Parallel Computation Method Research Articles

A Simulation of Domain Decomposition Method for Smoothed Particle Hydrodynamics

Nowadays, the numerical method has become a very important approach for solving complex problems in engineering and science. Some grid-based methods such as the finite difference method (FDM) and finite element method (FEM) have already been widely applied to various areas; however, they still suffer from inherent difficulties which limit their applications to many problems. Therefore, a strong interest is focused on the meshfree methods such as smoothed particle hydrodynamics (SPH) to simulate fluid flow recently due to the advantages in dealing with some complicated problems. In the SPH method, a great number of particles will be used because the whole domain is represented by a set of arbitrarily distributed particles. To improve the numerical efficiency, parallelization using message-passing interface (MPI) is applied to the problems with the large computational domain. In parallel computing, the whole domain is decomposed by the parallel method for continuity of subdomain boundary under the single instruction multiple data (SIMD) and also based on the procedure of the SPH computations. In this work, a new scheme of parallel computing is employed into the SPH method to analyze SPH particle fluid. In this scheme, the whole domain is decomposed into subdomains under the SIMD process and it composes the boundary conditions to the interface particles which will improve the detection of neighbor particles near the boundary. With the method of parallel computing, the SPH method is to be more flexible and perform better.

Comparison of Three Different Parallel Computation Methods for a Two-Dimensional Dam-Break Model

Three parallel methods (OpenMP, MPI, and OpenACC) are evaluated for the computation of a two-dimensional dam-break model using the explicit finite volume method. A dam-break event in the Pangtoupao flood storage area in China is selected as a case study to demonstrate the key technologies for implementing parallel computation. The subsequent acceleration of the methods is also evaluated. The simulation results show that the OpenMP and MPI parallel methods achieve a speedup factor of 9.8× and 5.1×, respectively, on a 32-core computer, whereas the OpenACC parallel method achieves a speedup factor of 20.7× on NVIDIA Tesla K20c graphics card. The results show that if the memory required by the dam-break simulation does not exceed the memory capacity of a single computer, the OpenMP parallel method is a good choice. Moreover, if GPU acceleration is used, the acceleration of the OpenACC parallel method is the best. Finally, the MPI parallel method is suitable for a model that requires little data exchange and large-scale calculation. This study compares the efficiency and methodology of accelerating algorithms for a dam-break model and can also be used as a reference for selecting the best acceleration method for a similar hydrodynamic model.

The theory of parallel crc codes based on automaton models

A theoretical representation of the CRC codes with the aid of the finite automata in the Galois fields is proposed –linear finite-state machine (LFSM). We examined three types of LSC, which are distinguished by its characteristic matrices: type 1, type 2, and type 3. Three methods of the parallel CRC computation are proposed. The theorems are proven about the estimation of duration of the CRC computation by tuple-parallel method for three types of LFSM. We presented algorithms for the CRC tuple-parallel computation by the tables lookup for different types of LFSM and demonstrated their hardware implementation. For the symbolic-parallel and symbolic-tuple-parallel methods of CRC computation, symbolic LFSM over the non-binary Galois fields are applied. In contrast to the known approaches, which consider the problem of high speed only, present article stresses the need for taking into account a capability to detect errors with the aid of generator polynomials of the CRC codes. We proposed the Hamming codes and the Abramson codes in the binary and non-binary Galois fields for this purpose. Within the framework of single mathematical apparatus – theory of LFSM – it is possible to solve two important tasks simultaneously: to carry out mathematical substantiation of performance efficiency of the methods for parallel CRC computation and to estimate the detection and correction capability of the CRC codes based on LFSM graph models. Such comprehensive approach is relevant in the practice of various data transmission, storage and compression systems.

Numerical investigation on radiation characteristics of aero-intake noise with ground effects

A hybrid method for the prediction of far-field Aero-intake noise from gas turbine engine with ground effects has been developed. The near-field predictions were obtained by solving the linearized Euler equations with computational aeroacoustic techniques composed of high-order finite difference scheme, non-reflecting boundary condition, overset grids, and parallel computational methods. Kirchhoff integral method was used for the prediction of far-field directivity patterns. In order to predict the noise propagation, ray theory is applied with Geographical Information System (GIS) terrain profile data. Then, the noise level on the ground has been predicted and analyzed, including the effects of air absorption, ground reflection, and diffraction.

