MATLAB Parallel Research Articles

PARALLEL PERFORMANCE OF THE EXPLICIT FDTD METHODS APPLIED ON 3D ACOUSTIC WAVE EQUATION

The 3D acoustic wave equation is a second-order linear hyperbolic partial differential equation. It is widely studied in acoustics, fluid dynamics, electromagnetism, and seismology within both science and engineering fields. Among the various numerical methods, the finite difference method is considered to be the simpler to understand and easy to implement. Based on the discretize schemes finite difference time domain method described by the explicit scheme in which spatial second order derivatives are evaluated at the previous time step. The solution of 3D acoustic wave equation may be required on the high-resolution mesh points consequently more computational resources are required. In this study parallel algorithm for the numerical solution of 3D acoustic wave equation is proposed and designed by using data parallel approach and message passing schemes. The algorithm is implemented on shared memory parallel system using MATLAB parallel computing mode. The parallel performance of the designed algorithm is analyzed on different mesh sizes and time steps. It is revealed that the proposed algorithm may reduce computational time up to 3 times as compared to sequential solution algorithm. The proposed parallel algorithm remains more efficient on workers while for the efficiency of the algorithm drops because of the high communication time. The results of the proposed research could be utilized in the large-scale simulations of 3D acoustic wave equations and may enable to simulate the acoustic wave pressure at more refined meshes on high performance computing systems.

Driving Control Strategy and Specification Optimization for All-Wheel-Drive Electric Vehicle System with a Two-Speed Transmission

Equipping electric vehicles with a two-speed gearbox allows for achieving high torque and maximum speed through appropriate gear ratio adjustments. Additionally, tuning motor operating points to efficient zones, considering energy efficiency, significantly enhances the vehicle’s overall performance. This paper presents an AWD system configuration method, integrating a two-speed transmission to improve energy efficiency and driving performance through front and rear motor torque distribution and powertrain specification optimization. Based on vehicle simulations conducted using MATLAB/Simulink, a strategy for torque distribution between the front/rear axles was established using fuzzy logic, considering energy efficiency and driving stability. Furthermore, a multi-objective optimization was performed using a surrogate model trained through MATLAB parallel simulations. When the optimization results were applied to various vehicle specifications, it was observed that energy efficiency was improved, and acceleration performance was increased compared to a baseline vehicle without optimization.

A comparative assessment of OMP and MATLAB for parallel computation

The prime goal of parallel computing is the simultaneous parallel execution of several program instructions. Consequently, to accomplish this, the program should be divided into independent sets so that each processor can execute its program part concurrently with the other processors. This study compares OMP and MATLAB, two important parallel computing simulation tools, through the use of a dense matrix multiplication technique. The results showed that OMP outperformed the MATLAB parallel environment by over 8 times in sequential execution and 6 times in parallel execution. From this proposed method, it was also observed that OMP with an even slower processor performs much better than MATLAB with a higher processor. Thus, the present analysis indicates that OMP is a superior environment for parallel computing and should be preferred over parallel MATLAB.

MatNLI: An open-source MATLAB-based solver for the non-linear inversion in elastography

Elastography has emerged as one of the most promising non-invasive clinical tools. In elastography, a stiffness map or elastogram is generated by solving an inverse problem of elasticity utilizing the tissue motion data acquired using magnetic resonance imaging or ultrasound. Among various inverse algorithms devoted to elastography, non-linear inversion coupled with the finite element method has demonstrated excellent applicability in extracting complex physics of the tissue. The development and implementation of such an inverse algorithm are challenging and often unavailable to clinicians. In the present work, we offer an open-source parallel MATLAB implementation of an efficient non-linear inversion algorithm based on the finite element method for different isotropic material models, viz., linear elastic and viscoelastic materials in the regime of compressible and nearly incompressible materials. Additionally, the framework has been extended to account for anisotropy by assuming transversely isotropic material. For the optimization module, the gradient of the objective function to the model parameters has been computed using the Adjoint method. Different case studies involving smooth variations and piece-wise discontinuities in the material property distribution are explored, and the efficacy of the inversion algorithm in reconstructing the stiffness map is discussed. In addition, noise is added to the synthetic data to depict a realistic setup, i.e., to prevent inverse crimes, and enhance the numerical stability and robustness of the current implementation. The present framework with general-purpose computer implementation could be beneficial for academic and clinical uses and may aid researchers in strengthening their existing frameworks and developing new algorithmic ideas.

Technical note: MAMBA-Multi-pAradigM voxel-Based Analysis: A computational cookbot.

