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P Systems Implementation on GPUs

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Membrane Computing Models: Implementations

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Abstract

The performance of P system simulators is key when deploying these models in real applications, or when pushing the limits of efficiency. In this concern, HPC is the key technology to accelerate the simulation of P systems, and specially, GPU computing is a good alternative since they provide a highly parallel device with a shared memory system and a flexible programming. GPUs are currently the enabling technology for trending areas such as Deep Learning. So far, many GPU simulators have been developed using CUDA, the first general-purpose programming model for GPUs. In this chapter, we introduce the concepts behind GPU computing and a taxonomy of GPU-based simulators: generic, specific, and adaptive simulation.

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Notes

  1. 1.

    Note that we use here “K” and “M” for binary prefixes “kilo” and “mega”, respectively. Therefore, 128 K = 217 = 131072.

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Authors and Affiliations

  1. School of Control Engineering, Chengdu University of Information Technology, Chengdu, China

    Gexiang Zhang

  2. Department of Computer Science and Artificial Intelligence, University of Seville, Sevilla, Spain

    Mario J. Pérez-Jiménez & Agustín Riscos-Núñez

  3. Département Informatique, LACL, Université Paris Est Créteil, Creteil cedex, France

    Sergey Verlan

  4. Department of Computer Science, University of Bradford, Bradford, West Yorkshire, UK

    Savas Konur & Marian Gheorghe

  5. Department of Bioinformatics, Friedrich Schiller University Jena, Jena, Thüringen, Germany

    Thomas Hinze

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Zhang, G. et al. (2021). P Systems Implementation on GPUs. In: Membrane Computing Models: Implementations. Springer, Singapore. https://doi.org/10.1007/978-981-16-1566-5_6

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