A parallel function evaluation approach for solution to large-scale equation-oriented models

Abstract The equation-oriented (EO) approach is widely used for process simulation and optimization. Nevertheless, large-scale EO models consist of a huge number of nonlinear equations and make the solution procedure a challenging and time-consuming task. For most gradient-based numerical algorithms, function evaluations are the dominant step during the solution procedure. Here, a parallel computation method is developed for function evaluations within EO optimization strategies. After dividing the equations into several groups, function evaluations are calculated by using multiple threads on a parallel hardware platform simultaneously. Theoretical analysis for the speedup ratio is conducted. The implementation of the proposed method on a multi-core processor platform as well as a graphics processing unit (GPU) platform is then presented with several case studies. Numerical results are compared and discussed to show that the multi-core processor implementation has good computational performance, whereas the GPU implementation only achieves computational acceleration under relatively specific conditions.

Explicit formulation of multibody dynamics based on principle of dynamical balance and its parallelization

Efficient computation of dynamics parameters is one of the important issues in simulation and control of the multibody systems as these systems become more complex. Recent advances in computer architecture are toward multiple core systems rather than high-speed single core systems. Therefore, parallel computation algorithms for dynamics parameters should be designed to improve the performance on these multicore architectures. In this paper, a new dynamics computation algorithm is derived using the principle of dynamical balance, which provides explicit computation of dynamic parameters. This new algorithm has the structure to which parallel computation can be easily applicable. Parallel computation methods are then applied so that we can exploit the structure of the proposed dynamics computation algorithm based on the principle of dynamical balance. The parallel algorithm is designed based on task and data-parallelism. The performance of the proposed algorithm is verified on robots with various topologies. The improved speed of parallel computation is demonstrated through these experiments.

プログラム全域を対象としたホットパスベース投機的マルチスレッド処理方式の提案

Nowadays, multicore processor is common presence and available everywhere. However, when we try to speedup non-numerical programs, which are used by ordinary users much especially on both desktop and mobile computers, by introducing parallel computation on multicore processors, it is necessary to strictly keep both control and data dependencies between parallel tasks in order to get the correct computation result. In many cases, parallel computation is difficult for non-numerical programs on multicore processors, since these dependencies are complicated. To solve this problem,we propose a method of speculative thread-level parallel computation along the hot path, the most frequently executed control flow, on entire program, assuming the use of the support hardware of speculative multithreading. This utilizes the general property of program codes, that is, the control flow of program tends to be highly biased to quite a small number of path even if the program code contains many possibilities of paths. Our method divides conventional serial program into thread codes along the hot path, and the thread codes are executed in parallel on each processor core on multicore processor. Preliminary performance evaluation by trace-based simulation using SPEC CINT2000, that is the collection of practical programs for performance valuation purpose, shows that the speedup of 2.48 times at maximum and 1.94 times on average can be expected by using our method, as compared to the serial execution.

A Massively Parallel Computational Method of Reading Index Files for SOAPsnv.

SOAPsnv is the software used for identifying the single nucleotide variation in cancer genes. However, its performance is yet to match the massive amount of data to be processed. Experiments reveal that the main performance bottleneck of SOAPsnv software is the pileup algorithm. The original pileup algorithm's I/O process is time-consuming and inefficient to read input files. Moreover, the scalability of the pileup algorithm is also poor. Therefore, we designed a new algorithm, named BamPileup, aiming to improve the performance of sequential read, and the new pileup algorithm implemented a parallel read mode based on index. Using this method, each thread can directly read the data start from a specific position. The results of experiments on the Tianhe-2 supercomputer show that, when reading data in a multi-threaded parallel I/O way, the processing time of algorithm is reduced to 3.9s and the application program can achieve a speedup up to 100×. Moreover, the scalability of the new algorithm is also satisfying.

Distributed Security-Constrained Unit Commitment for Large-Scale Power Systems

Independent system operators (ISOs) of electricity markets solve the security-constrained unit commitment (SCUC) problem to plan a secure and economic generation schedule. However, as the size of power systems increases, the current centralized SCUC algorithm could face critical challenges ranging from modeling accuracy to calculation complexity. This paper presents a distributed SCUC (D-SCUC) algorithm to accelerate the generation scheduling of large-scale power systems. In this algorithm, a power system is decomposed into several scalable zones which are interconnected through tie lines. Each zone solves its own SCUC problem and a parallel calculation method is proposed to coordinate individual D-SCUC problems. Several power systems are studied to show the effectiveness of the proposed algorithm.

A multi-thread parallel computation method for dynamic simulation of molecular weight distribution of multisite polymerization

Molecular weight distribution (MWD) is an important quality index of polymer products. Many methods have been proposed to dynamically simulate the MWD of polymerization, but these methods are normally designed for serial computations. In this paper, a multi-thread parallel computation method was proposed for multisite free-radical polymerization. Analysis of the relationship among different subtasks revealed a combined parallel strategy by fully exploiting the parallel feature of the process. A good performance was obtained to accelerate the dynamic simulation of MWD based on Flory method. We theoretically analyzed the speedup ratio (SR) and parallel efficiency (PE). Results showed that software algorithm and hardware configuration exhibited a good match. The efficiency of the proposed parallel method was presented through industrial slurry processes that used high-density polyethylene (HDPE).

