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Numba-accelerated parameter estimation for artificial pancreas applications

文献类型： 会议论文

第一作者： Máté Siket

作者： Máté Siket 1 ; Lehel Dénes-Fazakas 2 ; Levente Kovács 3 ; Gy?rgy Eigner 4 ;

作者机构： 1.Physiological Controls Research Center, óbuda University, Hungary|Institute for Computer Science and Control, Budapest, Hungary|Applied Informatics and Applied Mathematics Doctoral School, óbuda University, Hungary

2.Physiological Controls Research Center, óbuda University, Hungary|John von Neumann Faculty of Informatics, Biomatics and Applied Artificial Intelligence Institute, óbuda University, Hungary|Applied Informatics and Applied Mathematics Doctoral School, óbuda University, Hungary

3.Physiological Controls Research Center, óbuda University, Hungary

4.John von Neumann Faculty of Informatics, Biomatics and Applied Artificial Intelligence Institute, óbuda University, Hungary

关键词： Cloud computing;Runtime;Parameter estimation;Scientific computing;Graphics processing units;Syntactics;Central Processing Unit

会议名称： IEEE Jubilee International Symposium on Intelligent Systems and Informatics

主办单位：

页码： 279-284

摘要： General-purpose computing on graphics processing units (GPU) has gained more attention over the last years in scientific computing. Tasks like population-based optimizations are widely used and can be efficiently parallelized. While Python is considered generally “slow”, it is one of the most popular programming languages. In this study, we evaluate the acceleration possibilities of a parameter estimation problem based on optimization utilizing the Numba library on the central processing unit (CPU) and on the GPU. Runtimes indicate that Numba achieves high runtime improvements with little modifications in the syntax. On the other hand, utilization of the GPU is beneficial when high parallelization can be guaranteed and using cloud computing platforms.

分类号： tp391

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