Proposed Parallel Algorithm Research Articles

Parallel Scheme for Multi-Layer Refinement Non-Uniform Grid Lattice Boltzmann Method Based on Load Balancing

The large-scale numerical simulation of complex flows has been an important research area in scientific and engineering computing. The lattice Boltzmann method (LBM) as a mesoscopic method for solving flow field problems has become a relatively new research direction in computational fluid dynamics. The multi-layer grid-refinement strategy deals with different-level of computing complexity through multi-scale grids, which can be used to solve the complex flow field of the non-uniform grid LBM without destroying the parallelism of the standard LBM. It also avoids the inefficiencies and waste of computational resources associated with standard LBMs using uniform and homogeneous Cartesian grids. This paper proposed a multi-layer grid-refinement strategy for LBM and implemented the corresponding parallel algorithm with load balancing. Taking a parallel scheme for two-dimensional non-uniform meshes as an example, this method presented the implementation details of the proposed parallel algorithm, including a partitioning scheme for evaluating the load in a one-dimensional direction and an interpolation scheme based on buffer optimization. Simply by expanding the necessary data transfer of distribution functions and macroscopic quantities for non-uniform grids in different parallel domains, our method could be used to conduct numerical simulations of the flow field problems with complex geometry and achieved good load-balancing results. Among them, the weak scalability performance could be as high as 88.90% in a 16-threaded environment, while the numerical simulation with a specific grid structure still had a parallel efficiency of 77.4% when the parallel domain was expanded to 16 threads.

Multimobile Robot Cooperative Localization Using Ultrawideband Sensor and GPU Acceleration

To tackle the poor localization accuracy of multimobile robots caused by non-line-of-sight (NLOS) errors in a complex indoor environment and to meet the real-time requirement, this article proposes a multimobile robot cooperative localization system using ultrawideband (UWB) sensor and GPU hardware acceleration. First, a UWB multinode ranging network is established to obtain the relative distance information between robots and anchors. Then, the line-of-sight (LOS) and NLOS errors in distance information are effectively mitigated by using the proposed UWB ranging error mitigation algorithm based on the Bayesian filter. A cooperative particle filter (PF) localization algorithm based on the Gibbs sampling is designed to estimate the position information of each robot at any time. Finally, in order to improve the real-time performance of the collaborative localization system, a parallel Gibbs collaborative localization algorithm that can be accelerated by GPU is proposed considering the characteristics of GPU hardware and CUDA programming model. The experimental results of three TurtleBot2 mobile robots in real scene show that the proposed multimobile robot cooperative localization system using UWB technology can estimate the position information of each robot robustly and accurately, and the localization accuracy is superior to that of the popular extended Kalman filter (EKF) and PF algorithms. It is shown through further evaluations that the proposed parallel algorithm achieves about 3.2 times acceleration effect in the scenarios of three mobile robots. The speed gain is found more significant with more robots, which substantially improves the real-time performance of the cooperative localization system. In the test with seven mobile robots, the speedup is as high as 11.9, that is, the execution time of the algorithm is only 8.39% of that of the original algorithm. Note to Practitioners—The purpose of this article is to improve the accuracy and real-time indoor multimobile robot cooperative localization, but the method proposed in this article is also applicable to outdoor multimobile robot cooperative localization. The existing methods for indoor cooperative localization of mobile robots usually use Bluetooth, infrared, RFID, and other technologies to establish a wireless sensor network (WSN) and then combine Karman filter or particle filter (PF) to achieve cooperative localization, which is difficult to achieve low-cost and high-precision real-time localization. In this article, a new method of cooperative localization is proposed, which uses ultrawideband (UWB) ranging network with high penetration and high precision to obtain accurate distance information, then weakens NLOS error by the Bayesian filtering to further improve the accuracy of distance information, and, finally, uses a novel cooperative localization approach to realize fast and high-precision indoor multimobile robot localization. In addition, by redesigning the collaborative localization algorithm in parallel, the real-time performance of the algorithm is improved while ensuring high accuracy. The collaborative localization experiment of three mobile robots in the real scene shows that the proposed algorithm can effectively improve the localization accuracy, and the real-time performance of collaborative localization is significantly improved. However, the algorithm still has some limitations when mitigating UWB ranging errors in highly obstructed environments, and the algorithm parallelization framework can be further improved for higher real-time performance. In the future, we will further improve the robustness of cooperative localization and apply this algorithm to cooperative control of multiple mobile robots, such as formation control and cooperative search.