Application of Parallel Computing Methods for Improving Efficiency of Optimization in Hydrologic and Water Quality Modeling

Abstract. Many heuristic algorithms for solving optimization problems have been proposed, and efforts have been made to improve their efficiency in finding optimal solutions. However, the sampling-based search strategies of the heuristic optimization algorithms still require considerable time to locate optima, particularly in the hyper-dimensional parameter space that are typical for distributed hydrologic models. This study evaluated the performance of parallel computing methods, ‘spmd’ and ‘parfor,’ of Matrix Laboratory (MATLAB) in improving computational efficiency of solving optimization problems using sampling-based algorithms in hydrologic and water quality modeling applications. The parallel computing methods were applied in calibrating 34 parameters for hydrologic simulation of the Soil and Water Assessment Tool (SWAT) model and in identifying watershed-scale optimum spatial distributions of corn stover removal using the AMALGAM optimization algorithm. In the applications, the SWAT model was calibrated with NSE of 0.86 and R 2 of 0.93 at the watershed outlet for the calibration period of 1993 to 1999 using the parallel computing methods, and the calibrated model provided NSE of 0.84 and R 2 of 0.92 for the validation period of 2000 to 2009. The results clearly demonstrated the effectiveness of the methods in reducing computational time of the parameter calibration and spatial optimization. The SWAT model code modification was not required, and changes in the source code of the original sequential optimization algorithm were minimal. Thus, the parallel computing methods of MATLAB are expected to provide simple and quick solutions for improving efficiency of solving the optimization problems in watershed model applications.

A Study on Performance Analysis of Different Parallel algorithms Implemented for Geometric Problems

There are different techniques to make parallel programs. In parallel programming, independent pieces of tasks that may run in parallel. The objective of parallel algorithm design is to develop parallel computational methods that run very fast with as few processors as possible. A proper implementation expected to give a better speedup in dual core machine and in a quad core machine. In this paper, the implementation of different parallel algorithms such as Naive Algorithm , Brute Force Algorithm , Graham Scan Algorithm on geometric problems is presented.

Direct solution method of efficient large-scale parallel computation for 3D turbulent Rayleigh-Bénard convection

Research of turbulence Rayleigh-Bénard convection with high Ra number is a hot topic in physics research in the world. DNS simulation is one of the important means to study the subject. The computing work is hard to achieve when the calculation size is increased and the grid number is bigger. Numerical simulation for high Ra turbulent convection faces some major challenges. So the direct (non iterative) solution method of efficient large-scale parallel computation for the 3D turbulent convection is created in this paper. Main difficulties are the parallel computing technology for the pressure Poisson equation. The mass efficient parallel approximate solution with the block tridiagonal equations of OpenMP and MPI used simultaneously after decoupling pressure Poisson equation using FFT is presented. Through the validation of the efficiency of this method in parallel computing, the new method for direct solution of parallel computing have good parallel efficiency and computational time. Results of thermal convection in 3D narrow cavity show that the convection characteristics calculated by using the new method is reasonable. The direct solution method for efficient large-scale parallel computation of 3D turbulent convection created in this paper also is likely to be a breakthrough in computing technology about efficient large-scale parallel computing incompressible NS equations in some special cases.

Parareal Algorithms Implemented with IMEX Runge-Kutta Methods

Parareal algorithm is a very powerful parallel computation method and has received much interest from many researchers over the past few years. The aim of this paper is to investigate the performance of parareal algorithm implemented with IMEX Runge-Kutta (RK) methods. A stability criterion of the parareal algorithm coupled with IMEX RK methods is established and the advantage (in the sense of stability) of implementing with this kind of RK methods is numerically investigated. Finally, numerical examples are given to illustrate the efficiency and performance of different parareal methods.

DEVELOPMENT OF A CORE THERMO-FLUID ANALYSIS CODE FOR PRISMATIC GAS COOLED REACTORS

A new computer code, named CORONA (Core Reliable Optimization and thermo-fluid Network Analysis), was developed for the core thermo-fluid analysis of a prismatic gas cooled reactor. The CORONA code is targeted for whole-core thermo-fluid analysis of a prismatic gas cooled reactor, with fast computation and reasonable accuracy. In order to achieve this target, the development of CORONA focused on (1) an efficient numerical method, (2) efficient grid generation, and (3) parallel computation. The key idea for the efficient numerical method of CORONA is to solve a three-dimensional solid heat conduction equation combined with one-dimensional fluid flow network equations. The typical difficulties in generating computational grids for a whole core analysis were overcome by using a basic unit cell concept. A fast calculation was finally achieved by a block-wise parallel computation method. The objective of the present paper is to summarize the motivation and strategy, numerical approaches, verification and validation, parallel computation, and perspective of the CORONA code.

