15435

Utilizing GPUs to Accelerate Turbomachinery CFD Codes

Weylin MacCalla, Sameer Kulkarni
Embry-Riddle Aeronautical University, Daytona Beach, Florida
NASA Technical Report, NASA/TM-2016-218947, 2016

@article{maccalla2016utilizing,

   title={Utilizing GPUs to Accelerate Turbomachinery CFD Codes},

   author={MacCalla, Weylin and Kulkarni, Sameer},

   year={2016}

}

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GPU computing has established itself as a way to accelerate parallel codes in the high performance computing world. This work focuses on speeding up APNASA, a legacy CFD code used at NASA Glenn Research Center, while also drawing conclusions about the nature of GPU computing and the requirements to make GPGPU worthwhile on legacy codes. Rewriting and restructuring of the source code was avoided to limit the introduction of new bugs. The code was profiled and investigated for parallelization potential, then OpenACC directives were used to indicate parallel parts of the code. The use of OpenACC directives was not able to reduce the runtime of APNASA on either the NVIDIA Tesla discrete graphics card, or the AMD accelerated processing unit. Additionally, it was found that in order to justify the use of GPGPU, the amount of parallel work being done within a kernel would have to greatly exceed the work being done by any one portion of the APNASA code. It was determined that in order for an application like APNASA to be accelerated on the GPU, it should not be modular in nature, and the parallel portions of the code must contain a large portion of the code’s computation time.
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