Applications In Parallel Computing Research Articles

Speculative computing for AAFM solutions in large-scale product configurations.

Parallel computing is a current algorithmic approach to looking for efficient solutions; that is, to define a set of processes in charge of performing at the same time the same task. Advances in hardware permit the massification of accessibility to and applications of parallel computing. Nonetheless, some algorithms include steps that require or depend on the results of other steps that cannot be parallelized. Speculative computing allows parallelizing those tasks and reviewing different execution flows, which can involve executing invalid steps. Speculative computing solutions should reduce those invalid flows. Product configuration refers to selecting features from a set of available options respecting some configuration constraints; a not complex task for small configurations and models, but a complex one for large-scale scenarios. This article exemplifies a videogame product line feature model and a few configurations, valid and non-valid, respectively. Configuring products of large-scale feature models is a complex and time-demanding task requiring algorithmic solutions. Hence, parallel solutions are highly desired to assist the feature model product configuration tasks. Existing solutions follow a sequential computing approach and include steps that depend on others that cannot be parallelized at all, where the speculative computing approach is necessary. This article describes traditional sequential solutions for conflict detection and diagnosis, two relevant tasks in the automated analysis of feature models, and how to define their speculative parallel version, highlighting their computing improvements. Given the current parallel computing world, we remark on the advantages and current applicability of speculative computing for producing faster algorithmic solutions.

Multi-core shared tree based MP2MP RWA algorithms in large scale and multi-domain optical networks

The increasing demands of bandwidth-intensive parallel computing and collaborative applications, efficient service provisioning to support multipoint to multipoint (MP2MP) communications have attracted increasing attention. However, with the development of larger-scale and multi-domain optical networks, MP2MP RWA are introduced optimal domain sequence selection and core nodes belong to which domains problems which cannot be tackled by the conventional algorithms proposed aim at a signal domain. In this paper, we proposed a multi-core node shared multicast tree heuristic algorithm (MCSMT) which calculation could be parallelized, delay and minimal cost constrained for multi-domain optical networks. It could realize the accurate calculation of minimum number of cores and the hosted domains of the cores. The source and destination nodes were added in different shared trees respectively with delay constrained algorithms according to specific QoS selection strategy. At the same time, according to more complicated MP2MP algorithms we propose a PCE-cloud based control architecture for optical networks by applying the cloud computing technology (e.g. virtualization and parallel computing) to reform the control plane for improving system reliability, intelligence and maximizing resource utilization. The performances of the proposed heuristic with regard to the number of multi-core nodes, wavelength channel occupied and MP2MP setup latency are compared. In addition, the performance about path computing latency for PCE-cloud control architecture is compared with conversional control architecture.

Programming and monitoring surface-confined DNA computing

DNA-based molecular computing has evolved to encompass a diverse range of functions, demonstrating substantial promise for both highly parallel computing and various biomedical applications. Recent advances in DNA computing systems based on surface reactions have demonstrated improved levels of specificity and computational speed compared to their solution-based counterparts that depend on three-dimensional molecular collisions. Herein, computational biomolecular interactions confined by various surfaces such as DNA origamis, nanoparticles, lipid membranes and chips are systematically reviewed, along with their manipulation methodologies. Monitoring techniques and applications for these surface-based computing systems are also described. The advantages and challenges of surface-confined DNA computing are discussed.

Research on the Application and Performance Optimization of GPU Parallel Computing in Concrete Temperature Control Simulation