Voxel-Based (VB) analysis embraces a multifaceted ensemble of sophisticated techniques, lying at the boundary between image processing and statistical modeling, that allow for a frequentist inference of pathophysiological properties anchored to an anatomical reference. VB methods has been widely adopted in neuroimaging studies and, more recently, they are gaining momentum in radiation oncology research. However, the price for the power of VB analysis is the complexity of the underlying mathematics and algorithms. In this paper, we present the Multi-pAradigM voxel-Based Analysis (MAMBA) toolbox, which is intended for a flexible application of VB analysis in a wide variety of scenarios in medical imaging and radiation oncology. The MAMBA toolbox is implemented in Matlab. It provides open-source functions to compute VB statistical models of the input data, according to a great variety of regression schemes, and to derive VB maps of the observed significance level, performing a non-parametric permutation inference. The toolbox allows for including VB and global outcomes, as well as an arbitrary amount of VB and global Explanatory Variables (EVs). In addition, the Matlab Parallel Computing Toolbox is exploited to take advantage of the perfect parallelizability of most workloads. The use of MAMBA was demonstrated by means of several realistic examples on a synthetic dataset mimicking a radiation oncology scenario. MAMBA is an open-source toolbox, freely available for academic and non-commercial purposes. It is designed to make state-of-the-art VB analysis accessible to research scientists without the programming resources needed to build from scratch their own software solutions. At the same time, the source code is handed out for more experienced users to complement their own tools, also customizing user-defined models. MAMBA guarantees high generality and flexibility in the design of the statistical models, significantly expanding on the features of available free tools for VB analysis. The presented toolbox aims at increasing the reach of VB studies as well as the sharing of research results.

ForkJoinPcc Algorithm for Computing the Pcc Matrix in Gene Co-Expression Networks

High-throughput microarrays contain a huge number of genes. Determining the relationships between all these genes is a time-consuming computation. In this paper, the authors provide a parallel algorithm for finding the Pearson’s correlation coefficient between genes measured in the Affymetrix microarrays. The main idea in the proposed algorithm, ForkJoinPcc, mimics the well-known parallel programming model: the fork–join model. The parallel MATLAB APIs have been employed and evaluated on shared or distributed multiprocessing systems. Two performance metrics—the processing and communication times—have been used to assess the performance of the ForkJoinPcc. The experimental results reveal that the ForkJoinPcc algorithm achieves a substantial speedup on the cluster platform of 62× compared with a 3.8× speedup on the multicore platform.

Acceleration strategies for Tridimensional Coupled hydromechanical problems based on CPU and GPU programming in MATLAB.

Large-scale fluid flow in porous media demands intense computations and occurs in the most diverse applications, including groundwater flow and oil recovery. This article presents novel computational strategies applied to reservoir geomechanics. Advances are proposed for the efficient assembly of finite element matrices and the solution of linear systems using highly vectorized code in MATLAB. In the CPU version, element matrix assembly is performed using conventional vectorization procedures, based on two strategies: the explicit matrices, and the multidimensional products. Further assembly of the global sparse matrix is achieved using the native sparse function. For the GPU version, computation of the complete set of element matrices is performed with the same strategies as the CPU approach, using gpuArray structures and the native CUDA support provided by MATLAB Parallel Computing Toolbox. Solution of the resulting linear system in CPU and GPU versions is obtained with two strategies using a one-way approach: the native conjugate gradient solver (pcg), and the one provided by the Eigen library. A broad discussion is presented in a dedicated benchmark, where the different strategies using CPU and GPU are compared in processing time and memory requirements. These analyses present significant speedups over serial codes.

First- and second-order unconditionally stable direct discretization methods for multi-component Cahn–Hilliard system on surfaces

This paper proposes a first- and second-order unconditionally stable direct discretization method based on a surface mesh consisting of piecewise triangles and its dual-surface polygonal tessellation for solving the N-component Cahn–Hilliard system. We define the discretizations of the gradient, divergence, and Laplace–Beltrami operators on triangle surfaces. We prove that the proposed schemes, which combine a linearly stabilized splitting scheme, are unconditionally energy-stable. We also prove that our method satisfies the mass conservation. The proposed scheme is solved by the biconjugate gradient stabilized (BiCGSTAB) method, which can be straightforwardly applied to GPU-accelerated biconjugate gradient stabilized implementation by using the Matlab Parallel Computing Toolbox. Several numerical experiments are performed and confirm the accuracy, stability, and efficiency of our proposed algorithm.

