Parallel Computation Method Research Articles

A Multi-Time-Step Parallel Computing Method based on Overlapping Particles for DEM

The application of the discrete element method (DEM) to continuous medium problems is becoming increasingly widespread. In this work, a parallel computing method with multiple time steps based on overlapping particles is proposed. The domain decomposition method (DDM) with overlapping particles method is used to increase the speed up and shorten the computation time and meet the consistency requirements during data transmission. The multi-time-step method (MTSM) is adopted to tackle the matching of asynchronous step boundary. Data are exchanged at the boundaries in each subdomain with a message passing interface (MPI). The computational efficiency of different step ratios in both serial and parallel computing is studied respectively. Numerical examples show that the DEM can effectively handle large structure deformation problems, and provides a shorter calculation time than that of the finite element method (FEM). The DEM with multiple time steps in different subdomains effectively reduces the computation time than that with a single time step in the entire domain. Under fixed step ratio conditions, using parallel computing can save more time than serial computing. This work develops ideas for expanding the application of DEM for large engineering problems.

Parallel Computing Method of Commonly Used Interpolation Algorithms for Remote Sensing Images

Parallel Computing Method of Commonly Used Interpolation Algorithms for Remote Sensing Images

Research on Parallel Computing Methods for High-Resolution Atmospheric Spectral Lines

Research on Parallel Computing Methods for High-Resolution Atmospheric Spectral Lines

Retracted: Parallel Calculation Method of Patch Area Landscape Art Index Based on Surface Coverage Data

Retracted: Parallel Calculation Method of Patch Area Landscape Art Index Based on Surface Coverage Data

A customized two-stage parallel computing algorithm for solving the combined modal split and traffic assignment problem

Efficiently solving the traffic assignment problem (TAP) for large-scale transport networks is a critical problem for transportation studies. Most of the existing algorithms for TAP are serial ones based on single-computer mode, which has inherently limited the computational efficiency, compared with parallel computing methods. Thus, this paper aims to propose an efficient distributed multi-computer cluster resource allocation method for the parallel computing of TAP. Previous studies on the parallel computing of TAP are mainly based on a single-mode, which is extended to a more complex combined modal split and traffic assignment (CMSTA) case in this paper. In order to decompose the CMSTA problem, we proposed a block-decomposed model for solving the CMSTA problem. Then we designed an optimal parallel computing resource schedule for solving each block problem more quickly on the huge transportation network. Therefore, we implemented a customized two-stage parallel (TP) algorithm that can fully use parallel resources. The first parallel stage of the TP algorithm is used in the path generation phase, and the second parallel stage is used in the path flow adjustment phase. Besides, the parallel slowdown is uncovered in calculating each block problem of the path flow adjustment phase by using parallel resources. Numerical examples are taken to validate the efficiency and robustness of the proposed TP algorithm.

A Scalable Algorithm for Shape Optimization with Geometric Constraints in Banach Spaces

This work develops an algorithm for PDE-constrained shape optimization based on Lipschitz transformations. Building on previous work in this field, the $p$-Laplace operator is utilized to approximate a descent method for Lipschitz shapes. In particular, it is shown how geometric constraints are algorithmically incorporated avoiding penalty terms by assigning them to the subproblem of finding a suitable descent direction. A special focus is placed on the scalability of the proposed methods for large scale parallel computers via the application of multigrid solvers. The preservation of mesh quality under large deformations, where shape singularities have to be smoothed or generated within the optimization process, is also discussed. It is shown that the interaction of hierarchically refined grids and shape optimization can be realized by the choice of appropriate descent directions. The performance of the proposed methods is demonstrated for energy dissipation minimization in fluid dynamics applications.

Block-Wise Computation of Cyclic Redundancy Code Using Factored Toeplitz Matricesin Lieu of Look-Up Table

Cyclic Redundancy Check (CRC) or Cyclic Redundancy Code is a Cyclic Error Detection Code used to preserve the integrity of data in storage and transmission applications. CRC of a stream of message bits is usually calculated block-wise in parallel with the help of a Look-Up Table (LUT) in software or State Space transformation matrix in hardware. Presented here is a novel method and architecture of parallel computation of Cyclic Redundancy Codes without any Look-Up Table for an arbitrary generating polynomial which is programmable at runtime. The method reduces computational complexity and storage requirements by implicitly factorizing the transformation matrix needed to compute the remainder into two simpler Toeplitz matrices. The resulting hardware architecture is suitable for embedded applications.