A scalable, robust parallel algorithm on handling of sliding non-conformal interfaces with an efficient supermesh method

Handling of non-conformal interfaces communication is one of the challenges from which the parallelization of multi-zone unstructured grids technology suffers. Especially for dynamic mesh simulation, changing dynamic communication patterns induced by the grids relative motion requires data to be correctly transferred across discrete non-conformal interfaces at every time step. To overcome the challenges at extreme scales in this paper we attempt to design the parallel algorithm in such a way that it enhances the scalability of the sliding mesh method implemented in the simulation of practical engineering problems with complex geometries. In the proposed parallel algorithm, the issue of changing dynamic interface communication is handled by a scalable and robust parallel supermeshing algorithm which has no constraints on the domain partition schemes and is adapted to complex practical engineering simulation. Besides, a unified cell-based communication map adapting to parallelized matrixes solvers is dynamically reconstructed by assembling both the inner-boundary communication map and interface communication map, which greatly improves the robustness and scalability of the sliding mesh algorithm executed on the parallel CFD code. All methodologies are integrated and implemented for parallel computation obtaining good scalability and computational efficiency. Specific examples are solved to analyze the conservation, scalability and robustness of the parallelized interface approach and finally, the full-cycle unsteady simulation of a four-stroke engine is tested to evaluate the performance of this parallel strategy when applied to practical engineering problems.

A GPU-based accelerated ELM and deep-ELM training algorithms for traditional and deep neural networks classifiers

• In this paper, we have proposed novel parallel ELM and D-ELM algorithms on the GPU. The benefits of these parallel algorithms include reducing the training time of the algorithms and reducing the runtime significantly. Also, remarkably predictive 95 accuracy is presented. To the best of our knowledge, this is the first kind of parallelization of the D-ELM in which the high computing power of graphics processing equipped with CUDA is utilized to parallelize. Our main contribution is as follows. • All sub-operations of ELM and D-ELM which need to be parallelized 100 are parallelized on GPU, thus benefiting from the memory hierarchy. In the meantime, several optimizations are adopted to improve the training time and performance of our parallelization. • Maximizing parallel performance: It is very important organizing the algorithm in computational blocks that are able to run independently. 105 Also, the communication between the blocks is minimized to achieve better performance. • Improve the usage of memory hierarchy: It is especially important to execution maximize the number of operations on data stored in shared memory. This strategy is applied to ELM and D-ELM 110 algorithms. • Using of CUDA Optimal Libraries: In some operations of ELM and D-ELM algorithms, these useful tools have been used for improving the performance. • Maximizing the independent and concurrent execution of the times 115 auto-encoders in the D-ELM Algorithm: Given that many computational operations are located in this section, their concurrent and independent execution is extraordinary for improving the performance. • The implementation of the standard of ELM and D-ELM has a weak 120 learning speed by increasing the scale of data. We have optimally implemented serial versions of ELM and D-ELM algorithms as a basis and brought the cost of training time for different strategies using data with different scales. Serial part evaluations show that the cost of training time for ELM and D-ELM algorithms and the size of 125 the dataset have almost linearly related. • Our proposed parallel algorithms have well training time and prediction accuracy and offer high performance for iterative calculations. Since they have high computing power so another variant of machine learning algorithms that have high iterative 130 computations can benefit from our proposed methods. The extreme learning machine (ELM) has been effectively used for training single-layer neural networks. In recent years, great attention has been paid to deep extreme learning machine (D-ELM) structures. Deep neural network structures are trained via the ELM method. Some stacked auto-encoders followed by a simple ELM layer can be usually used for solving classification and regression tasks. Although ELM has been employed for speeding up the training process of the neural network, D-ELM based models suffer from some issues such as the time complexity and running time. In this paper, we explore how the evaluation of ELM and D-ELM can be accelerated. GPUs are used to speed up the training process of ELM and D-ELM models. In the proposed method, three separate phases are considered for the algorithms. In the first phase, loading and pre-processing the data are performed serially in the CPU. In the second and third phases, which respectively are the training and testing phases of the algorithm, all the matrix operations of the algorithms are implemented in parallel mode using the GPU memory hierarchy. Also, having access to highly efficient computational libraries, additional support is provided for GPU-based parallel computing. In the simulation setup, five sets of the database are applied to train the ELM and D-ELM on both CPU and GPU platforms. The results obtained show the proposed approach based on GPUs can remarkable save running time. Although both serial and parallel methods measure approximately the same accuracy, the parallel methods provided for the models reduce the run time significantly.

