High rayleigh number mantle convection on GPU

David A. Sanchez; Christopher Gonzalez; David A. Yuen; Grady B. Wright; Gregory A. Barnett

doi:10.1007/978-3-642-16405-7_22

High rayleigh number mantle convection on GPU

David A. Sanchez
, Christopher Gonzalez
, David A. Yuen
, Grady B. Wright
, Gregory A. Barnett

Mathematics Department

Research output: Chapter in Book/Report/Conference proceeding › Chapter › peer-review

Abstract

We implemented two- and three-dimensional Rayleigh–Benard convection on Nvidia GPUs by utilizing a 2nd-order finite difference method. By exploiting the massive parallelism of GPU using both CUDA for C and optimized CUBLAS routines, we have on a single Fermi GPU run simulations of Rayleigh number up to 6 × 10¹⁰ (on a mesh of 2000 × 4000 uniform grid points) in two dimensions and up to 10⁷ (on a mesh of 450 × 450 × 225 uniform grid points) for three dimensions. On Nvidia Tesla C2070 GPUs, these implementations enjoy single-precision performance of 535 GFLOP/s and 100 GFLOP/s respectively, and double-precision performance of 230 GFLOP/s and 70 GFLOP/s respectively.

Original language	American English
Title of host publication	Lecture Notes in Earth System Sciences
Pages	335-352
Number of pages	18
Edition	9783642164040
DOIs	https://doi.org/10.1007/978-3-642-16405-7_22
State	Published - 2013

Publication series

Name	Lecture Notes in Earth System Sciences
Number	9783642164040
Volume	0
ISSN (Print)	2193-8571
ISSN (Electronic)	2193-858X

Keywords

Rayleigh number
primitive mantle
mantle convection
high Rayleigh number
Benard convection

EGS Disciplines

Mathematics

Access to Document

10.1007/978-3-642-16405-7_22

Cite this

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title = "High rayleigh number mantle convection on GPU",

abstract = "We implemented two- and three-dimensional Rayleigh–Benard convection on Nvidia GPUs by utilizing a 2nd-order finite difference method. By exploiting the massive parallelism of GPU using both CUDA for C and optimized CUBLAS routines, we have on a single Fermi GPU run simulations of Rayleigh number up to 6 × 1010 (on a mesh of 2000 × 4000 uniform grid points) in two dimensions and up to 107 (on a mesh of 450 × 450 × 225 uniform grid points) for three dimensions. On Nvidia Tesla C2070 GPUs, these implementations enjoy single-precision performance of 535 GFLOP/s and 100 GFLOP/s respectively, and double-precision performance of 230 GFLOP/s and 70 GFLOP/s respectively.",

keywords = "Rayleigh number, primitive mantle, mantle convection, high Rayleigh number, Benard convection",

author = "Sanchez, \{David A.\} and Christopher Gonzalez and Yuen, \{David A.\} and Wright, \{Grady B.\} and Barnett, \{Gregory A.\}",

note = "Publisher Copyright: {\textcopyright} Springer-Verlag Berlin Heidelberg 2013.",

year = "2013",

doi = "10.1007/978-3-642-16405-7\_22",

language = "American English",

series = "Lecture Notes in Earth System Sciences",

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TY - CHAP

T1 - High rayleigh number mantle convection on GPU

AU - Sanchez, David A.

AU - Gonzalez, Christopher

AU - Yuen, David A.

AU - Wright, Grady B.

AU - Barnett, Gregory A.

PY - 2013

Y1 - 2013

N2 - We implemented two- and three-dimensional Rayleigh–Benard convection on Nvidia GPUs by utilizing a 2nd-order finite difference method. By exploiting the massive parallelism of GPU using both CUDA for C and optimized CUBLAS routines, we have on a single Fermi GPU run simulations of Rayleigh number up to 6 × 1010 (on a mesh of 2000 × 4000 uniform grid points) in two dimensions and up to 107 (on a mesh of 450 × 450 × 225 uniform grid points) for three dimensions. On Nvidia Tesla C2070 GPUs, these implementations enjoy single-precision performance of 535 GFLOP/s and 100 GFLOP/s respectively, and double-precision performance of 230 GFLOP/s and 70 GFLOP/s respectively.

AB - We implemented two- and three-dimensional Rayleigh–Benard convection on Nvidia GPUs by utilizing a 2nd-order finite difference method. By exploiting the massive parallelism of GPU using both CUDA for C and optimized CUBLAS routines, we have on a single Fermi GPU run simulations of Rayleigh number up to 6 × 1010 (on a mesh of 2000 × 4000 uniform grid points) in two dimensions and up to 107 (on a mesh of 450 × 450 × 225 uniform grid points) for three dimensions. On Nvidia Tesla C2070 GPUs, these implementations enjoy single-precision performance of 535 GFLOP/s and 100 GFLOP/s respectively, and double-precision performance of 230 GFLOP/s and 70 GFLOP/s respectively.

KW - Rayleigh number

KW - primitive mantle

KW - mantle convection

KW - high Rayleigh number

KW - Benard convection

UR - https://www.scopus.com/pages/publications/85038082044

U2 - 10.1007/978-3-642-16405-7_22

DO - 10.1007/978-3-642-16405-7_22

M3 - Chapter

T3 - Lecture Notes in Earth System Sciences

SP - 335

EP - 352

BT - Lecture Notes in Earth System Sciences

ER -

High rayleigh number mantle convection on GPU

Abstract

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Keywords

EGS Disciplines

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