Parallelized ultrasound homodyned-K imaging based on a generalized artificial neural network estimator

The homodyned-K (HK) distribution model is a generalized backscatter envelope statistical model for ultrasound tissue characterization, whose parameters are of physical meaning. To estimate the HK parameters is an inverse problem, and is quite complicated. Previously, we proposed an artificial neural network (ANN) estimator and an improved ANN (iANN) estimator for estimating the HK parameters, which are fast and flexible. However, a drawback of the conventional ANN and iANN estimators consists in that they use Monte Carlo simulations under known values of HK parameters to generate training samples, and thus the ANN and iANN models have to be re-trained when the size of the test sets (or of the envelope samples to be estimated) varies. In addition, conventional ultrasound HK imaging uses a sliding window technique, which is non-vectorized and does not support parallel computation, so HK image resolution is usually sacrificed to ensure a reasonable computation cost. To this end, we proposed a generalized ANN (gANN) estimator in this paper, which took the theoretical derivations of feature vectors for network training, and thus it is independent from the size of the test sets. Further, we proposed a parallelized HK imaging method that is based on the gANN estimator, which used a block-based parallel computation method, rather than the conventional sliding window technique. The gANN-based parallelized HK imaging method allowed a higher image resolution and a faster computation at the same time. Computer simulation experiments showed that the gANN estimator was generally comparable to the conventional ANN estimator in terms of HK parameter estimation performance. Clinical experiments of hepatic steatosis showed that the gANN-based parallelized HK imaging could be used to visually and quantitatively characterize hepatic steatosis, with similar performance to the conventional ANN-based HK imaging that used the sliding window technique, but the gANN-based parallelized HK imaging was over 3 times faster than the conventional ANN-based HK imaging. The parallelized computation method presented in this work can be easily extended to other quantitative ultrasound imaging applications.

A Hybrid GPU and CPU Parallel Computing Method to Accelerate Millimeter-Wave Imaging

The range migration algorithm (RMA) based on Fourier transformation is widely applied in millimeter-wave (MMW) close-range imaging because of its few operations and small approximation. However, its interpolation stage is not effective due to the involved intensive logic controls, which limits the speed performance in a graphics processing unit (GPU) platform. Therefore, in this paper, we present an acceleration optimization method based on the hybrid GPU and central processing unit (CPU) parallel computation for implementing the RMA. The proposed method exploits the strong logic-control capability of the CPU to assist the GPU in processing the logic controls of the interpolation stage. The common positions of wavenumber-domain components to be interpolated are calculated by the CPU and stored in the constant memory for broadcast at any time. This avoids the repetitive computation consumed in a GPU-only scheme. Then the GPU is responsible for the remaining matrix-related steps and outputs the needed wavenumber-domain values. The imaging experiments verify the acceleration efficiency of the proposed method and demonstrate that the speedup ratio of our proposed method is more than 15 times of that by the CPU-only method, and more than 2 times of that by the GPU-only method.

Matrix Chain Multiplication and Equivalent Reduced-Order Parallel Calculation Method for a Robotic Arm

Intelligence development has put forward increasing requirements of real-time planning and dynamic feedback in controlling robotic arms. It has become essential in engineering applications to complete the kinematics calculation of complex manipulators in real time. This paper proposes a matrix cascading multiplication equivalent reduced-order parallel computing method for the multiplication of homogeneous matrices in the process of forward and inverse kinematics solutions, which reduces the order of the matrix according to the distribution of zero vectors in the homogeneous matrix calculations. The method removes the unnecessary multiplication in joint homogeneous matrixes containing zero vectors. It obtains the optimal calculation order of the cascade matrix multiplication through dynamic planning searches, improving the efficiency of effective dot product calculation by parallel computation. Calculation processes and specific examples are presented in this paper. Compared with the previous algorithms, the proposed algorithm reduces the calculation cycle by 90%, effectively improving the real-time calculation efficiency in the complex control process.

A Parallel Computing Method for the Computation of the Moore–Penrose Generalized Inverse for Shared-Memory Architectures

A Parallel Computing Method for the Computation of the Moore–Penrose Generalized Inverse for Shared-Memory Architectures

Численное моделирование переноса твёрдых частиц в атмосферном городском пограничном слое с использованием лагранжева подхода: физические задачи и параллельная реализация

