Parallel Computing Environments: A Review

Dynamic responses considering fluid structure interaction (FSI) is important in many engineering fields and some of the FSI phenomena are treated as an acoustic fluid and structure interaction (AFSI) problem. The dynamic interactions between the fluid and structure can change dynamic characteristics of structures and their responses to external excitation such as seismic loading. The authors have developed a coupled simulation system for the large scale AFSI problems using an iterative partitioned coupling technique. In the system, the authors employed ADVENTURE system which adopted an efficient preconditioned iterative linear algebraic solver, and ADVENTURE Coupler is used to handle interface variable efficiently on various parallel computational environments. The authors employ Broyden method for updating interface accelerations to obtain the robust and fast convergence property of fixed point iterations. This paper presents the overview of the coupled analysis system and the results of its application to several AFSI problems are shown. The system runs efficiently in a parallel environment and it is capable for analyses of complex shaped three dimensional structures with more than 20 million degrees of freedom model.

Application of the parallel diagonal dominant algorithm for the incompressible Navier-Stokes equations

Fully sequential ranking-and-selection (R&S) procedures to find the best from a finite set of simulated alternatives are often designed to be implemented on a single processor. However, parallel computing environments, such as multi-core personal computers and many-core servers, are becoming ubiquitous and easily accessible for ordinary users. In this paper, we propose two types of fully sequential procedures that can be used in parallel computing environments. We call them vector-filling procedures and asymptotic parallel selection procedures, respectively. Extensive numerical experiments show that the proposed procedures can take advantage of multiple parallel processors and solve large-scale R&S problems.

We describe total congestion 1 embeddings of complete binary trees into three dimensional grids with expansion ratios 1.172 and 1.25. That is, we give a one-to-one embedding of any complete binary tree into a hexahedron shaped grid such that: the number of nodes in the grid is at most 1.172 (1.25) times the number of nodes in the tree; and no tree nodes or edges occupy the same grid positions. The first strategy embeds trees into cube shaped 3D grids. That is, 3D grids in which all dimensions are roughly equal in size. The second strategy embeds trees into flat 3D grid shapes. That is, it maps complete binary trees into 3D grids with a fixed, small number of layers. An application suggests which embedding to use. For simulations in parallel computer environments, or possibly graph drawing, a cube shaped 3D grid is appropriate. For the sake of VLSI, or other graph drawing applications, embeddings with a small number of layers are better.

During the development and parametrization of 2D image-processing algorithms for surface inspection uses, you need to test a huge amount of image-data for each modification of the algorithms or parameters. For algorithm runtimes up to several seconds, this will take a long time. To speed up this process it is recommended to distribute the computation in a parallel computation environment. Compute Grids, which use the unused resources of existing hardware are the most cost efficient way to solve this problem. The most existing Grid-Concepts are based on flat connection structures with a scheduler on the top; for high job-rates the scheduler becomes the bottleneck of the whole system. Concepts to solve this problem organize the nodes in tree-structures to discharge the central scheduler. In heterogeneous Desktop-Grids where the different nodes are widely distributed the usually used random arrangement of the nodes in the tree-structure can be counterproductive, because the bandwidthes and latencies in a Grid can be varying. In this paper we will show a solution to arrange the nodes of the grid optimized by bandwidth and latency, using modified spanning-tree algorithms, so that the average response time is reduced and in result of this the job-throughput of the Compute-Grid is increased.

The loss of orthogonality in the Gram-Schmidt orthogonalization process

The component-mode method in a parallel computer environment

Nowadays, research and development for optimal setting control (OSC) of complex industrial processes is becoming a hot topic. However, there is no configurable software for OSC design. In this paper, a novel software platform is proposed. The major superiority is that it not only integrates intelligent and data-driven mainstream algorithms within a unified framework, but also provides a powerful controller design tool and employs a Petri net engine to schedule control units in a parallel computation environment. The overall framework and key technologies are introduced in detail. The platform is verified and validated through a case of application to the design and development of OSC system of grinding process in a hardware-in-the-loop simulation environment.

