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Workload-Aware Optimization of Ray-Tracing on Heterogeneous Embedded GPGPUs

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As recent mobile embedded systems are required to operate many compute-intensive applications, it is important to optimize the applications considering the underlying hardware architecture. In this paper, we propose to optimize ray-tracing application tailored for a given mobile computing platform with multiple heterogeneous processing elements. We take an open-source C/C++ implementation of ray-tracing, adapt it to OpenCL, and perform two optimizations. First, we find out the optimal degree of parallelism, work group size. Then, an optimal workload distribution over two OpenCL devices is examined. We obtain the speedup of 10× over the open-source implementation by using a single GPU device on Exynos 5422. Further, we show that the performance gain can be improved up to 17× by balancing the workload between two GPGPUs in heterogeneous multi-processor system on chips, considering the different compute capabilities thereof.
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Keywords: Heterogeneous Embedded GPGPUs; Ray-Tracing; Workload Optimization

Document Type: Research Article

Affiliations: Department of Electrical and Computer Engineering, Ajou University, Suwon, Republic of Korea

Publication date: November 1, 2016

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