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A Step towards Energy Efficient Computing: Redesigning A Hydrodynamic Application on CPU-GPU

Tingxing Dong, Veselin Dobrev, Tzanio Kolev, Robert Rieben, Stanimire Tomov, Jack Dongarra
Innovative Computing Laboratory, University of Tennessee, Knoxville
28th IEEE International Parallel & Distributed Processing Symposium (IPDPS), 2014

@article{icl811,

   author={Tingxing Dong, Veselin Dobrev, Tzanio Kolev, Robert Rieben, Stanimire Tomov, Jack Dongarra},

   title={A Step towards Energy Efficient Computing: Redesigning A Hydrodynamic Application on CPU-GPU},

   institution={Innovative Computing Laboratory, University of Tennessee},

   journal={IPDPS 2014},

   month={may},

   year={2014}

}

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Power and energy consumption are becoming an increasing concern in high performance computing. Compared to multi-core CPUs, GPUs have a much better performance per watt. In this paper we discuss efforts to redesign the most computation intensive parts of BLAST, an application that solves the equations for compressible hydrodynamics with high order finite elements, using GPUs [10, 1]. In order to exploit the hardware parallelism of GPUs and achieve high performance, we implemented custom linear algebra kernels. We intensively optimized our CUDA kernels by exploiting the memory hierarchy, which exceed the vendor’s library routines substantially in performance. We proposed an autotuning technique to adapt our CUDA kernels to the orders of the finite element method. Compared to a previous base implementation, our redesign and optimization lowered the energy consumption of the GPU in two aspects: 60% less time to solution and 10% less power required. Compared to the CPU-only solution, our GPU accelerated BLAST obtained a 2:5x overall speedup and 1:42x energy efficiency (greenup) using 4th order (Q4) finite elements, and a 1:9x speedup and 1:27x greenup using 2nd order (Q2) finite elements.
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