Multiprocessor Systems Research Articles

Robustness of subsystem reliability of $k$-ary $n$-cube networks under probabilistic fault model

With the emergence of the Big Data era, as multiprocessor systems consisting of multiple processors play a vital role in big data analytics, we are prompted to explore the qualitative and quantitative metric to characterize the reliability of the systems. As the size of the multiprocessor systems grows, the probability of the occurrence of failing processors increases. One metric of the macroscopic reliability of a system is the measure of the collective effect when its subsystems are out of function. The subsystem reliability of a system is the quantitative metric that a fault-free subsystem of specific size is operational as before with the occurrence of individual faults. Although some networks have the same order and similar topologies, there are differences in their subsystem reliabilities. In this work, we focus on the comparison of two distinct topologies of <inline-formula><tex-math notation="LaTeX">$k$</tex-math></inline-formula>-ary <inline-formula><tex-math notation="LaTeX">$n$</tex-math></inline-formula>-cube networks with the same order and calculate the robustness of reliability bounds of <inline-formula><tex-math notation="LaTeX">$k$</tex-math></inline-formula>-ary <inline-formula><tex-math notation="LaTeX">$n$</tex-math></inline-formula>-cube networks. We analytically show that the subsystem reliability is negatively correlated with the dimension <inline-formula><tex-math notation="LaTeX">$n$</tex-math></inline-formula>, even if two subsystems of <inline-formula><tex-math notation="LaTeX">$Q_{n}^{k}$</tex-math></inline-formula> are of the same order. That is, the smaller <inline-formula><tex-math notation="LaTeX">$n$</tex-math></inline-formula> is, the larger subsystem reliability of <inline-formula><tex-math notation="LaTeX">$Q_{n}^{k}$</tex-math></inline-formula> will be. This work provides a theoretical methodology to choose the more dependable topology of <inline-formula><tex-math notation="LaTeX">$k$</tex-math></inline-formula>-ary <inline-formula><tex-math notation="LaTeX">$n$</tex-math></inline-formula>-cube networks with the same order. Finally, we apply some numerical simulations to validate the results we established.

Discrete Firefly Algorithm for Optimizing Topology Generation and Core Mapping of Network-on-Chip

Network-on-chip (NoC) proves to be the best alternative to replace the traditional bus-based interconnection in Multi-Processor System on a Chip (MPSoCs). Irregular NoC topologies are highly recommended and utilised in various applications as they are application specific. Optimized mapping of the cores in a NoC plays a major role in its performance. Firefly algorithm is a bio-inspired meta-heuristic approach. Discretized firefly algorithm is used in our proposed work. In this work, two optimization algorithms are proposed: Topology Generation using Discrete Firefly Algorithm (TGDFA) and Core Mapping using Discrete Firefly Algorithm (CMDFA) for multimedia benchmark applications, Video Object Plane Decoder (VOPD), Multimedia Window Display (MWD) and MP3 Encoder. The irregular topology generated using TGDFA is mapped using CMDFA onto a reconfigurable mesh with switches in between the routers to make it a fault tolerant one. The proposed TGDFA provides better optimization of communication cost. The speed-up on the average run time of the tasks and the time taken to attain the best solution by the proposed TGDFA is significant. The dynamic energy consumption of core mapping obtained by the proposed CMDFA is lesser when compared to the existing work. Finally, the optimized core mapping is implemented in a cycle accurate NoC simulator: Noxim. It is substantiated experimentally that the proposed CMDFA outperforms the previous work.

Multi-processor scheduling of elastic applications in compositional real-time systems

Scheduling of real-time applications modelled according to the periodic and the sporadic task model under hierarchical and compositional real-time systems has been widely studied to provide temporal isolation among independent applications running on shared resources. However, for some real-time applications which are amenable to variation in their timing behaviour, usage of these tasks models can result in pessimistic solutions. The elastic task model addresses this pessimism by allowing the timing requirements of an application’s tasks to be specified as a range of values instead of a single value. Although the scheduling of elastic applications on dedicated resources has received considerable attention, there is limited work on scheduling of such applications in hierarchical and compositional settings. In this paper, we evaluate different earliest deadline first scheduling algorithms to schedule elastic applications in a minimum parallelism supply form reservation on a multiprocessor system. Our evaluation indicates that the proposed approach provides performance comparable to the current state-of-art algorithms for scheduling elastic applications on dedicated processors in terms of schedulability.