This paper presents the results of the development of a numerical model of the Lagrangian particle transport and the application of parallel computation methods to increase the efficiency of the software implementation of the model. The model is a software package allowing calculations of transport and deposition of aerosol particles taking into account the properties of particles and input data describing atmospheric conditions and the underlying surface geometry. The dynamic core, physical parameterizations, numerical implementation, and algorithm of the model are described. Initially, the model has been used for computationally low-intensive problems. In this paper, given the need to use the model in computationally intensive problems, we conduct optimization of the sequential software implementation of the model, as well as creation of software implementations of the model with the use of parallel computing technologies OpenMP, MPI, CUDA. The results of testing of different implementations of the model show that optimization of the most computationally complex blocks in the sequential version of the model can reduce the execution time by 27%, at the same time the use of parallel computing technologies allows to achieve acceleration by several orders of magnitude. The use of OpenMP in dynamic block of the model resulted in acceleration of block up to 4 times, the use of MPI – up to 8 times, the use of CUDA – up to 16 times, all other conditions being equal. Recommendations on the choice of parallel computing technology depending on the properties of the computing system are proposed.

Identifying Reliability-Critical Primary Inputs of Combinational Circuits Based on the Model of Gate-Sensitive Attributes

The identification of reliability-critical primary input leads (RCPIs) plays an important role in the testing and prediction of reliability boundaries of logic circuits. This article presents a gate-sensitive-attributes-based approach to estimate the criticality of the primary input leads in combinational circuits to their reliability. Oriented to the input vector, a subcircuit-based traversal method marks the critical input leads of each gate in a circuit. Gate-sensitive attributes and a reverse recursive algorithm quantify the effect of each RCPI on circuit reliability under the input vector. A parallel calculation method based on subcircuits with only one primary output reduces the computational complexity to accelerate the calculation process. Similarity-based clustering avoids unnecessary calculations, and a self-adaptive strategy is used to check convergence. Experimental results on benchmark circuits show that the average accuracy of this approach is 0.9634 with Monte Carlo (MC) as the reference and it is 3445 times faster than the MC on average while its average memory cost is 1.67 greater than the MC model. Although the fitness of the worst input vector obtained by other reference methods is 1.09 times better than that of this approach on average, this approach is approximately 21 times faster than that reference method on average.

Research on the on-line fracture assessment of cracked primary loop pipes in NPPs

The cracked pipes usually need to be replaced or conservatively evaluated in the Nuclear Power Plants (NPPs). There is still a lack of effective methods for a real-time crack propagation and safety evaluation based on massive monitoring transient data. In order to ensure the safe and economic operation of the NPPs, the authors have developed an on-line fracture assessment technology in this paper. The calculation method of total stress suitable for on-line monitoring data, optimizing the calculation method of transient thermal stress and stress intensity factor (SIF) based on the influence function method are discussed. Then, the on-line software for the primary loop hot-leg pipe which is based on the research results is developed. In this paper, an on-line safety assessment method based on the monitoring data which can meet the requirements of the in-service inspection code (RSE-M Code) is given. The influence function is proposed to be established at a fixed time interval which will make the computational complexity reduced significantly. By the parallel calculation method and the characteristics of the algorithm, the origin algorithm along the time axis is optimized to an algorithm that can be calculated to obtain the target parameters at each time point within the specified attenuation time range of the influence function. Then the calculation efficiency is significantly improved. By compiling the data and calculation methods in the code, the calculation method of SIF suitable for on-line monitoring is obtained, and the reliability is also verified by the analyzed case. Based on the technical results obtained from the research and the requirements in the RSE-M Code, the re-development is carried out in the general commercial software platform. The software is developed, and it is demonstrated that it has sufficient analysis accuracy.

Block-Based Multicut Benders Decomposition Algorithm for Transmission and Energy Storage Co-Planning

This study proposes a block-based multicut Benders decomposition algorithm to solve the co-planning of transmission expansion and energy storage problem in a bi-level approach. The proposal breaks the chronological representative period into multiple subperiods blocks. This division makes it possible to use parallel computation methods to solve each block simultaneously, reducing the simulation time, which allows the use of a more extensive time window to model the variability of random variables of the system, such as wind and load. In the proposed algorithm, the master problem defines the State of Charge (SoC) of the energy storage devices between the blocks and the investment in transmission and energy storage devices. To demonstrate the effectiveness of the proposed method, different sizes of representative periods are evaluated in three test systems: Garver 6-bus, IEEE-RTS 24-bus, and IEEE-118 188-bus. The tests compare the performance of the proposed block-based multicut Benders decomposition algorithm with the usual approach applied in the literature considering Benders decomposition and the complete problem formulated as a Mixed-Integer Linear Programming (MILP) problem.