With the development of engineering technology, engineering has higher requirements for the accuracy and the scale of simulation calculation. The computational efficiency of traditional serial programs cannot meet the requirements of engineering. Therefore, reducing the calculation time of the temperature control simulation program has important engineering significance for real-time simulation of temperature field and stress field, and then adopting more reasonable temperature control and crack prevention measures. GPU parallel computing is introduced into the temperature control simulation program of massive concrete to solve this problem and the optimization is carried out. Considering factors such as GPU clock rate, number of cores, parallel overhead and Parallel Region, the improved GPU parallel algorithm analysis indicator formula is proposed. It makes up for the shortcomings of traditional formulas that focus only on time. According to this formula, when there are enough threads, the parallel effect is limited by the size of the parallel domain, and when the parallel domain is large enough, the efficiency is limited by the parallel overhead and the clock rate. This paper studies the optimal Kernel execution configuration. Shared memory is utilized to improve memory access efficiency by 155%. After solving the problem of bank conflicts, an accelerate rate of 437.5× was realized in the subroutine of the matrix transpose of the solver. The asynchronous parallel of data access and logical operation is realized on GPU by using CUDA Stream, which can overlap part of the data access time. On the basis of GPU parallelism, asynchronous parallelism can double the computing efficiency. Compared with the serial program, the accelerate rate of inner product matrix multiplication of the GPU asynchronous parallel program is 61.42×. This study further proposed a theoretical formula of data access overlap rate to guide the selection of the number of CUDA streams to achieve the optimal computing conditions. The GPU parallel program compiled and optimized by the CUDA Fortran platform can effectively improve the computational efficiency of the simulation program for concrete temperature control, and better serve engineering computing.

Preface

2023 the 7th International Conference on Fluid Mechanics and Industrial Applications (FMIA 2023) included original and peer-reviewed research papers and held on May 27-28, 2023 in Taiyuan, China. FMIA 2023 is co-organized by Northeast Petroleum University, Huazhong University of Science and Technology, Chongqing University, and sponsored by China Wanxiang Group Research Institute, Asian Union of Information Technology. It was attended by academicians, researchers, entrepreneurs, government agencies and policy-makers, postgraduates.FMIA is an annual international conference devoted to the discussion of recent developments and applications of parallel computing in the field of fluid mechanics, CFD, fluid power, fluid flow and related disciplines. The field of fluid mechanics is vast and covers numerous and diverse applications. The conference covered a wide range of topics, including basic formulations and their computer modelling as well as the relationship between experimental and analytical results. The meeting provided a forum for discussing new work on fluid mechanics and in particular for promoting the interchange of novel ideas and the presentations of the latest developments in this field. This conference would also provide an ideal environment to develop new collaborations and meet experts on the fundamentals, applications, and products of the mentioned fields.This two-day conference was organized in technical association with the Asian Union of Information Technology and included invited keynote talks and oral paper presentations from both academia and industry. The topics covered in this FMIA 2022 are: 1. Aerodynamics; 2. Thermodynamics and Heat Transfer; 3. Fluid Theory and Related Algorithms. Each keynote speakers lasted for 30 minutes, including 5 minutes for Q&A. and for other sessions, there was 20 minutes for each presentation, including 5 minutes for Q&A. The book is useful to professionals and scientists involved or interested in fluid mechanics and industrial applications. Hopefully, all participants and other interested readers would benefit scientifically from the proceedings and also find it stimulating in the process.We would like to thank all the author submitted papers to this FMIA 2023 and thank all the reviewers for their time and effort in reviewing articles. Especially we would like to thank the organizing committee for their valuable advices in the organization and helpful peer review of the papers.Conference Organizing Committee,Wuhan, ChinaList of Committees are available in this pdf.

A Real-Time Fluid Dynamic Air Brake Model for Long Heavy Haul Trains

AbstractPractical real-time fluid dynamic air brake models for long heavy haul trains have not been reported in open literature. Based on a previous work titled “Railway Air Brake Model and Parallel Computing Scheme” in the same journal, this paper proposed upgrades to the previous model and achieved the real-time feature. The real-time contributing factors included a new brake cylinder model, a new scheme for updating characteristics, and the application of parallel computing. Results show that, for a 150-wagon train emergency brake simulation, the computing speed was improved from 5.26 times slower than real-time to 8.6 times faster than real-time. The three contributions improved the computing speed by 8.8, 1.8, and 2.9 times faster than the baseline models, respectively.

