes of the faulty set [Formula: see text] that is correctly identified in a system, and the number of components in [Formula: see text] is at least [Formula: see text]. In this paper, we determine the [Formula: see text]-component diagnosability of general networks under the PMC model and MM[Formula: see text] model. As applications, the component diagnosability is explored for some well-known networks, including complete cubic networks, hierarchical cubic networks, generalized exchanged hypercubes, dual-cube-like networks, hierarchical hypercubes, Cayley graphs generated by transposition trees (except star graphs), and DQcube as well. Furthermore, we provide some comparison results between the component diagnosability and other fault diagnosabilities.

Parallel algorithms for solving diffusion-convection problems on a multiprocessor computer system using hybrid MPI / OpenMP technology

The paper deals with the mathematical models, algorithms and software for mathematical modeling of coastal systems’ water pollution spreading dynamics under various unfavorable phenomena of natural and artificial genesis, developed for high-performance cluster systems. Methods for partitioning the computational domain for solving diffusion-convection problems have been developed, which allow for efficient parallelization of a computationally complex modeling problem, taking into account the architecture of the multiprocessor system used. The developed mathematical models are based on high-precision models of hydrophysics and hydrobiology and take into account the peculiarities of water systems in the south of the Rostov region, as well as factors of hydrobiological dynamics such as microturbulent diffusion and advective transport in various directions, mechanisms of primary and secondary pollution of coastal systems, taking into account currents. The paper presents algorithms for solving a simulated problem based on MPI parallelization technology, as well as based on mixed MPI + OpenMP technology. Numerical experiments have been carried out and the two technologies efficiency comparison has been made in the conditions of computing cluster used.

Parallel Implementation of the Algorithm to Compute Forest Fire Impact on Infrastructure Facilities of JSC Russian Railways

Forest fires have a negative impact on the economy in a number of regions, especially in Wildland Urban Interface (WUI) areas. An important link in the fight against fires in WUI areas is the development of information and computer systems for predicting the fire safety of infrastructural facilities of Russian Railways. In this work, a numerical study of heat transfer processes in the enclosing structure of a wooden building near the forest fire front was carried out using the technology of parallel computing. The novelty of the development is explained by the creation of its own program code, which is planned to be put into operation either in the Information System for Remote Monitoring of Forest Fires ISDM-Rosleskhoz, or in the information and computing system of JSC Russian Railways. In the Russian Federation, it is forbidden to use foreign systems in the security services of industrial facilities. The implementation of the deterministic model of heat transfer in the enclosing structure with the complexity of the algorithm O (2N2 + 2K) is presented. The program is implemented in Python 3.x using the NumPy and Concurrent libraries. Calculations were carried out on a multiprocessor cluster in the Sirius University of Science and Technology. The results of calculations and the acceleration coefficient for operating modes for 1, 2, 4, 8, 16, 32, 48 and 64 processes are presented. The developed algorithm can be applied to assess the fire safety of infrastructure facilities of Russian Railways. The main merit of the new development should be noted, which is explained by the ability to use large computational domains with a large number of computational grid nodes in space and time. The use of caching intermediate data in files made it possible to distribute a large number of computational nodes among the processors of a computing multiprocessor system. However, one should also note a drawback; namely, a decrease in the acceleration of computational operations with a large number of involved nodes of a multiprocessor computing system, which is explained by the write and read cycles in cache files.

Reliability Evaluation of Bicube-Based Multiprocessor System under the g-Good-Neighbor Restriction

Multiprocessor systems are commonly deployed for big data analysis because of evolution in technologies such as cloud computing, IoT, social network and so on. Reliability evaluation is of significant importance for maintenance and improvement of fault tolerance for multiprocessor systems, and system-level diagnosis is a primary strategy to identify the faulty processors in the systems. In this paper, we first determine the [Formula: see text]-good-neighbor connectivity of the [Formula: see text]-dimensional Bicube-based multiprocessor system [Formula: see text], a novel variant of hypercube. Besides, we establish the [Formula: see text]-good-neighbor diagnosability of the Bicube-based multiprocessor system [Formula: see text] under the PMC and MM* models.

