The International Conference for High Performance Computing, Networking, Storage, and Analysis
The International Conference for High Performance Computing, Networking, Storage, and Analysis
Authors: Martin Kuhnel, Alex Loddoch, and Tao Sun (Chevron)
Abstract: We present a practical approach for the acceleration of an industrial and scientific application using graphics processing units (GPUs). Our original application is a computational stratigraphy codebase that couples fluid flow and sediment deposition submodels. The application uses domain decomposition and a halo exchange to split the workload among multiple workers in a distributed system. Our methodology abstracts and conserves the host data structures while re-writing computational elements in the GPU programming language CUDA. Utilizing high performance GPU machines in the Azure cloud, we show a minimum 90x speedup compared to a high-end CPU based cluster. In this poster, we give a brief description of the original algorithm, followed by a discussion of required software changes and additions. Although this case study focuses on a specific example, we hope this approach inspires similar efforts in other applications.
Best Poster Finalist (BP): no
Poster: PDF
Poster summary: PDF
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