A distributed-memory MPI parallelization scheme for multi-domain incompressible SPH

A parallel scheme for a multi-domain truly incompressible smoothed particle hydrodynamics (SPH) approach is presented. The proposed method is developed for distributed-memory architectures through the Message Passing Interface (MPI) paradigm as communication between partitions.The proposal aims to overcome one of the main drawbacks of the SPH method, which is the high computational cost with respect to mesh-based methods, by coupling a multi-resolution approach with parallel computing techniques.The multi-domain approach aims to employ different resolutions by subdividing the computational domain into non-overlapping blocks separated by block interfaces. The particles belonging to different blocks are efficiently distributed among processors ensuring well balanced loads.The parallelization procedure handles particle exchanges both throughout the blocks and the competence domains of the processors. The matching of the velocity values between neighbouring blocks is obtained solving a system of interpolation equations at each block interfaces through a parallelized BiCGSTAB algorithm. Otherwise, a whole pseudo-pressure system is solved in parallel considering the Pressure Poisson equations of the fluid particles of all the blocks and the interpolation equations of all the block interfaces.The employed test cases show the strong reduction of the computational efforts of the SPH method thanks to the interaction of the employed multi-resolution approach and the proposed parallel algorithms.

Parallel algorithm for synthetic image generation with application to tokamak plasma diagnostics

SummaryThe tomographic reconstruction is a powerful diagnostic tool in nuclear fusion experiments for the determination of the shape and position of the plasma. However, neural networks are emerging as a suitable alternative to conventional plasma tomography algorithms. In order to train such AI/ML based models, we need large‐scale, diversified image data for learning and evaluation which is difficult to obtain from real experiments. Accuracy and real‐time response is also critical, therefore in this article we propose an effective shared memory based parallel algorithm for synthetic imaging diagnostic data generation. The practicality of the proposed parallel algorithm has been evaluated experimentally by comparing it to the sequential algorithm on two different computing architectures. We observe a maximum speedup of 21 at 32 threads and demonstrate that our proposed parallel algorithm scales well over a range of image sizes. The proposed parallel algorithm can be used to obtain the synthetic images within a few seconds which is very important for real time applications. We also provide an analysis on the importance of choosing the right scheduling type and optimum chunk size to obtain the maximum speedup.

A Three-Layered Multifactorial Evolutionary Algorithm with Parallelization for Large-Scale Engraving Path Planning

Today, although laser engraving technology is widely used in 2D image engraving, when the image is larger and more complicated, most existing algorithms for engraving path planning have a huge computational burden and reduced engraving efficiency. Accordingly, this article addresses the trajectory optimization problem in large-scale image engraving. First, we formulate the problem as an improved model based on the large-scale traveling salesman problem (TSP). Then, we propose a three-layered algorithm called 3L-MFEA-MP, structured as follows: an upper layer, the genetic algorithm (GA); a middle layer, the GA; and a bottom layer, the parallel multifactorial evolutionary algorithm. Experiments on four classic large-scale TSP datasets show that our algorithm exhibits superior performance in terms of the path length and engraving time compared with other algorithms. In particular, compared with the single-thread algorithm, the proposed parallel algorithm reduced the engraving time by 80%. Moreover, the engraving machine experiment demonstrated that the engraving time of our algorithm on mona-lisa 100K, vangogh 120K, and venus 140K was approximately one tenth that of the traditional dot engraving method. The results indicate that the proposed algorithm can reduce the computational burden and improve engraving efficiency in engraving path planning.

