Abstract
Due to the parallel and computationally intensive nature of Artificial Neural Networks, we use GPUs to implement a generic Multilayer Perceptron (MLP) framework and compare the speed to an implementation on the CPU. The speedup achieved increases as the size of the network increases, but is also contingent on the hardware used. Three GPUs are tested, the Tesla K80, the Tesla T4, and the Tesla P100. For the largest ANNs tested, speedups ranged from 331.14\(\times \) for the K80 up to 2379.2\(\times \) on the P100.
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Udby, T., Tian, Y. (2023). A Generic Neural Network Implementation on GPU and Its Performance Benchmark. In: Arai, K. (eds) Proceedings of the Future Technologies Conference (FTC) 2022, Volume 3. FTC 2022 2022. Lecture Notes in Networks and Systems, vol 561. Springer, Cham. https://doi.org/10.1007/978-3-031-18344-7_9
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