A Peano-based space-filling surface of fractal dimension three

Although space-filling curves are well known, and have many applications in parallel computing and data mapping, there is a need for a space-filling surface that is a continuous mapping from two-dimensional domain onto the unit cube. This would allow efficient implementation of a 2D problem on parallel processors which are interconnected into a 3D grid. Such a surface is presented in this paper, which uses Hilbert’s geometric approach to generate a mapping from a unit square to a triangular prism. Using two such mappings we can create a mapping from a rectangle to a unit cube. To culminate, we use the mapping to produce a continuous omnichromatic picture, that is, one for which the colors change continuously, and under sufficient resolution, contains every possible RGB value.

Parallel computations in problems of reconstruction of distorted images in spatial-spectral form

The object of the study is the image processing process in preparation for data transmission, as well as subsequent recovery. The subject of the study is the application of parallel computing in image processing tasks. The purpose of the article is to study the method of image reconstruction with correction of distortion of the transmission vector based on the input of a value in the trend of its neighbors. The relevance of this topic is determined by the need to effectively perform the operations preceding and following the transmission over the communication channel. During the experimental part, the results were obtained in the form of execution time values with sequential execution and using parallel calculations, which gave the expected increase in speed-up. Since the image consists of unrelated parts, it can be successfully processed by applying data parallelization.

Energy-efficient task allocation for reliable parallel computation of cluster-based wireless sensor network in edge computing

To efficiently complete a complex computation task, the complex task should be decomposed into sub-computation tasks that run parallel in edge computing. Wireless Sensor Network (WSN) is a typical application of parallel computation. To achieve highly reliable parallel computation for wireless sensor network, the network's lifetime needs to be extended. Therefore, a proper task allocation strategy is needed to reduce the energy consumption and balance the load of the network. This paper proposes a task model and a cluster-based WSN model in edge computing. In our model, different tasks require different types of resources and different sensors provide different types of resources, so our model is heterogeneous, which makes the model more practical. Then we propose a task allocation algorithm that combines the Genetic Algorithm (GA) and the Ant Colony Optimization (ACO) algorithm. The algorithm concentrates on energy conservation and load balancing so that the lifetime of the network can be extended. The experimental result shows the algorithm's effectiveness and advantages in energy conservation and load balancing.

Nonlinear Computational Edge Detection Metalens

AbstractOptical image processing and computing systems provide supreme information processing rates by utilizing parallel optical architectures. Existing optical analog processing techniques require multiple devices for projecting images and executing computations. In addition, those devices are typically limited to linear operations due to the time‐invariant optical responses of the building materials. In this work, a single metalens with an illumination intensity dependent coherent transfer function (CTF) is proposed and experimentally demonstrated, which performs varying computed imaging without requiring any additional optical components. The metalens consisting of nanoantenna structures with a static geometric phase and a nonlinear metallic quantum well layer offering an intensity‐dependent dynamic phase results in a continuously tunable CTF. The approach allows for a weighted summation of two designed functions based on the metalens design, which potentially enables all optical computations of complex functions. The nonlinear metalens may lead to important applications in optical neural networks and parallel analog computing.

Preface

2022 the 6th International Conference on Fluid Mechanics and Industrial Applications (FMIA 2022) included original and peer-reviewed research papers and held on April 23-24, 2022 in Taiyuan, China. This conference was sponsored by Asian Union of Information Technology, and was attended by academicians, researchers, entrepreneurs, government agencies and policy-makers, postgraduates.FMIA is an annual international conference devoted to the discussion of recent developments and applications of parallel computing in the field of fluid mechanics, CFD, fluid power, fluid flow and related disciplines. The field of fluid mechanics is vast and covers numerous and diverse applications. The conference covered a wide range of topics, including basic formulations and their computer modelling as well as the relationship between experimental and analytical results. The meeting provided a forum for discussing new work on fluid mechanics and in particular for promoting the interchange of novel ideas and the presentations of the latest developments in this field. This conference would also provide an ideal environment to develop new collaborations and meet experts on the fundamentals, applications, and products of the mentioned fields.Committees are available in this pdf

