Parallel Environment Research Articles

Octree Optimized Micrometric Fibrous Microstructure Generation for Domain Reconstruction and Flow Simulation.

Over recent decades, tremendous advances in the field of scalable numerical tools and mesh immersion techniques have been achieved to improve numerical efficiency while preserving a good quality of the obtained results. In this context, an octree-optimized microstructure generation and domain reconstruction with adaptative meshing is presented and illustrated through a flow simulation example applied to permeability computation of micrometric fibrous materials. Thanks to the octree implementation, the numerous distance calculations in these processes are decreased, thus the computational complexity is reduced. Using the parallel environment of the ICI-tech library as a mesher and a solver, a large scale case study is performed. The study is applied to the computation of the full permeability tensor of a three-dimensional microstructure containing 10,000 fibers. The considered flow is a Stokes flow and it is solved with a stabilized finite element formulation and a monolithic approach.

Large-scale Simulation of Unsteady Flows in 13-stage Multi-spool Compressor

Although significant advances have been made in full-annulus simulation of flow in single-spool compressors. However, no work has been devoted to full-annulus simulation of flow in multi-spool compressors. The aim of the paper is to develop techniques for full-annulus simulation of unsteady flows in multi-spool compressor. The techniques for automatic generation of large-scale full-annulus grid of multi-spool compressor, for uniting simulation in LPC(Low Pressure Compressor) and HPC(High Pressure Compressor) in one solver, for processing LPC/HPC interface in multi-spool compressor in parallel environment, for managing vast amounts of flow field data in multi-spool compressor with low space complexity are proposed. Based on an in-house software ASPAC, these techniques are then applied to full-annulus unsteady simulation of flow in a 13-stage multi-spool compressor with 800 million grid cells using 10240 processes. The numerical simulation results are validated through compared with results of empirical model. Meanwhile, it indicates that results of large-scale unsteady simulation are more precise than results of steady simulation.

High performance computing of stiff bubble collapse on CPU-GPU heterogeneous platform

SCB is an efficient fluid solver developed for computing two-phase compressible flows involving strong shocks and expansion waves. It solves a four-equation diffuse-interface model, which is derived from the five-equation model proposed by Kapila et al. The governing equations are discretized by a finite volume method with explicit time stepping. SCB uses a fully parallel environment via Message Passing Interfaces (MPI). With the fast growing number of heterogeneous computing platforms including disparate hardware architectures, it becomes nowadays necessary to develop hybrid parallelization strategies with a special care to portability. In this context, we present an heterogeneous computing framework based on MPI library and OpenACC. The choice of OpenACC is discussed. Performances, scalability and adaptability are illustrated through a series of tests on an heterogeneous architecture. Validations are proposed on various bubble collapses, in free-field or near a rigid wall. Comparisons are done with existing results and analytical solutions. Furthermore a stiff shock-induced bubble collapse demonstrates the capabilities and the high potential of the code.

Multiscale analysis for 3D lattice structures based on parallel computing

AbstractIn this study, a parallel framework for the multiscale analysis of three‐dimensional lattice structures is developed. The established parallel framework performs the multiscale analysis using the extended multiscale finite element method (EMsFEM) in a parallel environment provided by the portable and extensible toolkit for scientific computation (PETSc), which is a high‐performance parallel scientific computing library. To realize this aim, we developed several modifications of the original EMsFEM method and adopted some routines from PETSc. Through numerical examples, the efficiency and accuracy of the proposed parallel computing framework were verified first. Then, the parallel acceleration ratio and parallel efficiency of the proposed parallel framework were studied. The proposed parallel computing framework shows good efficiency and can be used to deal with the analysis of large‐scale lattice structures.

