Parallel Computing on Various Platforms - A Case Study.

1994 ◽  
Author(s):  
B. N. Srivastava
Keyword(s):  
Parallel Computing

scholarly journals Numerical Solution of Diffusion Models in Biomedical Imaging on Multicore Processors

2011 ◽  
Vol 2011 ◽  
pp. 1-16 ◽  
Author(s):  
Luisa D'Amore ◽  
Daniela Casaburi ◽  
Livia Marcellino ◽  
Almerico Murli
Keyword(s):  
Image Analysis ◽  
Parallel Computing ◽  
Multicore Processors ◽  
Nonlinear Partial Differential Equations ◽  
Numerical Algorithms ◽  
Computing Environment ◽  
Fully Implicit ◽  
Discretization Schemes ◽  
Comparison Criteria

In this paper, we consider nonlinear partial differential equations (PDEs) of diffusion/advection type underlying most problems in image analysis. As case study, we address the segmentation of medical structures. We perform a comparative study of numerical algorithms arising from using the semi-implicit and the fully implicit discretization schemes. Comparison criteria take into account both the accuracy and the efficiency of the algorithms. As measure of accuracy, we consider the Hausdorff distance and the residuals of numerical solvers, while as measure of efficiency we consider convergence history, execution time, speedup, and parallel efficiency. This analysis is carried out in a multicore-based parallel computing environment.

scholarly journals Contextual Contracts for Component-Based Resource Abstraction in a Cloud of HPC Services

2019 ◽  
Author(s):  
Wagner Al Alam ◽  
Francisco Carvalho Junior
Keyword(s):  
Cloud Computing ◽  
Parallel Computing ◽  
Large Scale ◽  
Matrix Multiplication ◽  
Small Scale ◽  
Computing Systems ◽  
Computing Platform ◽  
Computing Platforms

The efforts to make cloud computing suitable for the requirements of HPC applications have motivated us to design HPC Shelf, a cloud computing platform of services for building and deploying parallel computing systems for large-scale parallel processing. We introduce Alite, the system of contextual contracts of HPC Shelf, aimed at selecting component implementations according to requirements of applications, features of targeting parallel computing platforms (e.g. clusters), QoS (Quality-of-Service) properties and cost restrictions. It is evaluated through a small-scale case study employing a componentbased framework for matrix-multiplication based on the BLAS library.

scholarly journals Parallel Computing for Rolling Mill Process with a Numerical Treatment of the LQR Problem

2020 ◽  
Vol 1 (1) ◽  
pp. 11-30
Author(s):  
John Anderson Gómez Múnera ◽  
Alejandro Giraldo Quintero
Keyword(s):  
Parallel Computing ◽  
Rolling Mill ◽  
Application Programming Interface ◽  
Processing Elements ◽  
Lqr Problem ◽  
Application Programming ◽  
Multiple Processing ◽  
Mill Process ◽  
Programming Interface

The considerable increase in computation of the optimal control problems has in many cases overflowed the computing capacity available to handle complex systems in real time. For this reason, alternatives such as parallel computing are studied in this article, where the problem is worked out by distributing the tasks among several processors in order to accelerate the computation and to analyze and investigate the reduction of the total time of calculation the incremental gradually the processors used in it. We explore the use of these methods with a case study represented in a rolling mill process, and in turn making use of the strategy of updating the Phase Finals values for the construction of the final penalty matrix for the solution of the differential Riccati Equation. In addition, the order of the problem studied is increasing gradually for compare the improvements achieved in the models with major dimension. Parallel computing alternatives are also studied through multiple processing elements within a single machine or in a cluster via OpenMP, which is an application programming interface (API) that allows the creation of shared memory programs.

A case study in parallel computing: I. Homogeneous turbulence on a hypercube

1991 ◽  
Vol 6 (1) ◽  
pp. 27-45 ◽  
Author(s):  
Eric Jackson ◽  
Zhen-Su She ◽  
Steven A. Orszag
Keyword(s):  
Parallel Computing ◽  
Homogeneous Turbulence

scholarly journals Quantifying the Performances of the Semi-Distributed Hydrologic Model in Parallel Computing—A Case Study

Water ◽  
2019 ◽  
Vol 11 (4) ◽  
pp. 823 ◽  
Author(s):  
JungJin Kim ◽  
Jae Ryu
Keyword(s):  
Parallel Computing ◽  
Execution Time ◽  
Hydrological Modeling ◽  
Cost Effective ◽  
Hydrologic Model ◽  
Coefficient Of Determination ◽  
Partial Sums ◽  
Urban Rural ◽  
Set Up

The research features how parallel computing can advance hydrological performances associated with different calibration schemes (SCOs). The result shows that parallel computing can save up to 90% execution time, while achieving 81% simulation improvement. Basic statistics, including (1) index of agreement (D), (2) coefficient of determination (R2), (3) root mean square error (RMSE), and (4) percentage of bias (PBIAS) are used to evaluate simulation performances after model calibration in computer parallelism. Once the best calibration scheme is selected, additional efforts are made to improve model performances at the selected calibration target points, while the Rescaled Adjusted Partial Sums (RAPS) is used to evaluate the trend in annual streamflow. The qualitative result of reducing execution time by 86% on average indicates that parallel computing is another avenue to advance hydrologic simulations in the urban-rural interface, such as the Boise River Watershed, Idaho. Therefore, this research will provide useful insights for hydrologists to design and set up their own hydrological modeling exercises using the cost-effective parallel computing described in this case study.

scholarly journals Use of GPU Computing for Uncertainty Quantification in Computational Mechanics: A Case Study

2011 ◽  
Vol 19 (4) ◽  
pp. 199-212 ◽  
Author(s):  
Gaurav ◽  
Steven F. Wojtkiewicz
Keyword(s):  
Parallel Computing ◽  
Uncertainty Quantification ◽  
Graphics Processing Units ◽  
Computational Mechanics ◽  
Gpu Computing ◽  
Single Instruction Multiple Data ◽  
Performance Constraints ◽  
Multiple Data ◽  
Graphics Processing

Graphics processing units (GPUs) are rapidly emerging as a more economical and highly competitive alternative to CPU-based parallel computing. As the degree of software control of GPUs has increased, many researchers have explored their use in non-gaming applications. Recent studies have shown that GPUs consistently outperform their best corresponding CPU-based parallel computing alternatives in single-instruction multiple-data (SIMD) strategies. This study explores the use of GPUs for uncertainty quantification in computational mechanics. Five types of analysis procedures that are frequently utilized for uncertainty quantification of mechanical and dynamical systems have been considered and their GPU implementations have been developed. The numerical examples presented in this study show that considerable gains in computational efficiency can be obtained for these procedures. It is expected that the GPU implementations presented in this study will serve as initial bases for further developments in the use of GPUs in the field of uncertainty quantification and will (i) aid the understanding of the performance constraints on the relevant GPU kernels and (ii) provide some guidance regarding the computational and the data structures to be utilized in these novel GPU implementations.

Parallel computing: A case study

1989 ◽  
Vol 56 (1) ◽  
pp. 63-67 ◽  
Author(s):  
Jan F.W. Slaets ◽  
Gonzalo Travieso
Keyword(s):  
Parallel Computing
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