Accelerating the RTTOV-7 IASI and AMSU-A radiative transfer models on graphics processing units: evaluating central processing unit/graphics processing unit-hybrid and pure-graphics processing unit approaches

2011 ◽  
Vol 5 (1) ◽  
pp. 051503 ◽  
Author(s):  
Jarno Mielikainen
Keyword(s):  
Radiative Transfer ◽  
Graphics Processing Units ◽  
Graphics Processing Unit ◽  
Central Processing Unit ◽  
Processing Unit ◽  
Central Processing ◽  
Radiative Transfer Models ◽  
Graphics Processing ◽  
Transfer Models

scholarly journals Graphics processing unit implementation of the F-statistic for continuous gravitational wave searches

2021 ◽  
Author(s):  
Liam Dunn ◽  
Patrick Clearwater ◽  
Andrew Melatos ◽  
Karl Wette
Keyword(s):  
Gravitational Wave ◽  
Graphics Processing Units ◽  
Graphics Processing Unit ◽  
Computational Cost ◽  
Processing Unit ◽  
Central Processing ◽  
Long Baseline ◽  
Using Data ◽  
Graphics Processing ◽  
Gpu Implementation

Abstract The F-statistic is a detection statistic used widely in searches for continuous gravitational waves with terrestrial, long-baseline interferometers. A new implementation of the F-statistic is presented which accelerates the existing "resampling" algorithm using graphics processing units (GPUs). The new implementation runs between 10 and 100 times faster than the existing implementation on central processing units without sacrificing numerical accuracy. The utility of the GPU implementation is demonstrated on a pilot narrowband search for four newly discovered millisecond pulsars in the globular cluster Omega Centauri using data from the second Laser Interferometer Gravitational-Wave Observatory observing run. The computational cost is 17:2 GPU-hours using the new implementation, compared to 1092 core-hours with the existing implementation.

scholarly journals Paralelização do Algoritmo Floyd-Warshall usando GPU

2013 ◽  
Author(s):  
Roussian R. A. Gaioso ◽  
Walid A. R. Jradi ◽  
Lauro C. M. de Paula ◽  
Wanderley De S. Alencar ◽  
Wellington S. Martins ◽  
...  
Keyword(s):  
Graphics Processing Unit ◽  
Central Processing Unit ◽  
Processing Unit ◽  
Central Processing ◽  
Graphics Processing

Este artigo apresenta uma implementação paralela baseada em Graphics Processing Unit (GPU) para o problema da identificação dos caminhos mínimos entre todos os pares de vértices em um grafo. A implementação é baseada no algoritmo Floyd-Warshall e tira o máximo proveito da arquitetura multithreaded das GPUs atuais. Nossa solução reduz a comunicação entre a Central Processing Unit (CPU) e a GPU, melhora a utilização dos Streaming Multiprocessors (SMs) e faz um uso intensivo de acesso aglutinado em memória para otimizar o acesso de dados do grafo. A vantagem da implementação proposta é demonstrada por vários grafos gerados aleatoriamente utilizando a ferramenta GTgraph. Grafos contendo milhares de vértices foram gerados e utilizados nos experimentos. Os resultados mostraram um excelente desempenho em diversos grafos, alcançando ganhos de até 149x, quando comparado com uma implementação sequencial, e superando implementações tradicionais por um fator de quase quatro vezes. Nossos resultados confirmam que implementações baseadas em GPU podem ser viáveis mesmo para algoritmos de grafos cujo acessos à memória e distribuição de trabalho são irregulares e causam dependência de dados.

scholarly journals Ray-based modeling and imaging in viscoelastic media using graphics processing units

Geophysics ◽  
2019 ◽  
Vol 84 (5) ◽  
pp. S425-S436
Author(s):  
Martin Sarajaervi ◽  
Henk Keers
Keyword(s):  
Seismic Data ◽  
Graphics Processing Units ◽  
Graphics Processing Unit ◽  
Parallel Implementation ◽  
Processing Unit ◽  
Central Processing ◽  
Imaging Results ◽  
Viscoelastic Modeling ◽  
Graphics Processing ◽  
Complex Valued