The influence of data size on a high-performance computing memetic algorithm in fingerprint dataset

The fingerprint is one kind of biometric. This biometric unique data have to be processed well and secure. The problem gets more complicated as data grows. This work is conducted to process image fingerprint data with a memetic algorithm, a simple and reliable algorithm. In order to achieve the best result, we run this algorithm in a parallel environment by utilizing a multi-thread feature of the processor. We propose a high-performance computing memetic algorithm (HPCMA) to process a 7200 image fingerprint dataset which is divided into fifteen specimens based on its characteristics based on the image specification to get the detail of each image. A combination of each specimen generates a new data variation. This algorithm runs in two different operating systems, Windows 7 and Windows 10 then we measure the influence of data size on processing time, speed up, and efficiency of HPCMA with simple linear regression. The result shows data size is very influencing to processing time more than 90%, to speed up more than 30%, and to efficiency more than 19%.

Efficiency of Mixed-Element USM3D for Benchmark Three-Dimensional Flows

The unstructured mixed-element cell-centered finite volume flow solver USM3D is enhanced with new capabilities such as line generation for general unstructured grids and optimization of the iterative solver. The efficiency of the new developments is assessed in a massively parallel environment. The assessments are conducted using a baseline method and the recent hierarchical adaptive nonlinear iteration method framework. Three benchmark turbulent flows (namely, a subsonic separated flow around a three-dimensional hemisphere–cylinder configuration, a transonic flow around the ONERA M6 wing, and a supersonic flow through a square duct) are considered.

Performance evaluation and analysis of distributed multi-agent optimization algorithms with sparsified directed communication

There has been significant interest in distributed optimization algorithms, motivated by applications in Big Data analytics, smart grid, vehicle networks, etc. While there have been extensive theory and theoretical advances, a proportionally small body of scientific literature focuses on numerical evaluation of the proposed methods in actual practical, parallel programming environments. This paper considers a general algorithmic framework of first and second order methods with sparsified communications and computations across worker nodes. The considered framework subsumes several existing methods. In addition, a novel method that utilizes unidirectional sparsified communications is introduced and theoretical convergence analysis is also provided. Namely, we prove R-linear convergence in the expected norm. A thorough empirical evaluation of the methods using Message Passing Interface (MPI) on a High Performance Computing (HPC) cluster is carried out and several useful insights and guidelines on the performance of algorithms and inherent communication-computational trade-offs in a realistic setting are derived.

Reading Rate of Academic English Texts: Comparing L1 and Advanced L2 Users in Different Language Environments

Slower reading in a second language (L2) has been attributed to lower proficiency and/or to slower language processing. This study investigates the role of linguistic context in L1 and L2 academic reading speed among 295 undergraduate Psychology students who all read English language texts at university. The aim was to compare academic reading among students in a predominantly English-speaking environment (the UK) with those in a parallel language context where both English and the local language are used in teaching (Norway). Three groups were tested: Norwegian students in Norway, and both L1 and L2 English-users in the UK. Participants completed a timed academic reading task, followed by comprehension questions. Although all three groups achieved similar mean scores on the comprehension questions, the L1 and L2 English-speaking students in the UK read the text significantly faster than the Norwegian students. There was no significant difference between reading times for the L1 and L2 readers in the UK, indicating that the difference was not simply a consequence of L2 reading. Additionally, in contrast to previous research on groups with lower L2 proficiency, this study found no significant association between reported extramural English exposure and reading speed in either L2 group. The results indicate that advanced L2 readers in a parallel language environment may need more time to read academic texts in L2 compared to L1 readers and L2 readers in an immersion context, which has implications for the time and support needed by these students.

Distributed model for customer churn prediction using convolutional neural network

PurposeThe purpose of the proposed model is to assist the e-business to predict the churned users using machine learning. This paper aims to monitor the customer behavior and to perform decision-making accordingly.Design/methodology/approachThe proposed model uses the 2-D convolutional neural network (CNN; a technique of deep learning). The proposed model is a layered architecture that comprises two different phases that are data load and preprocessing layer and 2-D CNN layer. In addition, the Apache Spark parallel and distributed framework is used to process the data in a parallel environment. Training data is captured from Kaggle by using Telco Customer Churn.FindingsThe proposed model is accurate and has an accuracy score of 0.963 out of 1. In addition, the training and validation loss is extremely less, which is 0.004. The confusion matric results show the true-positive values are 95% and the true-negative values are 94%. However, the false-negative is only 5% and the false-positive is only 6%, which is effective.Originality/valueThis paper highlights an inclusive description of preprocessing required for the CNN model. The data set is addressed more carefully for the successful customer churn prediction.