Large Data Visualization Analysis and Application in Scientific Computing

With the rapid development of computer hardware, software and network technology, the size of data is growing exponentially. Recently, large scientific computing data visualization becomes one of the important researches in scientific computing. This paper introduces the definition of big data, visualization analysis field and the significance of scientific computing for large data visualization. In the following part, we analyze and discuss the traditional scientific visualization methods, and introduce the algorithms in classification according to different types of datasets. We analyze the weak points of traditional methods in visualization of scientific computing, and then introduce the method of large data parallel computing and rendering based on GPU. Finally, combining the research of our group on scientific computing theory and application, we point out the direction for future work of scientific computing visualization with large data development.

Parallel Evolutionary Algorithm for Computationally Expensive Single Criteria Design Optimization

The paper presents an approach to design optimization using parallel evolutionary algorithms. The only use of a simple evolutionary algorithm in order to generate the optimal solution for complex problems can be ineffective due to long calculation time. Thus a tournament evolutionary algorithm (EA) and a parallel computation method are proposed and used. The proposed EA does not require an analysis of the optimization model for each potential solution from evolutionary populations. The second element of the method consists in parallel running of evolutionary algorithms using multi-threads approach. The experiments were carried out for many different single design optimization problems and two of them are presented in the paper. The first problem considers a task of robot gripper mechanism optimization and the second one deals with the optimization of a shaft based on Finite Element Method analysis. From the generated results it is clear that proposed approach is a very effective tool for solving fairly complicated tasks considering both the accuracy and the time of calculation.

Comparison of Parallel Computing Methods for Fast Cone-Beam Reconstruction with Similar Optimization Strategies

To investigate the performance of acceleration technologies for FDK algorithm, two of the most common high-performance computing hardware, multi-core CPU and GPU, are involved in our experiment. Both runtime and accuracy are regarded as the standards to evaluate the performance of four different programming methods: OpenMP, GLSL, CUDA and OpenCL. All the methods are estimated with comparable optimization strategies. The experimental results show that GPU has higher efficiency than multi-core CPU for fast cone-beam reconstruction, meanwhile CUDA is the best choice for programming on the multi-processor featured GPU.

A Parallel Computing Method for Community Structure Detection Based on BSP Model

Since the conventional algorithm for community structure detection in a stand-alone environment cannot handle the giant network whose number of nodes is more than 10 5 , and the widely used MapReduce method has a limitation on dealing with excessive I/O operations during the iterative process, an efficient parallel computing method based on BSP (Bulk Synchronous Parallel) model for detecting community structure is proposed in this paper. The Fast Newman method is improved into parallel calculations with multiple steps under the framework of BSP model. It is more efficient to discover community structures in the large scale network. In order to testify the performance of the proposed method, a hama platform was built up on the same cluster of the hadoop platform. And a dataset, at a scale of 10 6 , was also simulated for the experiments. It is approved that the proposed method is not only able to solve the issue of memory overrun in the conventional calculation on a stand-alone computer, but also to improve the performance effectively comparing to the MapReduce model. The proposed method has high practical value in large scale networks.

Reliable and Fast Estimation of Recombination Rates by Convergence Diagnosis and Parallel Markov Chain Monte Carlo.

Genetic recombination is an essential event during the process of meiosis resulting in an exchange of segments between paired chromosomes. Estimating recombination rate is crucial for understanding the process of recombination. Experimental methods are normally difficult and limited to small scale estimations. Thus statistical methods using population genetics data are important for large-scale analysis. LDhat is an extensively used statistical method using rjMCMC algorithm to predict recombination rates. Due to the complexity of rjMCMC scheme, LDhat may take a long time for large SNP data sets. In addition, rjMCMC parameters should be manually defined in the original program which directly impact results. To address these issues, we designed an improved algorithm based on LDhat implementing MCMC convergence diagnostic algorithms to automatically predict values of parameters and monitor the mixing process. Then parallel computation methods were employed to further accelerate the new program. The new algorithms have been tested on ten samples from HapMap phase 2 data set. The results were compared with previous code and showed nearly identical output. However, our new methods achieved significant acceleration proving that they are more efficient and reliable for the estimation of recombination rates. The stand-alone package is freely available for download http://www.ntu.edu.sg/home/zhengjie/software/CPLDhat.