A Case Study of OpenCL on an Android Mobile GPU

James A. Ross, David A. Richie, Song J. Park, Dale R. Shires, Lori L. Pollock
Engility Corporation, Chantilly, VA
2014 IEEE High Performance Extreme Computing Conference (HPEC ’14), 2014


   title={A Case Study of OpenCL on an Android Mobile GPU},

   author={Ross, James A. and Richie, David A. and Park, Song J. and Shires, Dale R. and Pollock, Lori L.},



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An observation in supercomputing in the past decade illustrates the transition of pervasive commodity products being integrated with the world’s fastest system. Given today’s exploding popularity of mobile devices, we investigate the possibilities for high performance mobile computing. Because parallel processing on mobile devices will be the key element in developing a mobile and computationally powerful system, this study was designed to assess the computational capability of a GPU on a low-power, ARM-based mobile device. The methodology for executing computationally intensive benchmarks on a handheld mobile GPU is presented, including the practical aspects of working with the existing Android-based software stack and leveraging the OpenCL-based parallel programming model. The empirical results provide the performance of an OpenCL N-body benchmark and an auto-tuning kernel parameterization strategy. The achieved computational performance of the lowpower mobile Adreno GPU is compared with a quad-core ARM, an x86 Intel processor, and a discrete AMD GPU.
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