.png){kind=icon}
返回
Parallel drainage network computation on CUDA
.png){kind=icon}
PRE
AI
DOI：10.1016/j.cageo.2009.07.005.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
Drainage networks determination from digital elevation models (DEM) has been a widely studied problem in the last three decades. During this time, satellite technology has been improving and optimizing digitalized images, and computers have been increasing their capabilities to manage such a huge quantity of information. The rapid growth of CPU power and memory size has concentrated the discussion of DEM algorithms on the accuracy of their results more than their running times. However, obtaining improved running times remains crucial when DEM dimensions and their resolutions increase. Parallel computation provides an opportunity to reduce run times. Recently developed graphics processing units (GPUs) are computationally fast not only in Computer Graphics but in General Purpose Computation, the so-called GPGPU. In this paper we explore the parallel characteristics of these GPUs for drainage network determination, using the C-oriented language of CUDA developed by NVIDIA. The results are simple algorithms that run on low-cost technology with a high performance response, obtaining CPU improvements of up to 8x. (C) 2009 Elsevier Ltd. All rights reserved.
Keyword:
GPU
GPGPU
CUDA
Drainage network
D8 algorithm
AI总结
对已上传原文的论文进行重点信息的提取，主要内容包括：简要概述、研究摘要、背景介绍、关键亮点、图文解析、展望与总结。
期刊
C
IF:
4.4
论文数:
5.0K
被引数:
1.5W
机构
引用论文
.png){kind=icon}
Data Mining of Protein Sequences with Amino Acid Position-Based Feature Encoding Technique基于氨基酸位置特征编码技术的蛋白质序列数据挖掘
PREAI
分享收藏
A new metallo-proteinase inhibitor (FMPI) produced by Streptomyces rishiriensis NK-122.一株由Streptomyces rishiriensis NK-122产生的新的金属蛋白酶抑制剂（FMPI）。
OAAI
OAAI分享收藏
查看更多内容