Dynamic propagation behaviors of hydraulic fracture networks considering hydro-mechanical coupling effects in tight oil and gas reservoirs: A multi-thread parallel computation method

Hydrofracturing is a remarkable technology for unconventional oil and gas exploitation in tight reservoirs, in which the fluid-driven propagation of hydraulic fractures in a porous medium rock reservoir is an extremely complex physical process, for which there are great challenges in numerical simulation, such as multiphysical field coupling, multiple field evolution on engineering-scale reservoirs, and multi-scale fracture propagation processes. This complex physical process renders it difficult to obtain reliable solutions rapidly and efficiently. Understanding the behavior and mechanism of hydrofracturing is complicated. A multi-thread parallel computation scheme for solid and fluid analysis was developed to improve computational efficiency in large-scale models and multi-physical field coupling (hydro-mechanical) problems, and fracture criteria via a dual bilinear cohesive zone model was introduced. Considering the hydro-mechanical coupling and leak-off effects, the combined finite element-discrete element-finite volume approach was introduced and implemented successfully, and a multi-thread parallel computation method and global procedure was proposed. This study provides valuable results for parallel computation of dynamic fluid-driven propagation of hydraulic fractures in porous elastic rock mass and lays the foundation for further development of efficient and reliable simulations for dynamic multiscale complex fractures under multiphysical field coupling.

A Parallel DNA Algorithm for Solving the Quota Traveling Salesman Problem Based on Biocomputing Model.

The quota traveling salesman problem (QTSP) is a variant of the traveling salesman problem (TSP), which is a classical optimization problem. In the QTSP, the salesman visits some of the n cities to meet a given sales quota Q while having minimized travel costs. In this paper, we develop a DNA algorithm based on Adleman-Lipton model to solve the quota traveling salesman problem. Its time complexity is O(n2+Q), which is a significant improvement over previous algorithms with exponential complexity. A coding scheme of element information is pointed out, and a reasonable biological algorithm is raised by using limited conditions, whose feasibility is verified by simulation experiments. The innovation of this study is to propose a polynomial time complexity algorithm to solve the QTSP. This advantage will become more obvious as the problem scale increases compared with the algorithm of exponential computational complexity. The proposed DNA algorithm also has the significant advantages of having a large storage capacity and consuming less energy during the operation. With the maturity of DNA manipulation technology, DNA computing, as one of the parallel biological computing methods, has the potential to solve more complex NP-hard problems.

A generalized block-iterative projection method for the common fixed point problem induced by cutters

The block-iterative projections (BIP) method of Aharoni and Censor [Block-iterative projection methods for parallel computation of solutions to convex feasibility problems, Linear Algebra and its Applications 120, (1989), 165–175] is an iterative process for finding asymptotically a point in the nonempty intersection of a family of closed convex subsets. It employs orthogonal projections onto the individual subsets in an algorithmic regime that uses “blocks” of operators and has great flexibility in constructing specific algorithms from it. We extend this algorithmic scheme to handle a family of continuous cutter operators and to find a common fixed point of them. Since the family of continuous cutters includes several important specific operators, our generalized scheme, which ensures global convergence and retains the flexibility of BIP, can handle, in particular, metric (orthogonal) projectors and continuous subgradient projections, which are very important in applications. We also allow a certain kind of adaptive perturbations to be included, and along the way we derive a perturbed Fejér monotonicity lemma which is of independent interest.

Parallel Calculation Method for Urban Two-Dimensional Rainfall Flood Model Based on Compute Unified Device Architecture

Parallel Calculation Method for Urban Two-Dimensional Rainfall Flood Model Based on Compute Unified Device Architecture

Batch sequential adaptive designs for global optimization

Efficient global optimization (EGO) is one of the most popular sequential adaptive design (SAD) methods for expensive black-box optimization problems. A well-recognized weakness of the original EGO in complex computer experiments is that it is serial, and hence the modern parallel computing techniques cannot be utilized to speed up the running of simulator experiments. For those multiple points EGO methods, the heavy computation and points clustering are the obstacles. In this work, a novel batch SAD method, named “Accelerated EGO”, is forwarded by using a refined sampling/importance resampling (SIR) method to search the points with large expected improvement (EI) values. The computation burden of the new method is much lighter, and the points clustering is also avoided. The efficiency of the proposed batch SAD is validated by nine classic test functions with dimension from 2 to 12. The empirical results show that the proposed algorithm indeed can parallelize original EGO, and gain much improvement compared against the other parallel EGO algorithm especially under high-dimensional case. Additionally, the new method is applied to the hyper-parameter tuning of support vector machine (SVM) and XGBoost models in machine learning. Accelerated EGO obtains comparable cross validation accuracy with other methods and the CPU time can be reduced a lot due to the parallel computation and sampling method.