Parallel border tracking in binary images using GPUs

Border tracking in binary images is an important kernel for many applications. There are very efficient sequential algorithms, most notably, the algorithm proposed by Suzuki et al., which has been implemented for CPUs in well-known libraries. However, under some circumstances, it would be advantageous to perform the border tracking in GPUs as efficiently as possible. In this paper, we propose a parallel version of the Suzuki algorithm that is designed to be executed in GPUs and implemented in CUDA. The proposed algorithm is based on splitting the image into small rectangles. Then, a thread is launched for each rectangle, which tracks the borders in its associated rectangle. The final step is to perform the connection of the borders belonging to several rectangles. The parallel algorithm has been compared with a state-of-the-art sequential CPU version, using two different CPUs and two different GPUs for the evaluation. The computing times obtained show that in these experiments with the GPUs and CPUs that we had available, the proposed parallel algorithm running in the fastest GPU is more than 10 times faster than the sequential CPU routine running in the fastest CPU.

A fast parallel high-precision summation algorithm based on AccSumK

In this paper, we present a new parallel accurate algorithm called PAccSumK for computing summation of floating-point numbers. It is based on AccSumK algorithm. In the experiment, for the summation problems with large condition numbers, our algorithm outperforms the PSumK algorithm in terms of accuracy and computing time. The reason is that our algorithm is based on a more accurate algorithm called AccSumK algorithm compared to the SumL algorithm used in PSumK. The proposed parallel algorithm in this paper is designed to compute a result as if computed internally in K-fold the working precision. Numerical results are presented showing the performance and the accuracy of our new parallel algorithm for calculating summation.

Implementing the Kalman Filter Algorithm in Parallel Form: Denoising Sound Wave as a Case Study

Introduction: Signal filters were originally seen as circuits or systems with frequency selecting behaviors. The development of filtering techniques went on and more sophisticated filters were introduced, such as e.g. Chebychev and Butterworth filters, which gave means of shaping the frequency characteristics of the filter in a more systematic design procedure. During this stage, the filtering was mainly considered from this frequency. Mehtod: In (SIMD) model, a parallel computer consists of N identical processors, each of the N processors possesses its own local memory where it can store both programs and data, and all processors operate under the control of a single instruction stream issued by a central control unit. Equivalently, the N processors may be assumed to hold identical copies of a single program, each processor's copy being stored in its local memory. There are N data streams, one per processor. Result: It can be seen that the computation time decreases when we increase the number of cores from 2 to 12 cores shows that Kalman Filter can achieve nearly linear speed-up by increasing the number of cores, both results are illustrated consecutively. Conclusion: Parallel multicore implementation of the Kalman filter is studied. The implementation based on SIMD model which we splitting all signal points into large segments of data and applying equations on each segment simultaneously. Discussion: Through implementing the algorithm in parallel form on the noisy sound wave signals as a case study, it is found that the proposed parallel algorithm executes about twice as fast on double cores as the sequential form on a single core, it enhances the execution time to 44.86%. And is capable of achieving linear speedup in the number of cores used.

Parallel assignment of flow directions over flat surfaces in massive digital elevation models

