scholarly journals TESLA GPUs versus MPI with OpenMP for the Forward Modeling of Gravity and Gravity Gradient of Large Prisms Ensemble

2013 ◽  
Vol 2013 ◽  
pp. 1-15 ◽  
Author(s):  
Carlos Couder-Castañeda ◽  
Carlos Ortiz-Alemán ◽  
Mauricio Gabriel Orozco-del-Castillo ◽  
Mauricio Nava-Flores
Keyword(s):  
Parallel Computing ◽  
Graphics Processing Units ◽  
Forward Modeling ◽  
Gravity Gradient ◽  
Constant Density ◽  
Gravitational Fields ◽  
Design And Implementation ◽  
Cuda Technology ◽  
Performance Results ◽  
Graphics Processing

An implementation with the CUDA technology in a single and in several graphics processing units (GPUs) is presented for the calculation of the forward modeling of gravitational fields from a tridimensional volumetric ensemble composed by unitary prisms of constant density. We compared the performance results obtained with the GPUs against a previous version coded in OpenMP with MPI, and we analyzed the results on both platforms. Today, the use of GPUs represents a breakthrough in parallel computing, which has led to the development of several applications with various applications. Nevertheless, in some applications the decomposition of the tasks is not trivial, as can be appreciated in this paper. Unlike a trivial decomposition of the domain, we proposed to decompose the problem by sets of prisms and use different memory spaces per processing CUDA core, avoiding the performance decay as a result of the constant calls to kernels functions which would be needed in a parallelization by observations points. The design and implementation created are the main contributions of this work, because the parallelization scheme implemented is not trivial. The performance results obtained are comparable to those of a small processing cluster.

scholarly journals Passive Radar Parallel Processing Using General-Purpose Computing on Graphics Processing Units

2015 ◽  
Vol 61 (4) ◽  
pp. 357-363 ◽  
Author(s):  
Karolina Szczepankiewicz ◽  
Mateusz Malanowski ◽  
Michał Szczepankiewicz
Keyword(s):  
Parallel Processing ◽  
Graphics Processing Units ◽  
General Purpose ◽  
Passive Radar ◽  
Effective Implementation ◽  
University Of Technology ◽  
Fm Radio ◽  
Cuda Technology ◽  
Performance Results ◽  
Graphics Processing

Abstract In the paper an implementation of signal processing chain for a passive radar is presented. The passive radar which was developed at the Warsaw University of Technology, uses FM radio and DVB-T television transmitters as ”illuminators of opportunity”. As the computational load associated with passive radar processing is very high, NVIDIA CUDA technology has been employed for effective implementation using parallel processing. The paper contains the description of the algorithms implementation and the performance results analysis.

Parallel computing for simultaneous iterative tomographic imaging by graphics processing units

2016 ◽  
Author(s):  
Pedro D. Bello-Maldonado ◽  
Ricardo López ◽  
Colleen Rogers ◽  
Yuanwei Jin ◽  
Enyue Lu
Keyword(s):  
Parallel Computing ◽  
Graphics Processing Units ◽  
Tomographic Imaging ◽  
Graphics Processing

scholarly journals Time-Domain Power Quality State Estimation Based on Kalman Filter Using Parallel Computing on Graphics Processing Units

IEEE Access ◽  
2018 ◽  
Vol 6 ◽  
pp. 21152-21163 ◽  
Author(s):  
Rafael Cisneros-Magana ◽  
Aurelio Medina ◽  
Venkata Dinavahi ◽  
Antonio Ramos-Paz
Keyword(s):  
Parallel Computing ◽  
Kalman Filter ◽  
State Estimation ◽  
Power Quality ◽  
Time Domain ◽  
Graphics Processing Units ◽  
Graphics Processing

Tesseroids: Forward-modeling gravitational fields in spherical coordinates

Geophysics ◽  
2016 ◽  
Vol 81 (5) ◽  
pp. F41-F48 ◽  
Author(s):  
Leonardo Uieda ◽  
Valéria C. F. Barbosa ◽  
Carla Braitenberg
Keyword(s):  
Gravitational Potential ◽  
Computation Time ◽  
Forward Modeling ◽  
Gravity Gradient ◽  
Maximum Relative Error ◽  
Gravitational Acceleration ◽  
Spherical Coordinates ◽  
Gravity Gradients ◽  
Gravitational Fields ◽  
Adaptive Discretization

We have developed the open-source software Tesseroids, a set of command-line programs to perform forward modeling of gravitational fields in spherical coordinates. The software is implemented in the C programming language and uses tesseroids (spherical prisms) for the discretization of the subsurface mass distribution. The gravitational fields of tesseroids are calculated numerically using the Gauss-Legendre quadrature (GLQ). We have improved upon an adaptive discretization algorithm to guarantee the accuracy of the GLQ integration. Our implementation of adaptive discretization uses a “stack-based” algorithm instead of recursion to achieve more control over execution errors and corner cases. The algorithm is controlled by a scalar value called the distance-size ratio ([Formula: see text]) that determines the accuracy of the integration as well as the computation time. We have determined optimal values of [Formula: see text] for the gravitational potential, gravitational acceleration, and gravity gradient tensor by comparing the computed tesseroids effects with those of a homogenous spherical shell. The values required for a maximum relative error of 0.1% of the shell effects are [Formula: see text] for the gravitational potential, [Formula: see text] for the gravitational acceleration, and [Formula: see text] for the gravity gradients. Contrary to previous assumptions, our results show that the potential and its first and second derivatives require different values of [Formula: see text] to achieve the same accuracy. These values were incorporated as defaults in the software.

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.

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