Development and verification of Geant4-based parallel computing Monte Carlo simulations for nuclear logging applications

Monte Carlo is one of the most common methods to solve radiation transport problems. However, due to its statistical nature, Monte Carlo is time-consuming when applied to large-scale problems. Therefore, parallel computing, a powerful tool in accelerating Monte Carlo calculations, has been receiving increasing attention along with new developments of computing hardware in recent years. This work documents the application of parallel computing in Geant4 simulations pertaining to nuclear logging tools. A user-specific interface is developed to enable Geant4 multithreading functionality for two nuclear logging models and the parallel Monte Carlo performance is evaluated on a designated HPC (high performance computing) platform. Upon the validation of the parallel-mode simulation, the performance of the Geant4-based simulation in multi-threaded mode (G4-MT) and with the G4-MPI native interface to MPI are compared to obtain the best combination of threads and processes on each node. Strong and weak scalabilities on multiple threads are then investigated by computing the speedup and the parallel efficiency of the simulation. The results show that combining multi-threaded and MPI-based execution can significantly reduce execution time in parallel mode. And the paper documents the improvement of Monte Carlo simulation efficiency by optimizing the configuration of computational resources for parallel execution while keeping close-to-linear acceleration.

MCDM Filter with Pareto Parallel Implementation in Shared Memory Environment

Nowadays, multi-criteria decision-making (MCDM) methods are often used to solve problems involving large data sets, especially with the advent of the big data age. In such a context, the multi-criteria decision-making methods theoretically can be used but technically are not effificient in terms of the treatment time. Indeed, the majority of commercial or even experimental multi-criteria decision support tools always have limits in terms of the number of alternatives and the number of criteria to be retained in the decision-making process, which presents a computational challenge to relieve. This present paper discusses the application of parallel computation to meet this challenge and make the application of MCDM methods possible in the presence of a big number of alternatives and criteria. More precisely, the main objective of this work is to provide a parallel fifiltering mechanism that can be executed even on accessible personal computers and offering a short and reasonable response time. The introduction of a fifilter as a fifirst step in the decision-making process consists in retaining, as alternatives to be treated by the MCDM method, and by parallel processing only the Pareto solutions. To achieve this objective, we propose a parallel computing approach deploying the Open MP (Open Multi-Processing) paradigm on a shared memory environment to fifind Pareto solutions. To prove the effectiveness of the proposed approach for problems with large dimensionality, several numerical examples with different dimensions will be examined.

A virtual procedure for real-time monitoring of intervisibility between conflicting agents at intersections using point cloud and trajectory data

A new procedure is developed for effectively monitoring intervisibility between conflicting agents in real-time at intersections. Dense light detection and ranging (Lidar) point clouds and time-stamped trajectory data are used to model static intersection environment and emulate traffic participants’ dynamic motion states, respectively. The proposed procedure reads trajectory data in sequence according to their timestamps. An agent-based approach that enables the application of multi-core parallel computing is applied to estimate conflict points and identify conflicting agents in pairs. Then, a linear elevation array is created from the Lidar point cloud data, based on which the elevation of each trajectory point is obtained in a real-time manner. Meanwhile, three-dimensional bounding cuboids are generated at each path point to represent digital twins of agents. Once a pair of conflicting agents are identified, a hybrid approach is triggered to examine whether the agents’ line-of-sights are occluded by either static (e.g., tree trunk) or dynamic obstacles (e.g., cars). Accordingly, virtual warning signals can be generated. The effectiveness of the procedure is demonstrated through controlled experiments. The procedure was also tested in two virtual scenarios. The mean processing time at each frame is less than 0.1 s as achieved with limited computational power. With implementation of parallel computing technique, processing time is not sensitive to number of agents within the intersection. In addition, by enabling the outputs of virtual warning signals, spatial distribution of conflict points, and individual conflict-related time series data, the procedure shall help provide substantial insights into intersection safety.