The assignment of flow directions in flat regions needs to be treated with special algorithms to obtain hydrologically correct flow patterns. Based on the sequential algorithm and the three-step parallel framework, we propose a parallel algorithm for assigning the hydrologically correct flow directions in flat regions. The proposed parallel algorithm assigns pre-divided tiles to multiple consumer processes, which construct local graphs that encode the geodesic distance information among tile border flat cells and higher terrain or lower terrain. Based on the local graphs in all tiles, the producer process constructs the global graph and computes the global geodesic distances of tile border flat cells from higher terrain and lower terrain. The consumer processes then compute the final geodesic distances in each tile and assign the hydrologically correct flow directions. Four experiments are conducted to evaluate the performance of our proposed algorithm. The speed-up ratios are greater than 3 when the number of consumer processes is greater than 9. When the number of consumer processes reaches 20, the average strong scaling efficiency is around 40%. The weak scaling efficiency is lower than 20% when the number of consumer processes is greater than 5. Compared with the sequential algorithm that is simpler to implement, the proposed parallel algorithm requires less memory on consumer processes than the sequential algorithm and can process massive digital elevation models that cannot be successfully processed using the sequential algorithm. It fills the void in the processing pipeline of automatic drainage network extraction based on the three-step parallel framework and enables parallel implementation of the entire processing pipeline possible, which should substantially improve the attractiveness of the three-step parallel framework.

Parallel unstructured finite volume lattice Boltzmann method for high-speed viscid compressible flows

Based on the double distribution function Boltzmann-BGK equations, a cell-centered finite volume lattice Boltzmann method on unstructured grids for high-speed viscid compressible flows is presented. In the equations, the particle distribution function is introduced on the basis of the D2Q17 circular function, and its corresponding total energy distribution function is adopted. In the proposed method, the advective term is evaluated by Roe’s flux-difference splitting scheme, and a limiter is used to prevent the generation of oscillations. The distribution functions on the interface are calculated by piecewise linear reconstruction, in which the gradient is computed by the least-squares approach. In order to do large-scale simulations, a parallel algorithm is illustrated. The present method is validated by a flow around the NACA0012 airfoil and a flow past a circular cylinder at high Mach numbers. The results agree well with the published results, which demonstrate that the present method is an efficient numerical method for high-speed viscid compressible flows. The parallel performance results show that the proposed parallel algorithm achieves 90% parallel efficiency on 4800 cores for a problem with [Formula: see text] unstructured triangle cells, which shows the potential to perform fast and high-fidelity simulations of large-scale high-speed viscid compressible flows in complicated computational domains.

Parallel signal detection for generalized spatial modulation MIMO systems

Generalized Spatial Modulation is a recently developed technique that is designed to enhance the efficiency of transmissions in MIMO Systems. However, the procedure for correctly retrieving the sent signal at the receiving end is quite demanding. Specifically, the computation of the maximum likelihood solution is computationally very expensive. In this paper, we propose a parallel method for the computation of the maximum likelihood solution using the parallel computing library OpenMP. The proposed parallel algorithm computes the maximum likelihood solution faster than the sequential version, and substantially reduces the worst-case computing times.

Load Balancing Strategies for Slice-Based Parallel Versions of JEM Video Encoder

The proportion of video traffic on the internet is expected to reach 82% by 2022, mainly due to the increasing number of consumers and the emergence of new video formats with more demanding features (depth, resolution, multiview, 360, etc.). Efforts are therefore being made to constantly improve video compression standards to minimize the necessary bandwidth while retaining high video quality levels. In this context, the Joint Collaborative Team on Video Coding has been analyzing new video coding technologies to improve the compression efficiency with respect to the HEVC video coding standard. A software package known as the Joint Exploration Test Model has been proposed to implement and evaluate new video coding tools. In this work, we present parallel versions of the JEM encoder that are particularly suited for shared memory platforms, and can significantly reduce its huge computational complexity. The proposed parallel algorithms are shown to achieve high levels of parallel efficiency. In particular, in the All Intra coding mode, the best of our proposed parallel versions achieves an average efficiency value of 93.4%. They also had high levels of scalability, as shown by the inclusion of an automatic load balancing mechanism.

Efficient and Portable Distribution Modeling for Large-Scale Scientific Data Processing with Data-Parallel Primitives

The use of distribution-based data representation to handle large-scale scientific datasets is a promising approach. Distribution-based approaches often transform a scientific dataset into many distributions, each of which is calculated from a small number of samples. Most of the proposed parallel algorithms focus on modeling single distributions from many input samples efficiently, but these may not fit the large-scale scientific data processing scenario because they cannot utilize computing resources effectively. Histograms and the Gaussian Mixture Model (GMM) are the most popular distribution representations used to model scientific datasets. Therefore, we propose the use of multi-set histogram and GMM modeling algorithms for the scenario of large-scale scientific data processing. Our algorithms are developed by data-parallel primitives to achieve portability across different hardware architectures. We evaluate the performance of the proposed algorithms in detail and demonstrate use cases for scientific data processing.

