Another Parallelism Technique of GLCM Implementation Using CUDA Programming

2020 ◽  
Author(s):  
Teamsar Muliadi Panggabean ◽  
Mario Elyezer Simaremare ◽  
Rusmina Siahaan ◽  
Chandro Pardede ◽  
Wiwin Putri Gurning
Keyword(s):  
Cuda Programming

A CUDA programming toolkit on grids

2012 ◽  
Vol 3 (2/3) ◽  
pp. 97 ◽  
Author(s):  
Tyng Yeu Liang ◽  
Yu Wei Chang ◽  
Hung Fu Li
Keyword(s):  
Cuda Programming

Advanced CUDA Programming

2018 ◽  
pp. 287-313
Author(s):  
Bertil Schmidt ◽  
Jorge González-Domínguez ◽  
Christian Hundt ◽  
Moritz Schlarb
Keyword(s):  
Cuda Programming

scholarly journals GPU-accelerated SART reconstruction using the CUDA programming environment

2009 ◽  
Author(s):  
Benjamin Keck ◽  
Hannes Hofmann ◽  
Holger Scherl ◽  
Markus Kowarschik ◽  
Joachim Hornegger
Keyword(s):  
Programming Environment ◽  
Cuda Programming

CUDA Programming

2013 ◽  
Keyword(s):  
Cuda Programming

Introduction to GPU Computing and CUDA Programming: A Case Study on FDTD [EM Programmer's Notebook

2010 ◽  
Vol 52 (3) ◽  
pp. 116-122 ◽  
Author(s):  
Danilo De Donno ◽  
Alessandra Esposito ◽  
Luciano Tarricone ◽  
Luca Catarinucci
Keyword(s):  
Gpu Computing ◽  
Cuda Programming

HIGH SPEED COMPUTING OF ICE THICKNESS EQUATION FOR ICE SHEET MODEL

2016 ◽  
Vol 78 (8-2) ◽  
Author(s):  
Norma Alias ◽  
Masyitah Mohd Saidi
Keyword(s):  
High Speed ◽  
High Performance ◽  
Graphics Processing Unit ◽  
Ice Sheet ◽  
Coupled Model ◽  
Vital Role ◽  
Ice Thickness ◽  
Processing Unit ◽  
Ice Flow ◽  
Cuda Programming

Two-dimensional (2-D) ice flow thermodynamics coupled model acts as a vital role for visualizing the ice sheet behaviours of the Antarctica region and the climate system. One of the parameters used in this model is ice thickness. Explicit method of finite difference method (FDM) is used to discretize the ice thickness equation. After that, the equation will be performed on Compute Unified Device Architecture (CUDA) programming by using Graphics Processing Unit (GPU) platform. Nowadays, the demand of GPU for solving the computational problem has been increasing due to the low price and high performance computation properties. This paper investigates the performance of GPU hardware supported by the CUDA parallel programming and capable to compute a large sparse complex system of the ice thickness equation of 2D ice flow thermodynamics model using multiple cores simultaneously and efficiently. The parallel performance evaluation (PPE) is evaluated in terms of execution time, speedup, efficiency, effectiveness and temporal performance.

Research on the single-host parallel computing with the local time step scheme for modeling of hydro-sediment-morphodynamic processes

2021 ◽  
Author(s):  
Zixiong Zhao ◽  
Peng Hu ◽  
Wei Li ◽  
Zhixian Cao ◽  
Zhiguo He
Keyword(s):  
Parallel Computing ◽  
Local Time ◽  
High Performance ◽  
Water Model ◽  
Time Step ◽  
Time Stepping ◽  
Computational Performance ◽  
Cuda Programming ◽  
Single Host ◽  
Algorithmic Acceleration

<p>In recent decades, computational hydraulics and sediment modelling have a great development due to compute technology. Applying a finite-volume Godunov-type hydrodynamic shallow water model with hydro-sediment-morphodynamic processes, this work demonstrates and analysis the ability of single-host parallel computing technology with algorithmic acceleration technology. This model is implemented for high-performance computing using the NVIDIA’s Compute Unified Device Architecture (CUDA) programming framework, using a domain decomposition technique and across multiple cores through an efficient implementation of the Open Multi-Processing (Open MP) architecture, and using an algorithmic acceleration technology named local time stepping scheme (LTS), which is capable of obtain much efficiency improvement via different time step sizes for different grid sizes. The model is applied for three cases, through which we compare the effectiveness of CPU, Open MP, Open MP+LTS, CUDA, and CUDA+LTS, demonstrating high computational performance across CUDA+LTS which can lead to speedups of 40 times with respect to CPU and high-precision results across CUDA +LTS.</p><p>KEY WORDS: Hydro-sediment-morphological modeling; local time step; Open MP; CUDA.</p>

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