Design and Implementation of Human-Computer Interaction System in Parallel Digital Library System Based on Neural Network

Information and communication technologies are well thought-out as probable assets for the development of socioeconomics in developing countries. Studies have shown that enhanced infrastructure of telecommunication has facilitated means for underserved population development by various ways. Among the existing applications of ICT, the digital library systems provide with better solutions and respond to a variety of unmet needs of research institutions, scientific communities, and developments. With the development of digital library technology, the parallel database system has become the main tool for efficient information processing in the digital library system. On this basis, based on the parallel environment of the computer cluster, by coordinating the communication in the parallel environment, the coordinator, the collection machine, and the query processor can complete the operation of distribution, load, and maintenance, which has high efficiency and saves much precious time, supports the digital library to meet user requirements effectively, and meets the digital library’s performance requirements for data, and also, the key problem in the parallel algorithm has been solved. The experimental results show that this parallel technique has very good performance and efficiency.

A robust multi-response VNS-aiNet approach for solving scheduling problems under unrelated parallel machines environments

In the last few years, a lot of researchers addresses scheduling problems considering unrelated parallel machines to bridge theoretical problems and real-world manufacture environments. Most of these works consider objective function’s characteristics to assist the algorithms in exploiting the search space. This assistance usually leads to a better convergence of the algorithms, but it tends to leave these algorithms restricted only to the own optimization criterion studied. This feature hinders to extend these works to optimization criteria with less theoretical visibility. This work proposes an immuno-inspired algorithm for the resolution of scheduling problems under unrelated parallel environments, evaluating the hypothesis that it is possible to construct a robust approach that is extensible to different optimization criteria, achieving results similar to the state-of-art approaches in the literature for specific criteria. Four case studies are proposed, each one addressing an optimization criterion, which is a benchmark for sequencing problems. The criteria are Total Weighted Tardiness, Makespan, Total Tardiness, and Total Weighted Completion Time. The experiments indicate that the results achieved by the proposed approach are compatible and, in many cases, superior to the state-of-art approaches for the optimization criteria studied, using computational resources compatible with those presented by these approaches.

A Note on Multimachine Scheduling with Weighted Early/Late Work Criteria and Common Due Date

In this note, we revisit two types of scheduling problem with weighted early/late work criteria and a common due date. For parallel identical machines environment, we present a dynamic programming approach running in pseudopolynomial time, to classify the considered problem into the set of binary NP-hard. We also propose an enumeration algorithm for comparison. For two-machine flow shop systems, we focus on a previous dynamic programming method, but with a more precise analysis, to improve the practical performance during its execution. For each model, we verify our studies through computational experiments, in which we show the advantages of our techniques, respectively.

Chaining Optimization Methodology: A New SHA-3 Implementation on Low-End Microcontrollers

Since the Keccak algorithm was selected by the US National Institute of Standards and Technology (NIST) as the standard SHA-3 hash algorithm for replacing the currently used SHA-2 algorithm in 2015, various optimization methods have been studied in parallel and hardware environments. However, in a software environment, the SHA-3 algorithm is much slower than the existing SHA-2 family; therefore, the use of the SHA-3 algorithm is low in a limited environment using embedded devices such as a Wireless Sensor Networks (WSN) enviornment. In this article, we propose a software optimization method that can be used generally to break through the speed limit of SHA-3. We combine the θ, π, and ρ processes into one, reducing memory access to the internal state more efficiently than conventional software methods. In addition, we present a new SHA-3 implementation for the proposed method in the most constrained environment, the 8-bit AVR microcontroller. This new implementation method, which we call the chaining optimization methodology, implicitly performs the π process of the f-function while minimizing memory access to the internal state of SHA-3. Through this, it achieves up to 26.1% performance improvement compared to the previous implementation in an AVR microcontroller and reduces the performance gap with the SHA-2 family to the maximum. Finally, we apply our SHA-3 implementation in Hash_Deterministic Random Bit Generator (Hash_DRBG), one of the upper algorithms of a hash function, to prove the applicability of our chaining optimization methodology on 8-bit AVR MCUs.