Use of the «BCR/ABL - multitest» kit in the algorithm of laboratory diagnostics of oncohematological diseases: economic aspects.

Abnormal mRNAs of the hybrid BCR-ABL gene in the majority of cases initiate the synthesis of proteins with a mass of 210 kDa (p210), 190 kDa (p190), and 230 kDa (p230). Expression of the p210 variant is most common in CML (95% of cases), while the p190 and p230 variants are less common (1-4%). On the contrary, p190 predominates in ALL. Measurement of BCR/ABL gene expression is included in clinical guidelines for the diagnosis of CML and ALL as sequential tests in accordance with their occurrence. At the same time, in the context of primary patients testing with suspected hematological malignancies with a low prevalence of BCR-ABL positive patients in the cohort of examined individuals, sequential testing is associated with low cost-effectiveness. Purpose: approbation of a parallel algorithm for detecting all three (p210, p190 and p230) using the multiplex RT-PCR format implemented in the «BCR/ABL-MULTITEST» reagent kit. We used anonymized blood samples from patients with suspected CML, as well as samples from ALL patients before starting therapy. Testing of blood samples was carried out using two variants of the algorithm: sequential determination of individual BCR-ABL transcripts and parallel determination using the developed set of reagents «BCR/ABL-MULTITEST». To detect the p210 transcript, a commercial kit «AmpliSens® Leukemia Quantum M-bcr-FRT» (Central Research Institute of Epidemiology of Rospotrebnadzor, Russia) was used. Simultaneously, a test was used to detect all three variants of BCR-ABL transcripts using the «BCR/ABL - MULTITEST» reagent kit based on a monochrome multiplex reaction «in one test tube». Reverse transcription were carried out using the REVERTA-L reagent kit (Central Research Institute of Epidemiology of Rospotrebnadzor, Russia) in accordance with the manufacturer's instructions. Using the reagent kits «BCR/ABL-MULTITEST» and «AmpliSens® Leukemia Quantum M-bcr-FRT» there is a high level of correlation of quantitative results of determining the chimeric transcript BCR-ABL р210 (r = 0.99). When using the proposed parallel algorithm with the primary use of the «BCR/ABL-MULTITEST» reagent kit, out of 95 patients with suspected CML, 9 samples with p210 transcript were identified, one with p190 BCR / ABL, and in one case a transcript variant characteristic of chronic neutrophilic leukemia - p230 BCR / ABL. The estimated cost for detecting one positive case of BCR-ABL when using the parallel diagnostic algorithm «BCR/ABL-MULTITEST» with a focused flow of studies is reduced by about 2 times due to a decrease in the amount of laboratory plastic used and the volume of the reaction mixture, as well as the absence of the need for repeated separate tests to detect p190 and p230. The use of the multiplex PCR-RT test system «BCR/ABL-MULTITEST» allows detecting in one test tube all three main variants of BCR-ABL transcripts - p210, p190, p230 and achieving significant resource savings when examining a cohort of patients with suspected CML and ALL and low frequency of positive samples.

An efficient parallel algorithm for mining weighted clickstream patterns

In the Internet age, analyzing the behavior of online users can help webstore owners understand customers’ interests. Insights from such analysis can be used to improve both user experience and website design. A prominent task for online behavior analysis is clickstream mining, which consists of identifying customer browsing patterns that reveal how users interact with websites. Recently, this task was extended to consider weights to find more impactful patterns. However, most algorithms for mining weighted clickstream patterns are serial algorithms, which are sequentially executed from the start to the end on one running thread. In real life, data is often very large, and serial algorithms can have long runtimes as they do not fully take advantage of the parallelism capabilities of modern multi-core CPUs. To address this limitation, this paper presents two parallel algorithms named DPCompact-SPADE (Depth load balancing Parallel Compact-SPADE) and APCompact-SPADE (Adaptive Parallel Compact-SPADE) for weighted clickstream pattern mining. Experiments on various datasets show that the proposed parallel algorithm is efficient, and outperforms state-of-the-art serial algorithms in terms of runtime, memory consumption, and scalability.