Multiprocessor system for detecting and tracking marine vessels based on the "system on chip" technology

Транспортные интеллектуальные видеосистемы наблюдения, включающие в свой состав умные камеры с функциями анализа видеоинформации для обеспечения безопасности транспорта, в том числе и морских объектов, представляют собой наиболее востребованные системы, синтезируемые на базе субмикронных технологий. При этом важнейшая особенность таких систем заключается в автоматизированной обработке и анализе видеоинформации, полученной от различных камер наблюдения. Определена базовая концепция обнаружения и слежения за объектами на основе применения метода формирования опорных векторов. Применение технологии «система на кристалле» позволяет оперативно обнаружить подозрительное поведение объектов, на основе встроенных интеллектуальных сложно-функциональных блоков, входящих в состав предложенной транспортной интеллектуальной видеосистемы. В данном исследовании предлагается конкретная архитектура системы видеонаблюдения на базе программируемой многопроцессорной системы с аппаратным ускорителем основных вычислений. В состав видеосистемы наблюдения входит схема аппаратного ускорителя формирования опорных векторов, составленная классификационной частью с использованием внутренней памяти. Экспериментальный раздел показывает возможность реализации предлагаемой системы с точки зрения производительности и потребляемых ресурсов на базе программируемых схем. Рассматривается реализация системы видеонаблюдения с использованием гибридной архитектуры на базе мультипроцессора и формирования опорных векторов на базе аппаратного ускорителя. Transport intelligent video surveillance systems, which include smart cameras with video information analysis functions to ensure the safety of transport, including marine objects, are the most popular systems synthesized on the basis of submicron technologies. At the same time, the most important feature of such systems is the automated processing and analysis of video information obtained from various surveillance cameras. Basic concept of object detection and tracking is defined based on application of method of reference vectors formation. The use of the "system on chip" technology allows you to quickly detect suspicious behavior of objects, based on the built-in intelligent complex-functional blocks that are part of the proposed transport intelligent video system. In this paper, we propose a specific architecture of a video surveillance system based on a programmable multiprocessor system with a hardware accelerator for basic computing. The video surveillance system includes a circuit of a hardware accelerator for generating reference vectors, compiled by a classification part using internal memory. The experimental section shows the possibility of implementing the proposed system in terms of performance and resource consumption based on programmable circuits FPGA. Implementation of video surveillance system using hybrid architecture based on multiprocessor and formation of reference vectors based on hardware accelerator is considered.

Congestion-Aware Routing Algorithm for NoC Using Data Packets

Network on Chip (NoC) is a communication framework for the Multiprocessor System on Chip (MPSoC). It is a router-based communication system. In NoC architecture, nodes of MPSoC are communicating through the network. Different routing algorithms have been developed by researchers, e.g., XY, intermittent XY, DyAD, and DyXY. The main problems in these algorithms are congestion and faults. Congestion and faults cause delay, which degrades the performance of NoC. A congestion-aware algorithm is used for the distribution of traffic over NoC and for the avoidance of congestion. In this paper, a congestion-aware routing algorithm is proposed. The algorithm works by sending congestion information in the data packet. The algorithm is implemented on a 4 × 4 mesh NoC using FPGA. The proposed algorithm decreases latency, increases throughput, and uses less bandwidth in sharing congestion information between routers in comparison to the existing congestion-aware routing algorithms.

On the Reliability Assessment of Artificial Neural Networks Running on AI-Oriented MPSoCs

Nowadays, the usage of electronic devices running artificial neural networks (ANNs)-based applications is spreading in our everyday life. Due to their outstanding computational capabilities, ANNs have become appealing solutions for safety-critical systems as well. Frequently, they are considered intrinsically robust and fault tolerant for being brain-inspired and redundant computing models. However, when ANNs are deployed on resource-constrained hardware devices, single physical faults may compromise the activity of multiple neurons. Therefore, it is crucial to assess the reliability of the entire neural computing system, including both the software and the hardware components. This article systematically addresses reliability concerns for ANNs running on multiprocessor system-on-a-chips (MPSoCs). It presents a methodology to assign resilience scores to individual neurons and, based on that, schedule the workload of an ANN on the target MPSoC so that critical neurons are neatly distributed among the available processing elements. This reliability-oriented methodology exploits an integer linear programming solver to find the optimal solution. Experimental results are given for three different convolutional neural networks trained on MNIST, SVHN, and CIFAR-10. We carried out a comprehensive assessment on an open-source artificial intelligence-based RISC-V MPSoC. The results show the reliability improvements of the proposed methodology against the traditional scheduling.