Using a hybrid content-based and behaviour-based featuring approach in a parallel environment to detect fake reviews

Abstract The financial impact of positive reviews has prompted some fraudulent sellers to generate fake product reviews for either promoting their products or discrediting competing products. Many e-commerce portals have implemented measures to detect such fake reviews, and these measures require excellent detectors to be effective. In this work, we propose 133 unique features from the combination of content and behaviour-based features to detect fake reviews using machine learning classifiers. Preliminary results show that these features can provide good results for all datasets tested. Detailed analysis of the results, however, reveals the existence of class imbalance issues for two of the bigger datasets - there is a high imbalance between the accuracies of different classes (e.g., 7.73% for the fake class and 99.3% for the genuine class using a Multilayer Perceptron classifier). We therefore introduce two sampling methods that can improve the accuracy of the fake review class on balanced datasets. The accuracies can be improved to a maximum of 89% for both random under and over-sampling on Convolutional Neural Networks. Additionally, we propose a parallel cross-validation method that can speed up the validation process in a parallel environment.

Efficient stress‐constrained topology optimization using inexact design sensitivities

AbstractAn efficient computational approach to stress‐constrained topology optimization is presented. Using a multigrid‐preconditioned Krylov solver for the state and adjoint problems, the number of Krylov iterations is reduced significantly by enforcing early termination of the iterative solves. The criterion for early termination is based on the convergence of the design sensitivities with respect to Krylov iterations. Consequently, the progress of optimization is not affected by the inexact resolution of the state and adjoint problems. The proposed approach is demonstrated on several design problems that possess different characteristics of the maximum stress. Optimization results obtained with early termination show very good agreement with those obtained with an accurate direct solver—in terms of objective value, constrained maximum stress and overall number of design cycles. Savings of 80% in the number of Krylov iterations are achieved, compared to the number of iterations required to satisfy the force residual convergence criterion to a common tolerance. The approach is directly applicable to high‐resolution problems that are solved on parallel computational environments. A MATLAB code for reproducing the results is freely available at https://github.com/odedamir/topopt‐stress‐inexact‐sensitivities

Parallel Newton–Chebyshev polynomial preconditioners for the conjugate gradient method

In this note, we exploit polynomial preconditioners for the conjugate gradient method to solve large symmetric positive definite linear systems in a parallel environment. We put in connection a specialized Newton method to solve the matrix equation X−1 = A and the Chebyshev polynomials for preconditioning. We propose a simple modification of one parameter which avoids clustering of extremal eigenvalues in order to speed-up convergence. We provide results on very large matrices (up to 8.6 billion unknowns) in a parallel environment showing the efficiency of the proposed class of preconditioners.

Tensor Decomposition and High-Performance Computing for Solving High-Dimensional Stochastic Control System Numerically

The paper presents a numerical method for solving a class of high-dimensional stochastic control systems based on tensor decomposition and parallel computing. The HJB solution provides a globally optimal controller to the associated dynamical system. Variable substitution is used to simplify the nonlinear HJB equation. The curse of dimensionality is avoided by representing the HJB equation using separated representation. Alternating least squares (ALS) is used to reduced the separation rank. The experiment is conducted and the numerical solution is obtained. A high-performance algorithm is designed to reduce the separation rank in the parallel environment, solving the high-dimensional HJB equation with high efficiency.