The Maximum k-Colorable Subgraph Problem and Related Problems

The maximum k-colorable subgraph (MkCS) problem is to find an induced k-colorable subgraph with maximum cardinality in a given graph. This paper is an in-depth analysis of the MkCS problem that considers various semidefinite programming relaxations, including their theoretical and numerical comparisons. To simplify these relaxations, we exploit the symmetry arising from permuting the colors, as well as the symmetry of the given graphs when applicable. We also show how to exploit invariance under permutations of the subsets for other partition problems and how to use the MkCS problem to derive bounds on the chromatic number of a graph. Our numerical results verify that the proposed relaxations provide strong bounds for the MkCS problem and that those outperform existing bounds for most of the test instances. Summary of Contribution: The maximum k-colorable subgraph (MkCS) problem is to find an induced k-colorable subgraph with maximum cardinality in a given graph. The MkCS problem has a number of applications, such as channel assignment in spectrum sharing networks (e.g., Wi-Fi or cellular), very-large-scale integration design, human genetic research, and so on. The MkCS problem is also related to several other optimization problems, including the graph partition problem and the max-k-cut problem. The two mentioned problems have applications in parallel computing, network partitioning, floor planning, and so on. This paper is an in-depth analysis of the MkCS problem that considers various semidefinite programming relaxations, including their theoretical and numerical comparisons. Further, our analysis relates the MkCS results with the stable set and the chromatic number problems. We provide extended numerical results that verify that the proposed bounding approaches provide strong bounds for the MkCS problem and that those outperform existing bounds for most of the test instances. Moreover, our lower bounds on the chromatic number of a graph are competitive with existing bounds in the literature.

Global optimization algorithms for particle swarm optimization to the derivation of horizontal multilayered soil models considering the frequency dependence of soil parameters

AbstractThe frequency dependence of soil parameters has been confirmed by many experiments. Most of them are done by the measurement of soil sample in laboratories. Although some studies are based on field measurement, only homogeneous ground or two‐layer model are considered. The study of frequency dependence of soil parameters with considering multilayered model is lacking. The frequency dependence of the horizontally multilayered soil model is studied by the inversion of soil parameters in the frequency domain. The inversion of frequency domain soil parameters is translated into an optimization problem. The model parameters are determined by spectral induced polarization data, and particle swarm optimization is applied to optimizing model parameters. The performances of three different methods to trap particles inside boundaries are compared. In order to improve the computational efficiency of the inversion program, parallel computing is combined with particle swarm optimization to solve the optimization problem. The execution time of the developed algorithm before and after the application of parallel computing is compared. The performances of other two optimization algorithms, simulated annealing and genetic algorithm, are compared with that of particle swarm optimization. So it would be more clear which optimization algorithm should be selected among these three commonly used algorithms when dealing with the inversion problem of soil parameters. The accuracy of the proposed method is verified by a numerical simulation experiment. Then, this method is applied to interpreting field data, and the frequency dependence of soil parameters can be observed from the inversion results.