Fault tolerance makespan synthesis in multi-process systems via resource sharing and backtracking

This paper proposes an algorithms to find the time optimal fault-tolerant controllable string for multi-process systems, in which some specifications are enforced to the nonfaulty system, such that the system can recover from faults and satisfy the specifications efficiently through resource sharing. We first derive an algorithm to check the fault-tolerant property via the abstraction of the system, then a backtracking algorithm is sequentially used to find the optimal fault-tolerant string based on the abstraction of the system. Moreover, the proposed fault tolerant string synthesis algorithms are tested on a simulated manufacturing system to show their effectiveness.

Information security models of real-time multiprocessor computing systems

Information security models of real-time multiprocessor computing systems

A software package for solving grid equations in areas with geometry "stretched" along one of the spatial directions

A method for solving grid equations in areas with geometry "stretched" along one of the spatial directions is proposed. Method proposed for model includes pollutants whose concentration in the Azov Sea, according to the analysis of scientific publications and environmental databases, exceeds the maximum permissible concentration (MPC). The mechanism of the preconditioner formation is described. The estimation of the minimum and maximum eigenvalues is performed. Using object-oriented analysis and design, a class library has been implemented to solve the problem of impurity propagation in a shallow reservoir. The architecture of an information system to support scientific research capable of functioning in conditions of using heterogeneous computing environments is described. The system is based on the requirement of the maximum possible use of free software in order to minimize capital and operational costs. The system uses classes that allow you to read and write information about the geometry of the object under study and the parameters of the calculated grid. Numerical experiments were carried out using the developed software package, including the proposed mathematical model, focused on the multiprocessor computing system (MCS). The dependences of the SLAE solution time on the number of diagonals and the order of the matrix are obtained. The number of iterations for solving grid equations has been experimentally determined depending on the ratio of the number of nodes in spatial coordinates.

Evaluation of the reliability of special software for real-time multiprocessor computing systems

Evaluation of the reliability of special software for real-time multiprocessor computing systems

Optimization of the amount of transmitted data in parallel algorithms for iterative methods with a triangular preconditioner

Software has been developed in the C++ programming language using the MPI parallel programming technology, designed for mathematical modeling of the transport of substances in coastal systems. When calculating the dynamics of the spread of a pollutant, the decomposition of the computational domain was carried out to organize the computational process on a multiprocessor computer system K-60 in KIAM RAS. To solve the system of grid equations obtained as a result of the approximation of the problem, iterative methods were used, with a triangular preconditioner.

G-Good-Neighbour Diagnosability Under the Modified Comparison Model for Multiprocessor Systems

G-Good-Neighbour Diagnosability Under the Modified Comparison Model for Multiprocessor Systems

AI SoC-Based Accelerator for Speech Classification Accélérateur de classification de la parole basé sur un AI SoC

Speech classification acceleration using field-programmable gate arrays (FPGAs) is a well-studied field and enables the potential to gain both speed and better energy efficiency over other processor-intensive classifiers. System-on-chip (SoC) architecture allows for an integrated system between programmable logic and processor and for increased bandwidth communications to on-chip peripherals and memory. This article serves as an investigation of the utility of an edge-based support-vector machine (SVM) implemented onto a Zynq-XC7Z020 multiprocessor system on a chip (MPSoC) for the acceleration of three speech class pairs. The system allows for a parallelized structure, which yielded a faster classifier model. The results were found to be an acceleration factor of 2.08 <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\times$</tex-math> </inline-formula> . This appears to have come at the cost of a decrease in prediction accuracy, lowering from 92.5% to 83.5% positive prediction percentage likely due to decreased data resolution. The resolution used in this model was a 16-bit fixed-point format for the hardware interpretation and a floating-point format for the software benchmark. The resource usage of the FPGA was also analyzed for both overlays and can yield a 21% reduction in CPU usage.

SpNNGP R Package for Nearest Neighbor Gaussian Process Models

This paper describes and illustrates functionality of the spNNGP R package. The package provides a suite of spatial regression models for Gaussian and non-Gaussian pointreferenced outcomes that are spatially indexed. The package implements several Markov chain Monte Carlo (MCMC) and MCMC-free nearest neighbor Gaussian process (NNGP) models for inference about large spatial data. Non-Gaussian outcomes are modeled using a NNGP Pólya-Gamma latent variable. OpenMP parallelization options are provided to take advantage of multiprocessor systems. Package features are illustrated using simulated and real data sets.

