Abstract
There exist various different high- and low-level approaches for GPU programming. These include the newer directive based OpenACC programming model, Nvidia’s programming platform CUDA and existing libraries like cuSPARSE with a fixed functionality. This work compares the attained performance and development effort of different approaches based on the example of implementing the SpMV operation, which is an important and performance critical building block in many application fields. We show that the main differences in development effort using CUDA and OpenACC are related to the memory management and the thread mapping.
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Notes
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version 6.5 for Tesla K20m and M2050, version 7.0 for Tesla K80.
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Acknowledgements
We would like to thank the CMT team at Saudi Aramco EXPEC ARC for their support and input. Especially we want to thank Ali H. Dogru for making this research project possible.
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Ecker, J.P., Berrendorf, R., Razzaq, J., Scholl, S.E., Mannuss, F. (2016). Comparing Different Programming Approaches for SpMV-Operations on GPUs. In: Wyrzykowski, R., Deelman, E., Dongarra, J., Karczewski, K., Kitowski, J., Wiatr, K. (eds) Parallel Processing and Applied Mathematics. PPAM 2015. Lecture Notes in Computer Science(), vol 9573. Springer, Cham. https://doi.org/10.1007/978-3-319-32149-3_50
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