Binary Particle Swarm Optimization Algorithm for Kidney Exchanges Acceleration using Parallel MATLAB

Binary Particle Swarm Optimization Algorithm for Kidney Exchanges Acceleration using Parallel MATLAB

ICN-Based Enhanced Cooperative Caching for Multimedia Streaming in Resource Constrained Vehicular Environment

Today, with the worldwide offer and rapid increment in multimedia applications on the web, the demands of users to get them accessed are also increasing prominently. The users in vehicular environment too expect efficient multimedia streaming while travelling on the road. However, the high mobility of vehicles as well as the limited transmission range of infrastructure components in IP based network provides low performance by offering high delay and additional network overhead. To provide better Quality of Experience (QoE) with high performance, Information Centric Networking (ICN) is blended with vehicular environment. Caching the content inside network nodes is inherent feature of ICN with various associated benefits such as low content retrieval delay, less network traffic, path reduction and so on. However, challenges still exists for caching the content due to resource constrained network environment (such as limited cache capacity, node battery) as well as for secure delivery of cached data. To solve these challenges and to enhance network performance, we propose a cooperative caching scheme in hierarchical network architecture that jointly considers cache location as well as combined content popularity and predicted future rating score while making caching decision. The proposed approach uses two layer hierarchical architecture where nodes in edge layer are divided into clusters. The proposed scheme uses modified Weighted Clustering Algorithms (WCA) for selection of cluster heads which are then used to decide cache location. A probability matrix is used to compute content caching probability which considers both popularity and predicted future rating of content. The proposed approach dynamically predict the user's preferences using non-negative matrix factorization (NMF) - a machine learning technique which eventually provides prediction of future rating. Based on the selection of both cache location and content to cache, the proposed scheme can effectively cache the content in the network. Further, to deal with the secure delivery of cached content, this work supports legitimate user authorization at edge nodes. The performance of the proposed scheme is evaluated in MATLAB parallel computing toolkit. The results prove significant caching improvement in terms of cache hit, hop reduction and average delay using our proposed scheme.

Parallel spectral element method for guided wave based structural health monitoring

Parallel implementation of the spectral element method is developed in which flat shell spectral elements are utilized for spatial domain representation. The implementation is realised by using Matlab Parallel Computing Toolbox and optimized for Graphics Processing Unit (GPU) computation. In this way, considerable computation speed-up can be achieved in comparison to computation on conventional processors. The implementation includes an interpolation of wavefield on a uniform grid. The method was tested on experimental data set available on the Open Guided Waves platform. The qualitative comparison was performed on full wavefield data, whereas quantitative comparison was made directly on signals of propagating Lamb waves registered by piezoelectric transducers. In both cases, good agreement between numerical and experimental results was achieved. The proposed method is particularly useful for structural health monitoring algorithms in which signal parameters are required such as wave velocity dependence on the angle of propagation. Moreover, it enables model–assisted damage size estimation in which the damage influence curve is estimated based on large numerical data sets and sparse experimental data. Due to relatively short computation times, large data sets can be generated and used for machine learning or other soft computing methods opening up new possibilities in health monitoring of metallic and composite structures.

Displacement Imaging During Focused Ultrasound Median Nerve Modulation: A Preliminary Study in Human Pain Sensation Mitigation.

Focused ultrasound (FUS)-based viscoelastic imaging techniques using high frame rate (HFR) ultrasound to track tissue displacement can be used for mechanistic monitoring of FUS neuromodulation. However, a majority of techniques avoid imaging during the active push transmit (interleaved or postpush acquisitions) to mitigate ultrasound interference, which leads to missing temporal information of ultrasound effects when FUS is being applied. Furthermore, critical for clinical translation, use of both axial steering and real-time (<1 s) capabilities for optimizing acoustic parameters for tissue engagement are largely missing. In this study, we describe a method of noninterleaved, single Vantage imaging displacement within an active FUS push with simultaneous axial steering and real-time capabilities using a single ultrasound acquisition machine. Results show that the pulse sequence can track micron-sized displacements using frame rates determined by the calculated time-of-flight (TOF), without interleaving the FUS pulses and imaging acquisition. Decimation by 3-7 frames increases signal-to-noise ratio (SNR) by 15.09±7.03 dB. Benchmarking tests of CUDA-optimized code show increase in processing speed of 35- and 300-fold in comparison with MATLAB parallel processing GPU and CPU functions, respectively, and we can estimate displacement from steered push beams ±10 mm from the geometric focus. Preliminary validation of displacement imaging in humans shows that the same driving pressures led to variable nerve engagement, demonstrating important feedback to improve transducer coupling, FUS incident angle, and targeting. Regarding the use of our technique for neuromodulation, we found that FUS altered thermal perception of thermal pain by 0.9643 units of pain ratings in a single trial. Additionally, 5 [Formula: see text] of nerve displacement was shown in on-target versus off-target sonications. The initial feasibility in healthy volunteers warrants further study for potential clinical translation of FUS for pain suppression.

Parallel Computing and Multicore Platform to Assess Electric Vehicle Hosting Capacity

The impact generated by the massive connection of electric vehicles (EVs) in the distribution networks needs to be evaluated through methodologies that consider the stochastic behavior of the EV connections. These methodologies must allow obtaining representative results of annual simulations, considering different charging scenarios, with high resolution of time steps. This kind of analysis implies a combinatorial explosion of case studies with a high computational cost. This article presents a methodology aimed at reducing the simulation time required to determine the EV hosting capacity (HC), understood as the maximum EV penetration level that a distribution network could host before affecting its operating limits, through the co-simulation of hardware and software. Monte Carlo methods and quasi-static time series simulation concepts are used to model the connection features of EVs. The simulation and implementation are developed using parallel computing, and MATLAB and OpenDSS software. The methodology is tested on the IEEE 123 nodes system which shows the dependence of the HC indicator with the location and type EV chargers. Finally, the benefits of parallel computing in the proposed methodology are exposed to get a significant reduction in the execution time to evaluate the HC in the IEEE 123 nodes system.

An electric power trading framework for smart residential community in smart cities

This study proposes a multi-agent-based framework for Peer-to-Peer (P2P) power trading in a locality electricity market (LEM) for self-interested smart residential prosumers. In LEM, prosumers may sell (buy) their excess generation (demand) at a profitable market prices compared to utility prices to achieve a win–win outcome. In LEM, three agents namely locality electricity trading system (LETS), utility and prosumer act together to achieve P2P power trading in a day-ahead market. LETS computes the internal market prices employing any one of the market-clearing mechanisms and broadcasts it to the prosumers. Prosumers optimise the generation-demand schedule for the next day using residential energy management and trading system to achieve minimum electricity bill. The performance of the proposed framework is validated through different case studies on a residential locality with ten prosumers. The simulation is carried out using MATLAB parallel computation tool box and the load data is collected from the residential locality of National Institute of Technology Tiruchirappalli, India. It is evident from the simulation results that all the participants are economically benefited by P2P power trading. It is also found that the SDR mechanism in P2P outperforms and reduces the locality electricity bill by 27–68% under different operating conditions.

Parallel Spatial Pyramid Match Kernel Algorithm for Object Recognition using a Cluster of Computers

This paper parallelizes the spatial pyramid match kernel (SPK) implementation. SPK is one of the most usable kernel methods, along with support vector machine classifier, with high accuracy in object recognition. MATLAB parallel computing toolbox has been used to parallelize SPK. In this implementation, MATLAB Message Passing Interface (MPI) functions and features included in the toolbox help us obtain good performance by two schemes of task-parallelization and dataparallelization models. Parallel SPK algorithm ran over a cluster of computers and achieved less run time. A speedup value equal to 13 is obtained for a configuration with up to 5 Quad processors.

Parallel computing of multi-contingency optimal power flow with transient stability constraints

To deal with the high dimensionality and computational density of the Optimal Power Flow model with Transient Stability Constraints (OTS), a credible criterion to determine transient stability is proposed based on swing curves of generator rotor and the characteristics of transient stability. With this method, the swing curves of all generator rotors will be independent one another. Therefore, when a parallel computing approach based on the MATLAB parallel toolbox is used to handle multi-contingency cases, the calculation speed is improved significantly. Finally, numerical simulations on three test systems including the NE-39 system, the IEEE 300-bus system, and 703-bus systems, show the effectiveness of the proposed method in reducing the computing time of OTS calculation.

Efficient Parallelization of Electromagnetic Field Computations on a Beowulf Cluster

In this paper, efficient high performance computation of electromagnetic field problems using a Beowulf cluster and MatLab® is presented. A cluster with one master node, eight identical computing nodes, and a Gigabit Ethernet (GbE) switch is firstly built. An open source Rocks toolkit solution is used to simplify the process of deploying high-performance parallel computing clusters. Then, three computational electromagnetic applications are developed and implemented on the cluster using parallel MatLab. In the first application, is calculated the input impedance of a multilayer multiconductor circular microstrip antenna that is excited with coaxial probe and operating in a wide frequency band. Parallelization is then performed over the frequency vector permitting a speedup ratio of 23. In the second application, is presented a systematic method for generating and computing symbolic expressions of 3D tetrahedral FEM basis functions of higher orders up to 10. The final application is dedicated to the parallelization of the spatial moment method code for 2D array of bowtie antenna. The simulation results have shown that the array size can be increased considerably leading to a linear system with a size up to 100 times greater than the one under study.

Agent-based game theoretic model for block motion estimation and its multicore implementation

Abstract Motion estimation (ME) is one of the main tools employed for eliminating temporal redundancies in video coding. It is the most critical and time-consuming tool of the complete encoder and typically requires 60%–80% of the total computational time. Block-matching ME (BME) algorithms divide a frame into macroblocks (MB) and look for the best possible match in the reference frame. This paper introduces a novel parallel framework to speed up the BME process. This is done by introducing a novel level of parallelism within the MB. The problem of BME is cast in a non-cooperative game-theoretic setting and a distributed multi-agent system is employed to solve the problem. First, a given MB is divided into subblocks and an agent is defined for each subblock. Then, the problem is formulated as a Consensus game and our approximation of the global utility function for the MB is defined. Building on this, agents' utilities are derived so that the resulting game is a potential game. To solve the game, distributed sequential and simultaneous algorithms based on game-theoretic Best Response Dynamics (BRD) and particle swarm optimization (PSO) are presented. Each agent uses PSO as its local search engine to autonomously maximize the utility of its subblock and BRD drive the agents with minimum local communication towards the maximum of the global utility function of the whole MB. Experimental results show that these algorithms provide good estimation quality with low computational cost as compared to other techniques. Moreover, in addition to its decentralized and distributed nature, the simultaneous algorithm is also inherently parallel at the agents' level within the MB. A parallel implementation of this algorithm using the MATLAB Parallel Computing Toolbox™ (PCT) on a multicore system shows that speedup is indeed obtained.

Homogenization of the finite-length fibre composite materials by boundary meshless type method

The paper presents the process of homogenization of the composite material properties obtained by method of continuous source functions developed for simulation both elasticity and heat conduction in composite material reinforced by finite-length regularly distributed, parallel, overlapping fibres. The interaction (fibre–fibre, fibre–matrix) of physical micro-fields influences the composite behaviour. Comparing with finite element method (FEM), the interaction can be simulated either by very fine FE mesh or the interaction is smoothed. The presented computational method is a mesh-reducing boundary meshless type method. The increase in computational efficiency is obtained by use of parallel MATLAB in presented computational models. The stiffness/conductivity is incrementally reduced starting with superconductive/rigid material properties of fibres and the fibre–matrix interface boundary conditions are satisfied by the iterative procedure. The computational examples presented in paper show the homogenized properties of finite-length fibre composites; the thermal and elasticity behaviour of the finite-length fibre composites; the similarities and differences in composite behaviour in thermal and elasticity problems; the control volume element for homogenization of composite materials reinforced by finite-length fibres with the large aspect ratio (length/diameter). The behaviour of the finite-length fibre composite will be shown in similar the heat conduction and elasticity problems. Moreover, the paper provides the possibilities and difficulties connected with present numerical models and suggested ways for further developments.

A New Lagrange-Newton-Krylov Solver for PDE-constrained Nonlinear Model Predictive Control

Abstract Real-time optimization of systems governed by partial differential equations (PDEs) presents significant computational challenges to nonlinear model predictive control (NMPC). The large-scale nature of PDEs often limits the use of standard nested black-box optimizers that require repeated forward simulations and expensive gradient computations. Hence, to ensure online solutions at relevant time-scales, large-scale NMPC algorithms typically require powerful, customized PDE-constrained optimization solvers. To this end, this paper proposes a new Lagrange-Newton-Krylov (LNK) method that targets the class of time-dependent nonlinear diffusion-reaction systems arising from chemical processes. The LNK solver combines a high-order spectral Petrov-Galerkin (SPG) method with a new, parallel preconditioner tailored for the large-scale saddle-point systems that form subproblems of Sequential Quadratic Programming (SQP) methods. To establish proof-of-concept, a case study uses a simple parallel MATLAB implementation of the preconditioner with 10 cores. As a step towards real-time control, the results demonstrate that large-scale diffusion-reaction optimization problems with more than 106 unknowns can be solved efficiently in less than a minute.