Parallelizing TUNAMI-N1 Using GPGPU
Parallelizing TUNAMI-N1 Using GPGPU
We present a high performance tsunami-prediction system using General Purpose Graphics Processing Units (GPGPU). It is based on TUNAMI-N1, a Numerical Analysis Model for Investigation of near-field tsunamis. It uses linear shallow water wave equations, commonly accepted approximation for tsunami propagation, taking the input from a bathymetry file containing a large data set. Due to the largeness of the data set, the model is more amenable to parallelization. The system maps the TUNAMI-N1 model into the massively parallel GPU architecture using Nvidia CUDA framework. It employs multiple kernels that contain inherently parallel portion of the model and uses the concepts of data and hybrid parallelism to fully exploit the hardware capabilities of the GPUs. Experimental results show that our system achieves a speed up of six times
GPGPU, nvidia CUDA framework, TUNAMI-N1 numerical analysis model, bathymetry file, data parallelism, general purpose graphics processing unit, hybrid parallelism, linear shallow water wave equation, massively parallel GPU architecture, parallelization, tsunami propagation approximation, tsunami-prediction system, approximation theory, computer graphic equipment, coprocessors, geophysics computing, parallel processing, shallow water equations, tsunami
978-1-4577-1564-8
845-850
Gidra, H.
ff2fb044-60cf-477b-a3cd-29c99091065c
Haque, I.
9d51f711-1a0b-4a89-aaf4-310dd5db9eef
Kumar, N.P.
180d6331-9918-48f2-a95d-e3ad659642e3
Sargurunathan, M.
5207e45f-ebe5-4fea-ab89-65e890258eaf
Gaur, M.S.
d4a63afd-c0a0-479e-9a17-e93d99630bd3
Laxmi, V.
8ba515fc-a1f3-4685-8fac-3519ae9de905
Zwolinski, Mark
adfcb8e7-877f-4bd7-9b55-7553b6cb3ea0
Singh, V.
2e76a61b-fde2-4a5f-9557-ca1c52d01abd
September 2011
Gidra, H.
ff2fb044-60cf-477b-a3cd-29c99091065c
Haque, I.
9d51f711-1a0b-4a89-aaf4-310dd5db9eef
Kumar, N.P.
180d6331-9918-48f2-a95d-e3ad659642e3
Sargurunathan, M.
5207e45f-ebe5-4fea-ab89-65e890258eaf
Gaur, M.S.
d4a63afd-c0a0-479e-9a17-e93d99630bd3
Laxmi, V.
8ba515fc-a1f3-4685-8fac-3519ae9de905
Zwolinski, Mark
adfcb8e7-877f-4bd7-9b55-7553b6cb3ea0
Singh, V.
2e76a61b-fde2-4a5f-9557-ca1c52d01abd
Gidra, H., Haque, I., Kumar, N.P., Sargurunathan, M., Gaur, M.S., Laxmi, V., Zwolinski, Mark and Singh, V.
(2011)
Parallelizing TUNAMI-N1 Using GPGPU.
In Proceedings of the 2011 IEEE International Conference on High Performance Computing and Communications.
IEEE.
.
(doi:10.1109/HPCC.2011.120).
Record type:
Conference or Workshop Item
(Paper)
Abstract
We present a high performance tsunami-prediction system using General Purpose Graphics Processing Units (GPGPU). It is based on TUNAMI-N1, a Numerical Analysis Model for Investigation of near-field tsunamis. It uses linear shallow water wave equations, commonly accepted approximation for tsunami propagation, taking the input from a bathymetry file containing a large data set. Due to the largeness of the data set, the model is more amenable to parallelization. The system maps the TUNAMI-N1 model into the massively parallel GPU architecture using Nvidia CUDA framework. It employs multiple kernels that contain inherently parallel portion of the model and uses the concepts of data and hybrid parallelism to fully exploit the hardware capabilities of the GPUs. Experimental results show that our system achieves a speed up of six times
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More information
Published date: September 2011
Venue - Dates:
IEEE 13th International Conference on High Performance Computing and Communications, Banff, Canada, 2011-09-02 - 2011-09-04
Keywords:
GPGPU, nvidia CUDA framework, TUNAMI-N1 numerical analysis model, bathymetry file, data parallelism, general purpose graphics processing unit, hybrid parallelism, linear shallow water wave equation, massively parallel GPU architecture, parallelization, tsunami propagation approximation, tsunami-prediction system, approximation theory, computer graphic equipment, coprocessors, geophysics computing, parallel processing, shallow water equations, tsunami
Organisations:
EEE
Identifiers
Local EPrints ID: 339231
URI: http://eprints.soton.ac.uk/id/eprint/339231
ISBN: 978-1-4577-1564-8
PURE UUID: d5af8545-472a-4a08-b9ab-57e9ac1d4f86
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Date deposited: 25 May 2012 14:31
Last modified: 15 Mar 2024 02:39
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Contributors
Author:
H. Gidra
Author:
I. Haque
Author:
N.P. Kumar
Author:
M. Sargurunathan
Author:
M.S. Gaur
Author:
V. Laxmi
Author:
Mark Zwolinski
Author:
V. Singh
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