The Pessimistic Diagnosability of Folded Petersen Cubes

Diagnosability is an important metric parameter for measuring the reliability of multiprocessor systems. The pessimistic diagnosis strategy is a classic diagnostic model based on the PMC model. The class of folded Petersen cubes, denoted by FPQn,k, where n, k ≥ 0 and (n, k) ≠ (0, 0), is introduced as a competitive model of the hypercubes, which is constructed by iteratively applying the Cartesian product operation on the hypercube Qn and the Petersen graph P. In this paper, by exploring the structural properties of the folded Petersen cubes FPQn,k, we first prove that FPQn,k is (n + 3k) diagnosable under the PMC model. Then, we completely derive that the pessimistic diagnosability of FPQn,k is 2n + 6k − 2 under the PMC model. Furthermore, the diagnosability and the pessimistic diagnosability of the class of folded Petersen cubes, including the hypercube, folded Petersen graph, and hyper Petersen graph, are obtained.

РЕАЛІЗАЦІЯ БАГАТОПОТОЧНОСТІ НА АРХІТЕКТУРІ МУЛЬТИМЕДІЙНИХ ПРОЦЕСОРІВ NEXPERIA

The structure of multiprocessor systems based on the architecture of modern Nexperia multimedia processors with a 32-bit computing core is considered. The fields of application of multimedia processors as general purpose processors and their data processing functions are studied. Multimedia processors use either functional architectures with limited flexibility but higher speed and efficiency, or programmable architectures with increased flexibility. The architecture of one of the Nexperia processors - TriMedia TM-1300 is analyzed and the diagram of its main components, the principle of operation of the central arbitration of the processor bus and methods of increasing its speed are given. Advanced general-purpose processors have been found to provide multimedia support by incorporating new multimedia instructions and executing them in parallel using a SIMD coprocessor approach. They provide multimedia support, including multimedia instructions in the instruction set. Instead of performing specific multimedia functions (such as compression and 3D graphics), multimedia processors provide purpose-built instructions to support general video processing operations. These instructions include support for 8-bit data types (pixels), efficient data addressing, and I/O instructions. The article examines the possibilities of software implementation of the parallelization of processors using parallel processing technologies, which is achieved by dividing one execution of a task into several independent smaller tasks. A software implementation of the work using a static and anonymous method is proposed. The program codes and the results of their testing are given. It has been proven that the division of different parts of the task between several computational resources of the CPU allows to reduce the execution time of the program and improves the potential computing power of the computer system.

Task Scheduling Optimization in Cloud Computing by Rao Algorithm

Cloud computing is currently dominated within the space of high-performance distributed computing and it provides resource polling and on-demand services through the web. So, task scheduling problem becomes a very important analysis space within the field of a cloud computing environment as a result of user's services demand modification dynamically. The main purpose of task scheduling is to assign tasks to available processors to produce minimum schedule length without violating precedence restrictions. In heterogeneous multiprocessor systems, task assignments and schedules have a significant impact on system operation. Within the heuristic-based task scheduling algorithm, the different processes will lead to a different task execution time (makespan) on a heterogeneous computing system. Thus, a good scheduling algorithm should be able to set precedence efficiently for every subtask depending on the resources required to reduce (makespan). In this paper, we propose a new efficient task scheduling algorithm in cloud computing systems based on RAO algorithm to solve an important task and schedule a heterogeneous multiple processing problem. The basic idea of this process is to exploit the advantages of heuristic-based algorithms to reduce space search and time to get the best solution. We evaluate our algorithm's performance by applying it to three examples with a different number of tasks and processors. The experimental results show that the proposed approach significantly succeeded in finding the optimal solutions than others in terms of the time of task implementation.

On the Possibilities of Using Modern Multiprocessor Systems for Diffraction of Electromagnetic Waves on Bodies of Complex Shape Problem Solving

On the Possibilities of Using Modern Multiprocessor Systems for Diffraction of Electromagnetic Waves on Bodies of Complex Shape Problem Solving

Solar Particle Event and Single Event Upset Prediction from SRAM-based Monitor and Supervised Machine Learning

The intensity of cosmic radiation may differ over five orders of magnitude within a few hours or days during the Solar Particle Events (SPEs), thus increasing for several orders of magnitude the probability of Single Event Upsets (SEUs) in space-borne electronic systems. Therefore, it is vital to enable the early detection of the SEU rate changes in order to ensure timely activation of dynamic radiation hardening measures. In this paper, an embedded approach for the prediction of SPEs and SRAM SEU rate is presented. The proposed solution combines the real-time SRAM-based SEU monitor, the offline-trained machine learning model and online learning algorithm for the prediction. With respect to the state-of-the-art, our solution brings the following benefits: (1) Use of existing on-chip data storage SRAM as a particle detector, thus minimizing the hardware and power overhead, (2) Prediction of SRAM SEU rate one hour in advance, with the fine-grained hourly tracking of SEU variations during SPEs as well as under normal conditions, (3) Online optimization of the prediction model for enhancing the prediction accuracy during run-time, (4) Negligible cost of hardware accelerator design for the implementation of selected machine learning model and online learning algorithm. The proposed design is intended for a highly dependable and self-adaptive multiprocessing system employed in space applications, allowing to trigger the radiation mitigation mechanisms before the onset of high radiation levels.