Direct numerical simulation of incompressible flows on parallel Octree grids

We introduce an approach for solving the incompressible Navier-Stokes equations on a forest of Octree grids in a parallel environment. The methodology uses the p4est library of Burstedde et al. (2011) [15] for the construction and the handling of forests of Octree meshes on massively parallel distributed machines and the framework of Mirzadeh et al. (2016) [54] for the discretizations on Octree data structures. We introduce relevant additional parallel algorithms and provide performance analyses for individual building bricks and for the full solver. We demonstrate strong scaling for the solver up to 32,768 cores for a problem involving O(6.1×108) computational cells. We illustrate the dynamic adaptive capabilities of our approach by simulating flows past a stationary sphere, flows due to an oscillatory sphere in a closed box and transport of a passive scalar. Without sacrificing accuracy nor spatial resolution in regions of interest, our approach successfully reduces the number of computational cells to (at most) a few percents of uniform grids with equivalent resolution. We also perform a numerical simulation of the turbulent flow in a superhydrophobic channel with unparalleled wall grid resolution in the streamwise and spanwise directions.

Padyak V. Istoriia karpatorusyns′koho natsional′noho teatra y dramaturgiï. Vysokoshkols′kyi uchebnyk. Priashiv: Vydavatel′stvo Priashivs′koho univerziteta, 2018. 344 s.

This article presents the textbook of the famous Transcarpathian and Slovak Rusinist Valery Padyak Istoriia karpatorusyns′koho natsional′noho teatra y dramaturgiï [History of the Carpatho-Rusyn National theatre and drama]. The Rusyns (more precisely, the Carpatho-Rusyns) are the autochthonous East Slavic population of the Carpathian region: Ukrainian Transcarpathia and the adjacent regions of Poland, Slovakia, Hungary, and Romania. There is also a Rusyn diaspora present in Serbia, Croatia, the USA, Canada, and other states. The number of Rusyns in the world is a controversial issue, one reason for this being their polyvariant identification. At present, a significant proportion of ethnic Rusyns consider themselves representatives of an independent people, who are not Ukrainian or Russian. Padyak, a staunch supporter of the Rusyn ethno-national identity and at the same time a serious academic scientist, made an attempt to trace the development of drama and theatre in the Rusyn environment in parallel with the processes of Rusyn national self-determination. The author traces the evolution of folk theatre performances to the modern Rusyn national theatre, which is actively being developed in different states with Rusyn inhabitants. V. Padyak pays increased attention to theater and drama in the second half of the nineteenth and early twentieth centuries, as well as during the period when Subcarpathian Rus (Transcarpathia) entered the structure of interwar Czechoslovakia and Horthy Hungary. In contrast, the author hardly examines post-war theater life in the Transcarpathian region, believing that its study is more appropriate in the context of Ukrainian, rather than Rusyn, studies. This textbook by V. Padyak, addressed primarily to Rusynist students at the University of Prešov (Slovakia), is of undoubted interest to anyone interested in the history and culture of the Rusyns.

A strong reinforcement parallel implementation of k-means algorithm using message passing interface

In Data analysis process clustering is one of the standard methods, which is used in many area such as pattern recognition and image segmentation and statistics, bioinformatics etc. Clustering have several algorithms in that most well know simplest algorithm is K-means, because of its simplicity, empirical success and efficiency. This present reality applications produce tremendous volumes of data, subsequently, how to productively deal with these data in a significant mining task has been a difficult and huge issue. Likewise Message passing Interface (MPI) as a Programming model for increases the scalability, performance and execution speed. Enthused by this, MPI with strong reinforcement parallel K- means clustering algorithm is implemented in this paper. The efficiency of the strong reinforcement K means clustering algorithm is improved in parallel environment by implementing in MPI methodology. In this paper performance of the clustering the data using K- means is compared between sequentially run of K- means algorithm and parallel run of K-means algorithm in Message passing interface architecture in terms of overhead cost and execution.