Parallel Processing Letters

Balanced hypercube is a variant of the hypercube structure; it offers desirable properties such as connectivity, diameter, and fault-tolerance. Cycles and tori are popular interconnection topologies and have been widely used in distributed-memory parallel computers. Scholars have developed diverse and numerous parallel algorithms of cycles and tori. This paper proposes an efficient algorithm, called reflected edge label sequence, to generate cycle structures in balanced hypercubes. With this algorithm, we can embed cycles of length $2^{2k+1}$, $1\le k<n$, into an $n$-dimensional balanced hypercube. The corresponding algorithm for embedding 2-dimensional $2^{2k}\times 2^{2k}$ tori can be constructed on the basis of this approach, and the time complexity is linear with respect to the size of a balanced hypercube.

The conventional form of Amdahl’s law states that speedup of calculations in a multiprocessor machine is limited by the definite constant value just due to the existence of some non-parallelizable part in any algorithm. This brief paper considers one more general reason, which prevents a growth of parallel performance: processes that implement distributed task cannot start simultaneously and hence every process adds some start-up time, also reducing by that the gain from a parallel processing. The simple formula, proposed here to extend Amdahl’s law, leads to a less optimistic picture in comparison with classical results: for large amount of processor units the modified law does not approach to constant but vanishes. This is the result of competition between two factors: decreasing of calculation duty and increasing of start-up time when a number of parallel processes grows. The effect may be subdued by means of specific regularity in launching parallel processes.

Models for energy-efficient static scheduling of parallelizable tasks with deadlines on frequency-scalable parallel machines comprise moldable vs. malleable tasks and continuous vs. discrete frequency levels, plus preemptive vs. non-preemptive task execution with or without task migration. We investigate the tradeoff between scheduling time and energy efficiency when going from continuous to discrete core allocation and frequency levels on a multicore processor, and from preemptive to non-preemptive task execution. To this end, we present a tool to convert a schedule computed for malleable tasks on machines with continuous frequency scaling [Sanders and Speck, Euro-Par (2012)] into one for moldable tasks on a machine with discrete frequency levels. We compare the energy efficiency of the converted schedule to the energy consumed by a schedule produced by the integrated crown scheduler [Melot et al., ACM TACO (2015)] for moldable tasks and a machine with discrete frequency levels. Our experiments with synthetic and application-based task sets indicate that the converted Sanders Speck schedules, while computed faster, consume more energy on average than crown schedules. Surprisingly, it is not the step from malleable to moldable tasks that is responsible but the step from continuous to discrete frequency levels. One-time frequency scaling during a task’s execution can compensate for most of the energy overhead caused by frequency discretization.

This paper presents and discusses the implementation of deep neural network for the purpose of failure prediction in the cold forging process. The implementation consists of an LSTM and a dense layer implemented on FPGA. The network was trained beforehand on Desktop Computer using Keras library for Python and the weights and the biases were embedded into the implementation. The implementation is executed using the DSP blocks, available via Vivado Design Suite, which are in compliance with the IEEE754 standard. The simulation of the network achieves 100% classification accuracy on the test data and high calculation speed.

We discuss the problem of filtering out abnormal states from a larger number of quantum states. For this type of problem with $N$ items to be searched, both the traditional search by enumeration and classical Grover search algorithm have the complexity about $O(N)$. In this letter a novel quantum search scheme with exponential speed up is proposed for abnormal states. First, a new comprehensive quantum operator is well-designed to extract the superposition state containing all abnormal states with unknown number $L(\ll N)$ with complexity $O(1)$ in probability 1 via well-designed parallel phase comparison. Then, every abnormal state is achieved respectively from $L$ abnormal states via $O(4L^2)$ times’ measurement. Finally, a numerical example is given to show the efficiency of the proposed scheme.

In order to overcome the problems of low detection accuracy and long detection time of traditional fault detection methods for CNC machine tools, a new fault detection method for CNC machine tools based on wavelet transform is proposed in this paper. In order to improve the effectiveness of running fault detection of CNC machine tools, a wavelet transform method is used to extract the features of the running fault signals of CNC machine tools. According to the feature extraction results, the convolution calculation of the continuous wavelet transform is used to complete the fault detection of CNC machine tool according to the scale result of fault signal. The experimental results show that, compared with traditional fault detection methods, the detection accuracy and efficiency of this method is significantly better: the highest detection accuracy is 97%, and the lowest detection time is only 1.1s.