In seismic data processing, the amplitude loss caused by attenuation should be taken into account. The basis for this is provided by a 3D attenuation model described by the quality factor [Formula: see text], which is used in viscoelastic modeling and imaging. We have accomplished viscoelastic modeling and imaging using ray theory and the ray-Born approximation. This makes it possible to take [Formula: see text] into account using complex-valued and frequency-dependent traveltimes. We have developed a unified parallel implementation for modeling and imaging in the frequency domain and carried out the numerical integration on a graphics processing unit. A central part of the implementation is an efficient technique for computing large integrals. We applied the integration method to the 3D SEG/EAGE overthrust model to generate synthetic seismograms and imaging results. The attenuation effects are accurately modeled in the seismograms and compensated for in the imaging algorithm. The results indicate a significant improvement in computational efficiency compared to a parallel central processing unit baseline.

scholarly journals GPU accelerated computation of fast spectral transforms

2011 ◽  
Vol 24 (3) ◽  
pp. 483-499
Author(s):  
Dusan Gajic ◽  
Radomir Stankovic
Keyword(s):  
Graphics Processing Units ◽  
Fast Algorithms ◽  
Central Processing Unit ◽  
Processing Unit ◽  
Memory Transfer ◽  
Simple Arithmetic ◽  
Central Processing ◽  
Graphics Processing ◽  
Spectral Transforms ◽  
Gpu Implementation

This paper discusses techniques for accelerated computation of several fast spectral transforms on graphics processing units (GPUs) using the Open Computing Language (OpenCL). We present a reformulation of fast algorithms which takes into account peculiar properties of transforms to make them suitable for the GPU implementation. A special attention is paid to the organization of computations, memory transfer reductions, impact of integer and Boolean arithmetic, different structure of algorithms, etc. Performance of the GPU implementations is compared with the classical C/C++ implementations for the central processing unit (CPU). Experiments confirm that, even though the spectral transforms considered involve only simple arithmetic, significant speedups are achieved by implementing the algorithms in OpenCL and performing them on the GPU.

History and Evolution of GPU Architecture

2016 ◽  
pp. 109-135
Author(s):  
Prashanta Kumar Das ◽  
Ganesh Chandra Deka
Keyword(s):  
Image Processing ◽  
Graphics Processing Unit ◽  
Hardware Architecture ◽  
Central Processing Unit ◽  
Processing Unit ◽  
3D Image ◽  
Central Processing ◽  
Graphics Processing ◽  
Graphics Engine ◽  
Gpu Architecture

The Graphics Processing Unit (GPU) is a specialized and highly parallel microprocessor designed to offload 2D/3D image from the Central Processing Unit (CPU) to expedite image processing. The modern GPU is not only a powerful graphics engine, but also a parallel programmable processor with high precision and powerful features. It is forcasted that by 2020, 48 Core GPU will be available while by 2030 GPU with 3000 core is likely to be available.This chapter describes the chronology of evolution of GPU hardware architecture and the future ahead.

scholarly journals CENTRAL PROCESSING UNIT-GRAPHICS PROCESSING UNIT COMPUTING SCHEME FOR MULTI-OBJECT TRACKING IN SURVEILLANCE

2017 ◽  
Vol 10 (13) ◽  
pp. 251
Author(s):  
Ankush Rai ◽  
Jagadeesh Kannan R
Keyword(s):  
Gpu Computing ◽  
Graphics Processing Unit ◽  
Research Work ◽  
Central Processing Unit ◽  
Processing Unit ◽  
Minimal Processing ◽  
Central Processing ◽  
Gpu Processing ◽  
Graphics Processing ◽  
Computing Scheme

This research work presents a novel central processing unit-graphics processing unit (CPU-GPU) computing scheme for multiple object trackingduring a surveillance operation. This facilitates nonlinear computational jobs to avail completion of computation in minimal processing time for tracking function. The work is divided into two essential objectives. First is to dynamically divide the processing operations into parallel units, and second is to reduce the communication between CPU-GPU processing units.

Sign in / Sign up

Export Citation Format

Share Document