A novel risk analysis approach for occupational safety using Bayesian Network and interval type-2 fuzzy sets: the case of underground mining

Occupational safety problems are no longer acceptable for any industrial environment. Lack of comprehensive and reliable evaluations for occupational safety causes many undesired events and harm to employees during the industrial process. In this study, it is aimed to develop an applicable risk analysis methodology for evaluating the undesired occupational events that occurred in the multi-process system where no historical accident records. The difficulty in obtaining and analyzing the data required for the determination of the occupational safety risks especially in the manually executed processes has been overcome with the Bayesian Network and interval type-2 fuzzy sets by using the expert judgments. While BN enables to development of a comprehensive reasoning approach about the occurrence of the events, interval type-2 fuzzy sets better represent the ambiguity in the judgments by covering the uncertainty in a wider mathematical range with less computational effort according to other fuzzy sets. During multi-processes in industrial activity, various occupational undesired events may occur, including rare events with very serious consequences or frequent events with very low severity consequences. To able to consider all kinds of events occurring in an industrial environment from a holistic risk perspective, a novel fuzzy scale for specifying the probability and consequence of the events are proposed by the interval type-2 fuzzy numbers. Therefore, all undesired events regardless the probability and consequence which may occur during the multi-processes in a system and the main root causes of these events can be observed within the proposed methodology. A case study is used to emphasize the effect of the proposed methodology for risk analysis of occupational safety in underground mining. The results have indicated that occupational safety education is the most contributing factor to occurring the undesired occupational events in underground mining. We believe that this study could help evaluate the safety risk of the multi-process systems comprehensively and holistically and proposing strategic planning for mitigating the occupational safety risks.

A Linguistic Study of Speech Comprehension and Production in Aphasic Speakers

Distributed memory is a term used in computer science to describe a multiprocessor computer system in which each processor has its own private memory. Computational jobs can only work with local data, so if you need remote data, you'll have to communicate with one or more remote processors. Parallel and distributed computing are frequently used together. Distributed parallel computing employs many computing devices to process tasks in parallel, whereas parallel computing on a single computer uses multiple processors to execute tasks in parallel. Distributed systems are designed separately from the core network. There are different kinds of distributed systems such as peer-to-peer (P2P) networks, groups, grids, distributed storage systems. The multicore processor can be classified into two types: homogeneous and heterogeneous. This paper reviews the impact of the distributed-memory parallel processing approach on performance-enhancing of multicomputer-multicore systems. Also, number of methods have been introduced which used in distributed-memory systems and discuss which method is the best and enhance multicore performance in distributed systems. The best methods were those which used an operating system named gun/Linux 4.8.0-36, intel Xeon 2.5, python programming language.

Two important parameters of HPMC model

To address the fault diagnosis problem in hybrid fault circumstances in which both node and link faults may happen, Q. Zhu et al. propose the HPMC model. As the generalizations of the diagnosability under the PMC model, h-restricted vertex diagnosability and r-restricted edge diagnosability can evaluate the fault diagnosis capability of multiprocessor systems under the HPMC model. In this paper, we provide a more precise characterization for the hybrid fault diagnosis capability of multiprocessor systems by studying the h-restricted vertex diagnosability and r-restricted edge diagnosability of a class of interconnection networks. As applications, these two parameters of various networks are established under the HPMC model.

Index Dependent Nested Loops Parallelization with an Even Distributed Number of Steps

Parallel processing of algorithms is an effective way to achieve higher performance on multiprocessor systems rather. During parallelization, it is critical to minimize the difference between the processing time for threads. It is necessary to choose a method that can efficiently distribute the workload evenly across the threads. This paper deals with a special kind of nested loops where the internal loop iterator depends on the outer loop iterator. In such cases, the process can be represented as an upper (or lower) triangular matrix. This paper introduces a method for partitioning the outer loop according to the indices in an almost optimal manner, so that the partial loops in each thread will take nearly the same number of steps. In addition, we examine the potential of a perfect partition and try to determine the maximum (but still meaningful) partition size.