Digital Signal Processors (DSP) have a ubiquitous presence in almost all civil and military signal processing applications, including mission critical environments like nuclear reactors, process control etc. Arithmetic and Logic units (ALU), being the heart of any digital signal processor, play critical and decisive roles in achieving the required parameter benchmarks and the overall efficiency and robustness of the digital signal processor. State of the art research has shown successful traction with the performance requirements of critical Multiply-Accumulate (MAC) parameters, like reduced power consumption, small electronic real estate footprint and reduction in delay with the associated design complexity. Judicious placement of its building blocks, namely, the truncated multiplier and half-sum carry generation-sum carry generation (HSCG-SCG) adder in the architectural design of ALU and the type of adder and multiplier circuits selected are the core decisions that decide the overall performance of the ALU. To overcome the drawback and to improve the performance further, this work proposes a new architecture for the square root (SQRT) carry select adder (CSLA) using half-sum generation (HSG), half-carry generation (HCG), full-sum generation (FSG) and full-carry generation (FCG) blocks. The proposed design contains N-bit architecture, and comparative results are considered for 8-bit, 16-bit and 32-bit combinations. All the designs are implemented in the Xilinx ISE environment and the results show that better area, power, and delay performance compared to the state of art methods.

The recent rapid spread of deadly epidemics have precipitated an urgent need to speed up the development of different treatments, as well as methods of evaluating their efficacy. The first step towards this is the collection of data relating to the cure rate in groups of patients who have had different treatments applied to them. As most of the available data in these cases is random, it is now the role of statisticians to provide efficient statistical tests to assess the treatment methods through the data. This research aims to provide a new statistical test with high efficiency to reach the right decision with accurate results as quickly as possible using parallel computing algorithms through Wolfram Mathematica software.

A new coronavirus, causing a severe acute respiratory syndrome (COVID-19), was started at Wuhan, China, in December 2019. The epidemic has rapidly spread across the world becoming a pandemic that, as of today, has affected more than 70 million people causing over 2 million deaths. To better understand the evolution of spread of the COVID-19 pandemic, we developed PANC (Parallel Network Analysis and Communities Detection), a new parallel preprocessing methodology for network-based analysis and communities detection on Italian COVID-19 data. The goal of the methodology is to analyze set of homogeneous datasets (i.e. COVID-19 data in several regions) using a statistical test to find similar/dissimilar behaviours, mapping such similarity information on a graph and then using community detection algorithm to visualize and analyze the initial dataset. The methodology includes the following steps: (i) a parallel methodology to build similarity matrices that represent similar or dissimilar regions with respect to data; (ii) an effective workload balancing function to improve performance; (iii) the mapping of similarity matrices into networks where nodes represent Italian regions, and edges represent similarity relationships; (iv) the discovering and visualization of communities of regions that show similar behaviour. The methodology is general and can be applied to world-wide data about COVID-19, as well as to all types of data sets in tabular and matrix format. To estimate the scalability with increasing workloads, we analyzed three synthetic COVID-19 datasets with the size of 90.0MB, 180.0MB, and 360.0MB. Experiments was performed on showing the amount of data that can be analyzed in a given amount of time increases almost linearly with the number of computing resources available. Instead, to perform communities detection, we employed the real data set.

This paper presents a hybrid approach based discrete Particle Swarm Optimization (PSO) and chaotic strategies for solving multi-objective task scheduling problem in cloud computing. The main purpose is to allocate the summited tasks to the available resources in the cloud environment with minimum makespan (i.e. schedule length) and processing cost while maximizing resource utilization without violating Service Level Agreement (SLA) among users and cloud providers. The main challenges faced by Particle Swarm Optimization (PSO) when used to solve scheduling problems are premature convergence and trapping into local optimum. This paper presents an enhanced Particle Swarm Optimization algorithm hybridized with Chaotic Map strategies. The proposed approach is called Enhanced Particle Swarm Optimization based Chaotic Strategies (EPSOCHO) algorithm. Our proposed approach suggests two Chaotic Map strategies: sinusoidal iterator and Lorenz attractor to enhanced PSO algorithm in order to get good convergence and diversity for optimizing the task scheduling in cloud computing. The proposed approach is simulated and implemented in Cloudsim simulator. The performance of the proposed approach is compared with the standard PSO algorithm, the improved PSO algorithm with Longest job to fastest processor (LJFP-PSO), and the improved PSO algorithm with minimum completion time (MCT-PSO) using different sizes of tasks and various benchmark datasets. The results clearly demonstrate the efficiency of the proposed approach in terms of makespan, processing cost and resources utilization.

In the power system, research is being conducted in diagnosing and monitoring the condition of power equipment in a precise way. The Partial Discharges (PD) estimations under high voltage is recognized to be the most renowned and useful approach for accessing the electrical behaviour of the insulation material. The PD is good at localizing the dielectric failures even in the smaller regions before the occurrence of the dielectric breakdown. Therefore, the PD condition monitoring with accurate feature specification will be the appropriate model for enhancing the life span of the electrical apparatus. In this research work, a novel data-driven approach is introduced to detect the PD pulses in power cables using optimization based machine learning models. The proposed model will encompass two major phases: feature extraction and recognition. The first phase of the proposed method concentrates on extracting the wavelet scattering transform-based features. In the second phase, these features are fed as the input to optimized Deep Belief Network (DBN), whose count of the hidden neuron is optimized via a Self Adaptive Border Collie Optimization algorithm (SA-BCO). Finally, the performance evaluation is done in terms of diverse performance measures.