Accelerating a ray launching model using GPU with CUDA

Zhuangzhuang Dai; Robert J. Watson

Accelerating a ray launching model using GPU with CUDA

Department of Electronic & Electrical Engineering

Research output: Chapter or section in a book/report/conference proceeding › Chapter in a published conference proceeding

Abstract

The high computational cost of accurate deterministic wave propagation models often prevent them from being used in channel modelling. In this work, we present our experience of attempts to accelerate our 2.5D ray launching model using GPUs (Graphic Processing Units), which continue to grow in popularity due to their vast computation capability. At the heart of this trial is an implementation of a ray-surface intersection detection function, which was found to be the bottleneck of serial CPU computation, using NVIDIA's CUDA (Compute Unified Device Architecture). Various optimization efforts are made to obtain the best overall performance. The intersection detection function executes seven times faster on a large urban scenario after acceleration on a modest laptop GPU. This paper details the implementation of the CUDA-based intersection detection function and presents the acceleration results for different simulated environments.

Original language	English
Title of host publication	Proceedings of 12th European Conference on Antennas and Propagation (EuCAP), 2018
Publisher	IET
Publication status	Published - 1 Jan 2018
Event	12th European Conference on Antennas and Propagation, EuCAP 2018 - London, UK United Kingdom Duration: 9 Apr 2018 → 13 Apr 2018

Conference

Conference	12th European Conference on Antennas and Propagation, EuCAP 2018
Country/Territory	UK United Kingdom
City	London
Period	9/04/18 → 13/04/18

Keywords

GPU
Propagation modelling acceleration
Ray-tracing

ASJC Scopus subject areas

Electrical and Electronic Engineering

Cite this

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title = "Accelerating a ray launching model using GPU with CUDA",

abstract = "The high computational cost of accurate deterministic wave propagation models often prevent them from being used in channel modelling. In this work, we present our experience of attempts to accelerate our 2.5D ray launching model using GPUs (Graphic Processing Units), which continue to grow in popularity due to their vast computation capability. At the heart of this trial is an implementation of a ray-surface intersection detection function, which was found to be the bottleneck of serial CPU computation, using NVIDIA's CUDA (Compute Unified Device Architecture). Various optimization efforts are made to obtain the best overall performance. The intersection detection function executes seven times faster on a large urban scenario after acceleration on a modest laptop GPU. This paper details the implementation of the CUDA-based intersection detection function and presents the acceleration results for different simulated environments.",

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author = "Zhuangzhuang Dai and Watson, {Robert J.}",

year = "2018",

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language = "English",

booktitle = "Proceedings of 12th European Conference on Antennas and Propagation (EuCAP), 2018",

publisher = "IET",

note = "12th European Conference on Antennas and Propagation, EuCAP 2018 ; Conference date: 09-04-2018 Through 13-04-2018",

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TY - GEN

T1 - Accelerating a ray launching model using GPU with CUDA

AU - Dai, Zhuangzhuang

AU - Watson, Robert J.

PY - 2018/1/1

Y1 - 2018/1/1

N2 - The high computational cost of accurate deterministic wave propagation models often prevent them from being used in channel modelling. In this work, we present our experience of attempts to accelerate our 2.5D ray launching model using GPUs (Graphic Processing Units), which continue to grow in popularity due to their vast computation capability. At the heart of this trial is an implementation of a ray-surface intersection detection function, which was found to be the bottleneck of serial CPU computation, using NVIDIA's CUDA (Compute Unified Device Architecture). Various optimization efforts are made to obtain the best overall performance. The intersection detection function executes seven times faster on a large urban scenario after acceleration on a modest laptop GPU. This paper details the implementation of the CUDA-based intersection detection function and presents the acceleration results for different simulated environments.

AB - The high computational cost of accurate deterministic wave propagation models often prevent them from being used in channel modelling. In this work, we present our experience of attempts to accelerate our 2.5D ray launching model using GPUs (Graphic Processing Units), which continue to grow in popularity due to their vast computation capability. At the heart of this trial is an implementation of a ray-surface intersection detection function, which was found to be the bottleneck of serial CPU computation, using NVIDIA's CUDA (Compute Unified Device Architecture). Various optimization efforts are made to obtain the best overall performance. The intersection detection function executes seven times faster on a large urban scenario after acceleration on a modest laptop GPU. This paper details the implementation of the CUDA-based intersection detection function and presents the acceleration results for different simulated environments.

KW - GPU

KW - Propagation modelling acceleration

KW - Ray-tracing

UR - http://www.scopus.com/inward/record.url?scp=85057313094&partnerID=8YFLogxK

M3 - Chapter in a published conference proceeding

AN - SCOPUS:85057313094

BT - Proceedings of 12th European Conference on Antennas and Propagation (EuCAP), 2018

PB - IET

T2 - 12th European Conference on Antennas and Propagation, EuCAP 2018

Y2 - 9 April 2018 through 13 April 2018

ER -

Accelerating a ray launching model using GPU with CUDA

Abstract

Conference

Keywords

ASJC Scopus subject areas

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