Accelerating the RTTOV-7 IASI and AMSU-A radiative transfer models on graphics processing units: evaluating central processing unit/graphics processing unit-hybrid and pure-graphics processing unit approaches

Jarno Mielikainen; Bormin Huang; Hung-Lung Allen Huang; Roger W. Saunders

doi:10.1117/1.3658028

1 January 2011 Accelerating the RTTOV-7 IASI and AMSU-A radiative transfer models on graphics processing units: evaluating central processing unit/graphics processing unit-hybrid and pure-graphics processing unit approaches

Jarno Mielikainen, Bormin Huang, Hung-Lung Allen Huang, Roger W. Saunders

Author Affiliations +

Journal of Applied Remote Sensing, Vol. 5, Issue 1, 051503 (January 2011). https://doi.org/10.1117/1.3658028

Abstract

The radiative transfer for television operational vertical sounder (RTTOV) is a widely-used radiative transfer model (RTM) for calculation of radiances for satellite infrared and microwave sensors, including the 8461-channel infrared atmospheric sounding interferometer (IASI) and the 15-band Advanced Microwave Sounding Unit-A (AMSU-A). In the era of hyperspectral sounders with thousands of spectral channels, the computation of the RTM becomes more time-consuming. The RTM performance in operational numerical weather prediction systems still limits the number of used channels in hyperspectral sounders to only a few hundred. To take full advantage of such high-resolution infrared observations, a computationally efficient radiative transfer model is needed to facilitate satellite data assimilation. In this paper, we develop the parallel implementation of the RTTOV-7 IASI and AMSU-A RTMs to run the predictor module on CPUs in pipeline with the transmittance and radiance modules on NVIDIA many-core graphics processing units (GPUs). We show that concurrent execution of RTTOV-7 IASI RTM on CPU and GPU, in addition to asynchronous data transfer from CPU to GPU, allows the GPU accelerated code running on the 240-core NVIDIA Tesla C1060 to reach a speedup of 461× and 1793× for 1- and 4-GPU configurations, respectively. To compute one day's amount of 1,296,000 IASI spectra, the CPU code running on the host AMD Phenom II X4 940 CPU core with 3.0 GHz will take 2.8 days. Thus, GPU acceleration reduced running time to 8.75 and 2.25 min on 1- and 4-GPU configurations, respectively. Speedup for the RTTOV AMSU-A RTM varied from 29× to 75× for 1 and 4 GPUs, respectively.To further boost the speedup of a multispectral RTM, we developed a novel pure-GPU version of the RTTOV AMSU-A RTM where the predictor module also runs on GPUs to achieve a 96% reduction in the host-to-device data transfer. The speedups for the pure-GPU AMSU-A RTM are significantly increased to 56× and 125× for 1- and 4-GPU configurations, respectively.

©(2011) Society of Photo-Optical Instrumentation Engineers (SPIE)

Citation Download Citation

Jarno Mielikainen, Bormin Huang, Hung-Lung Allen Huang, and Roger W. Saunders "Accelerating the RTTOV-7 IASI and AMSU-A radiative transfer models on graphics processing units: evaluating central processing unit/graphics processing unit-hybrid and pure-graphics processing unit approaches," Journal of Applied Remote Sensing 5(1), 051503 (1 January 2011). https://doi.org/10.1117/1.3658028

Published: 1 January 2011

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

;

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE