Parallel implementation for SAM algorithm based on GPU and distributed computing

Haicheng Qu; Junping Zhang; Yushi Chen; Hao Chen; Zhouhan Lin

doi:10.1109/IGARSS.2012.6350514

Parallel implementation for SAM algorithm based on GPU and distributed computing

Haicheng Qu^*
, Junping Zhang
, Yushi Chen
, Hao Chen
, Zhouhan Lin

^*Corresponding author for this work

School of Electronics and Information Engineering

Harbin Institute of Technology
Liaoning Technical University

Research output: Contribution to conference › Paper › peer-review

2 Scopus citations

Abstract

Advances in sensor and computer technology are revolutionizing the way that remote sensing data with hundreds or even thousands of channels for the same area on the surface of the earth is collected, managed and analyzed. In this paper, the classical Spectral Angle Mapper (SAM) algorithm, which is fit for parallel and distributed computing, is implemented by using Graphic Processing Units (GPU) and distributed cluster respectively to accelerate the computations. A quantitative performance comparison between Compute Unified Device Architecture (CUDA) and Matlab platform is given by analyzing result of different parallel architectures' implementation of the same SAM algorithm.

Original language	English
Pages	4074-4077
Number of pages	4
DOIs	https://doi.org/10.1109/IGARSS.2012.6350514
State	Published - 2012
Event	2012 32nd IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2012 - Munich, Germany Duration: 22 Jul 2012 → 27 Jul 2012

Conference

Conference	2012 32nd IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2012
Country/Territory	Germany
City	Munich
Period	22/07/12 → 27/07/12

Keywords

GPU
SAM
distributed computing
high-performance computing

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10.1109/IGARSS.2012.6350514

Cite this

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title = "Parallel implementation for SAM algorithm based on GPU and distributed computing",

abstract = "Advances in sensor and computer technology are revolutionizing the way that remote sensing data with hundreds or even thousands of channels for the same area on the surface of the earth is collected, managed and analyzed. In this paper, the classical Spectral Angle Mapper (SAM) algorithm, which is fit for parallel and distributed computing, is implemented by using Graphic Processing Units (GPU) and distributed cluster respectively to accelerate the computations. A quantitative performance comparison between Compute Unified Device Architecture (CUDA) and Matlab platform is given by analyzing result of different parallel architectures' implementation of the same SAM algorithm.",

keywords = "GPU, SAM, distributed computing, high-performance computing",

author = "Haicheng Qu and Junping Zhang and Yushi Chen and Hao Chen and Zhouhan Lin",

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doi = "10.1109/IGARSS.2012.6350514",

language = "英语",

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AU - Qu, Haicheng

AU - Zhang, Junping

AU - Chen, Yushi

AU - Chen, Hao

AU - Lin, Zhouhan

PY - 2012

Y1 - 2012

N2 - Advances in sensor and computer technology are revolutionizing the way that remote sensing data with hundreds or even thousands of channels for the same area on the surface of the earth is collected, managed and analyzed. In this paper, the classical Spectral Angle Mapper (SAM) algorithm, which is fit for parallel and distributed computing, is implemented by using Graphic Processing Units (GPU) and distributed cluster respectively to accelerate the computations. A quantitative performance comparison between Compute Unified Device Architecture (CUDA) and Matlab platform is given by analyzing result of different parallel architectures' implementation of the same SAM algorithm.

AB - Advances in sensor and computer technology are revolutionizing the way that remote sensing data with hundreds or even thousands of channels for the same area on the surface of the earth is collected, managed and analyzed. In this paper, the classical Spectral Angle Mapper (SAM) algorithm, which is fit for parallel and distributed computing, is implemented by using Graphic Processing Units (GPU) and distributed cluster respectively to accelerate the computations. A quantitative performance comparison between Compute Unified Device Architecture (CUDA) and Matlab platform is given by analyzing result of different parallel architectures' implementation of the same SAM algorithm.

KW - GPU

KW - SAM

KW - distributed computing

KW - high-performance computing

UR - https://www.scopus.com/pages/publications/84873134313

U2 - 10.1109/IGARSS.2012.6350514

DO - 10.1109/IGARSS.2012.6350514

M3 - 论文

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T2 - 2012 32nd IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2012

Y2 - 22 July 2012 through 27 July 2012

ER -

Parallel implementation for SAM algorithm based on GPU and distributed computing

Abstract

Conference

Keywords

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