Preface

2021 the 5th International Conference on Fluid Mechanics and Industrial Applications (FMIA 2021) included original and peer-reviewed research papers and held on June 26-27, 2021 in Taiyuan, China. This conference was sponsored by Asian Union of Information Technology, and was attended by academicians, researchers, entrepreneurs, government agencies and policy-makers, postgraduates.FMIA is an annual international conference devoted to the discussion of recent developments and applications of parallel computing in the field of fluid mechanics, CFD, fluid power, fluid flow and related disciplines. The field of fluid mechanics is vast and covers numerous and diverse applications. The conference covered a wide range of topics, including basic formulations and their computer modelling as well as the relationship between experimental and analytical results. The meeting provided a forum for discussing new work on fluid mechanics and in particular for promoting the interchange of novel ideas and the presentations of the latest developments in this field. This conference would also provide an ideal environment to develop new collaborations and meet experts on the fundamentals, applications, and products of the mentioned fields.FMIA 2021 received 118 manuscripts, and 78 submissions had been accepted by our reviewers. By submitting a paper to FMIA 2021, the authors agreed to the review process and understood that papers would undergo a peer-review process. Manuscripts were reviewed by appropriately qualified experts in the field selected by the conference committee, who took detailed comments and-if the submission was accepted-the authors would submit a revised version that took into account this feedback. All papers were reviewed using a double-blind review process: authors declared their names and affiliations in the manuscript for the reviewers to see, but reviewers did not know each other’s identities, nor did the authors receive information about who had reviewed their manuscript. The committees of FMIA 2021 invested great efforts in reviewing the papers submitted to the conference and organizing the sessions to enable the participants to gain maximum benefit.Conference Organizing CommitteeWuhan, ChinaCommittees, Chairmen, Members, and this titles are available in this pdf

Application of parallel computing in finite element analysis of constructions

To optimize finite elements computational processes of the “MIRELA +” computing complex, parallel programming algorithms have been developed for creating stiffness matrices of finite elements and calculating the stress-strain. The parallel data processing is implemented using OpenMP library. On the basis of computational methods of experiments, the use of parallel computing algorithms at the beginning of solving problems with traditional finite element algorithms for large-dimensional grids has been established.

A parallel computing-based and spatially stepwise strategy for constraining a semi-distributed hydrological model with streamflow observations and satellite-based evapotranspiration

Satellite-based products can provide valuable information for model calibrations and evaluations. However, effectively and efficiently constraining hydrological models with both satellite-based information and streamflow measurements remains a challenge. Here, a parallel computing-based and spatially stepwise strategy that enables separate optimizations of different objective functions in a spatially distributed and parallel manner was proposed for model calibration with streamflow observations and satellite-based evapotranspiration (ET). The new calibration strategy (M5) was tested with the Soil and Water Assessment Tool (SWAT) model in the upper Heihe River basin of China, a mountainous watershed on the northeastern Qinghai-Tibet Plateau. The performance of M5 was evaluated and compared with two single-variable calibration strategies, i.e., streamflow-only calibration and ET-only calibration, and with two multivariate calibration methods, i.e., joint calibration and stepwise calibration. Results indicate that M5 achieves the best model performance among the five calibration strategies in reproducing temporal variations of streamflow and ET. Moreover, M5 improves the simulation of the spatial pattern of ET and attains a higher spatial efficiency than the other calibration strategies. M5 also exhibits a higher computational efficiency, with a magnitude up to 2 times greater than the other calibration strategies, due to the application of parallel computing. Further analysis demonstrates that the new calibration strategy can lead to a synergic relationship between the simulation accuracy of streamflow and ET, underscoring the added value of satellite-based products for model calibrations.

Packing internally disjoint Steiner trees to compute the κ3-connectivity in augmented cubes

Given a connected graph G and S⊆V(G) with |S|≥2, a tree T in G is called an S-Steiner tree (or S-tree for short) if S⊆V(T). Two S-trees T1 and T2 are internally disjoint if E(T1)∩E(T2)=∅ and V(T1)∩V(T2)=S. The packing number of internally disjoint S-trees, denoted as κG(S), is the maximum size of a set of internally disjoint S-trees in G. For an integer k≥2, the generalized k-connectivity (abbr. κk-connectivity) of a graph G is defined as κk(G)=min{κG(S)|S⊆V(G) and |S|=k}. The n-dimensional augmented cube, denoted as AQn, is an important variant of the hypercube that possesses several desired topology properties such as diverse embedding schemes in applications of parallel computing. In this paper, we focus on the study of constructing internally disjoint S-trees with |S|=3 in AQn. As a result, we completely determine the κ3-connectivity of AQn as follows: κ3(AQ4)=5 and κ3(AQn)=2n−2 for n=3 or n≥5.