The demand for processing power has been ever increasing with the growth of high-performance computing (HPC) applications and so have the constraints restricting the solutions to such requirements. High-performance distributed and parallel computing and custom-built, hardware-based computing have attempted to address this problem with some success but at a substantial cost. Recently, systems augmented with Field-Programmable Gate Arrays (FPGAs) offering a fusion of traditional parallel and distributed machines with customizable and dynamically reconfigurable hardware have emerged as a cost-effective alternative to traditional systems. However, providing a robust runtime environment for such systems to which HPC users have become accustomed has been fraught with numerous challenges. Dynamic scheduling of large-scale HPC applications in such parallel reconfigurable computing (RC) environments is one such challenge and has not been sufficiently studied to our knowledge. In this paper, we simulatively analyze the performance of several common HPC scheduling heuristics that can be used by an automated job management service to schedule application tasks on a parallel RC system. We also present a performance prediction model which the scheduling heuristics employ to schedule several common HPC applications on a collection of typical FPGA processing platforms.

Land-surface temperature (LST) is a very important parameter in the geosciences. Conventional LST retrieval is based on large-scale remote-sensing (RS) images where split-window algorithms are usually employed via a traditional stand-alone method. When using the environment to visualize images (ENVI) software to carry out LST retrieval of large time-series datasets of infrared RS images, the processing time taken for traditional stand-alone servers becomes untenable. To address this shortcoming, cluster-based parallel computing is an ideal solution. However, traditional parallel computing is mostly based on the Linux environment, while the LST algorithm developed within the ENVI interactive data language (IDL) can only be run in the Windows environment in our project. To address this problem, we combine the characteristics of LST algorithms with parallel computing, and propose the design and implementation of a parallel LST retrieval algorithm using the message-passing interface (MPI) parallel-programming model on a Windows-based PC cluster platform. Furthermore, we present our solutions to the problems associated with performance bottlenecks and fault tolerance during the deployment stage. Our results show that, by improving the parallel environment of the storage system and network, one can effectively solve the stability issues of the parallel environment for large-scale RS data processing.

In this chapter, we give more detailed review of on-going research activities in virtual computing environment. The projects to be reviewed are not by no means a comprehensive list, but can be considered as a representative of the main project in this area.

A numerical method is developed for solving parabolic partial differential equations with integral boundary conditions. The method is moderately sixth-order accurate due to merging of sixth order finite difference scheme and fifth order Pade’s approximation. Simpson’s 1/3 rule is used to approximate integral conditions. The method does not involve the use of complex arithmetic and optimizes the results. It is observed that this numerical method can be easily coded on serial as well as parallel computers.

Implementation and performance optimization of a parallel contour line generation algorithm

The goal of this dissertation is two-fold. The first part concerns the development of a numerical method for solving Maxwell's equations on unstructured hexahedral grids that employs both high order spatial and high order temporal discretizations. The second part involves the use of this method as a computational tool to perform high fidelity simulations of various electromagnetic devices such as optical transmission lines and photonic crystal structures to yield a level of accuracy that has previously been computationally cost prohibitive. This work is based on the initial research of Daniel White who developed a provably stable, charge and energy conserving method for solving Maxwell's equations in the time domain that is second order accurate in both space and time. The research presented here has involved the generalization of this procedure to higher order methods. High order methods are capable of yielding far more accurate numerical results for certain problems when compared to corresponding h-refined first order methods , and often times at a significant reduction in total computational cost. The first half of this dissertation presents the method as well as the necessary mathematics required for its derivation. The second half addresses the implementation of the method in a parallel computational environment, its validation using benchmark problems, and finally its use in large scale numerical simulations of electromagnetic transmission devices.

An asynchronous version of the optimized Schwarz method for the solution of differential equations on a parallel computational environment is studied. In a one-way subdivision of the computational domain, with and without overlap, the method is shown to converge when the optimal artificial interface conditions are used. Convergence is also proved for the Laplacian operator under very mild conditions on the size of the subdomains, when approximate (non-optimal) interface conditions are utilized. Numerical results are presented on a large three-dimensional problem on modern parallel clusters and supercomputers illustrating the efficiency of the asynchronous approach.