ON THE PROBLEM OF PREDICTING CALCULATION TIME NEEDED FOR NEURAL NETWORK EXECUTED BY MEANS OF GPU IN CASE OF CONVOLUTION NEURAL NETWORKS
( Pp. 47-51)

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Buryak Dmitry Yurievich kand. fiz.-mat. nauk, starshiy inzhener-issledovatel
Branch of LG Electronics Popova Nina Nikolaevna kandidat fiziko-matematicheskih nauk; docent fakulteta vychislitelnoy matematiki i kibernetiki
Lomonosov Moscow State University
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Abstract:
Computation performance of GPU devices has grown significantly in recent time. After CUDA architecture has appeared researchers could make active use of GPU devices in their work including nanotechnology area. However in many cases it is difficult to predict acceleration factor for an algorithm after its implementation by using GPU and consequently to estimate computational efficiency of this algorithm. Thus the task of computational performance prediction of an algorithm implemented using GPU is crucial. This work describes computational performance prediction model for algorithms based on artificial neural networks. Neural network depends on large amount of hyperparameters, which are defined on the architecture design stage, and affect its execution speed and results accuracy. A process of selecting these parameters values could take a long time. Application of prediction approaches allows to reduce time needed for the selection stage and to increase precision of hyperparameters' estimations.
How to Cite:
Buryak D.Y., Popova N.N., (2017), ON THE PROBLEM OF PREDICTING CALCULATION TIME NEEDED FOR NEURAL NETWORK EXECUTED BY MEANS OF GPU IN CASE OF CONVOLUTION NEURAL NETWORKS. Computational Nanotechnology, 2 => 47-51.
Reference list:
Amani A., Mohammadyani D. Artificial Neural Networks: Applications in Nanotechnology. Chapter in book: Artificial Neural Networks - Application. book edited by Chi Leung Patrick Hui, ISBN 978-953-307-188-6, Published: April 11, 2011.
Baghsorkhi S., Delahaye M., Gropp W., Wen-mei H.,W. Analytical performance prediction for evaluation and tuning of GPGPU applications. Workshop on Exploiting Parallelism using GPUs and other Hardware-Assisted Methods (EPHAM 09), In conjunction with The International Symposium on Code Generation and Optimization (CGO), 2009.
Fortune S., Wyllie J. Parallelism in Random Access Machines. In Proc. ACM STOC, 1978.
Gibbons P. B., Matias Y., Ramachandran V. The queue-read queue-write asynchronous pram model. In In Proc. of EURO-PAR, 1996.
G mez-Luna J., Gonz lez-Linares J.M., Benavides J.I., Guil N. Performance models for CUDA streams on NVIDIA GeForce series. Technical Report, University of M laga, 2011.
Guo P., Wang L. Accurate CUDA performance modeling for sparse matrix-vector multiplication. International Conference on High Performance Computing and Simulation (HPCS), 2012.
Hasan K.S., Chatterjee A., Radhakrishnan S., Antonio J.K. Performance Prediction Model and Analysis for Compute-Intensive Tasks on GPUs. In: Hsu CH., Shi X., Salapura V. (eds) Network and Parallel Computing. NPC 2014.
Hopf M., Ertl T. Hardware Accelerated Wavelet Transformations. In Proc. EG Symposium on Visualization, 2000.
Kothapalli K., Mukherjee R., Rehman M. S., Patidar, S., Narayanan, P.J., Srinathan K. A performance prediction model for the CUDA GPGPU platform. 2009 International Conference on High Performance Computing (HiPC), 2009.
LeCun Y., Boser B., Denker J.S., Henderson D., Howard R.E., Hubbard W., Jackel L.D. Handwritten digit recognition with a back-propagation network, Advanced in Neural Information Processing, 1990a, vol.2, 1990.
Luo Y., Duraiswami R. Canny Edge Detection on Nvidia CUDA. In Proc. of IEEE Computer Vision and Pattern Recognition, 2008.
Nguyen H. GPU Gems 3. Addison-Wesley Professional, 2007.
Sacha G. M., Varona P. Artificial intelligence in nanotechnology. Nanotechnology, 24 452002, 2013.
Valiant L. G. A Bridging Model for Parallel Computation. Comm. ACM 33, 8, 1990.
Vineet V., Narayanan P.J. CUDA cuts: Fast Graph Cuts on the GPU. In Proceedings of the CVPR Workshop on Visual Computer Vision on GPUs, 2008.
Keywords:
Artificial neural network, convolutional neural networks, parallel computing, motherboard GPU, prediction of execution time.


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