Designing Parallel Adaptive Laplacian Smoothing for Improving Tetrahedral Mesh Quality on the GPU

Mesh quality is a critical issue in numerical computing because it directly impacts both computational efficiency and accuracy. Tetrahedral meshes are widely used in various engineering and science applications. However, in large-scale and complicated application scenarios, there are a large number of tetrahedrons, and in this case, the improvement of mesh quality is computationally expensive. Laplacian mesh smoothing is a simple mesh optimization method that improves mesh quality by changing the locations of nodes. In this paper, by exploiting the parallelism features of the modern graphics processing unit (GPU), we specifically designed a parallel adaptive Laplacian smoothing algorithm for improving the quality of large-scale tetrahedral meshes. In the proposed adaptive algorithm, we defined the aspect ratio as a metric to judge the mesh quality after each iteration to ensure that every smoothing improves the mesh quality. The adaptive algorithm avoids the shortcoming of the ordinary Laplacian algorithm to create potential invalid elements in the concave area. We conducted 5 groups of comparative experimental tests to evaluate the performance of the proposed parallel algorithm. The results demonstrated that the proposed adaptive algorithm is up to 23 times faster than the serial algorithms; and the accuracy of the tetrahedral mesh is satisfactorily improved after adaptive Laplacian mesh smoothing. Compared with the ordinary Laplacian algorithm, the proposed adaptive Laplacian algorithm is more applicable, and can effectively deal with those tetrahedrons with extremely poor quality. This indicates that the proposed parallel algorithm can be applied to improve the mesh quality in large-scale and complicated application scenarios.

A scalable parallel unstructured finite volume lattice Boltzmann method for three‐dimensional incompressible flow simulations

AbstractThe standard lattice Boltzmann method, which employs certain regular lattices coupled with discrete velocities as the computational grid, is limited in its flexibility to simulate flows in irregular geometries. To simulate large‐scale complex flows, we present a cell‐centered finite volume lattice Boltzmann method for incompressible flows on three‐dimensional (3D) unstructured grids and its corresponding parallel algorithm. The advective fluxes are calculated by the low‐diffusion Roe scheme, and the gradients of the particle distribution functions are computed with a least squares method. The presented scheme is validated by three benchmark flows: (a) a 3D Poiseuille flow, (b) cubic cavity flows with Reynolds numbers Re = 100 and 400, and (c) flows past a sphere with Re = 50, 100, 150, 200, and 250. Some parallel performance results are presented to show the scalability of the method, which reveal that the proposed parallel algorithm has considerable scalability and that the parallel efficiency is higher than 87% on 3840 processor cores. It can be seen that the presented parallel solver has significant potential for the accurate simulation of flows in complex 3D geometries.

Multi-level parallel chaotic Jaya optimization algorithms for solving constrained engineering design problems

Several heuristic optimization algorithms have been applied to solve engineering problems. Most of these algorithms are based on populations that evolve according to different rules and parameters to reach the optimal value of a function cost through an iterative process. Different parallel strategies have been proposed to accelerate these algorithms. In this work, we combined coarse-grained strategies, based on multi-populations, with fine-grained strategies, based on a diffusion grid, to efficiently use a large number of processes, thus drastically decreasing the computing time. The Chaotic Jaya optimization algorithm has been considered in this work due to its good optimization and computational behaviors in solving both the constrained optimization engineering problems (seven problems) and the unconstrained benchmark functions (a set of 18 functions). The experimental results show that the proposed parallel algorithms outperform the state-of-the-art algorithms in terms of optimization behavior, according to the quality of the obtained solutions, and efficiently exploit shared memory parallel platforms.