Parallel implementation of neural networks with the use of GPGPU technology OpenCL

• Autorzy: Kłyś M. , Szymczyk M. , Szymczyk P. , Gajer M.
• Języki publikacji: EN

Abstrakty

EN

The article discusses possibilities of implementing a neural network in a parallel way. The issues of implementation are illustrated with the example of the non-linear neural network. Parallel implementation of earlier mentioned neural network is written with the use of OpenCL library, which is a representative of software supporting general-purpose computing on graphics processor units (GPGPU). The obtained results demonstrate that some group of algorithms can be computed faster if they are implemented in a parallel way and run on a multi-core processor (CPU) or a graphics processing unit (GPU). In case of the GPU, the implemented algorithm should be divided into many threads in order to perform computations faster than on a multi-core CPU. In general, computations on a GPU should be performed when there is a need to process a large amount of data with the use of algorithm which is very well suited to parallel implementation.

Słowa kluczowe

EN

OpenCL; Artificial Neural Networks; GPGPU

• Wydawca: Wydawnictwo PAK
• Czasopismo: Measurement Automation Monitoring
• Rocznik: 2015
• Tom: Vol. 61, No. 1
• Strony: 16--20
• Opis fizyczny: Bibliogr. 8 poz., rys., tab., wykr.

Twórcy

autor

Kłyś M.

• AGH University of Science and Technology

autor

Szymczyk M.

• AGH University of Science and Technology

autor

Szymczyk P.

• AGH University of Science and Technology

autor

Gajer M.

• AGH University of Science and Technology

Bibliografia

• [1] AMD OpenCL programming guide, AMD Accelerated Parallel Processing, http://developer.amd.com/download/AMD_Accele rated_ Parallel_Processing_OpenCL_Programming_Guide.pdf
• [2] Khronos, OpenCL documentation, http://www.khronos.org/registry/ cl/sdk/1.2/docs/man/xhtml/
• [3] Kłyś M.: Master thesis „Rozpoznawanie kształtów z wykorzystaniem sieci neuronowych i technologii GPGPU OpenCL“, AGH, 2013.
• [4] NVIDIA, OpenCL programming guide for the CUDA architecture, http://www.nvidia.com/content/cudazone/download/OpenCL/NVIDIA_OpenCL\linebreak_ProgrammingGuide.pdf
• [5] Scarpino M.: How to accelerate graphics and computation, 2011.
• [6] Tadeusiewicz R.: Sieci neuronowe, Akademicka Oficyna Wydawnicza, 1993.
• [7] Tadeusiewicz R., Flasiński M.: Rozpoznawanie obrazów, PWN, 1991.
• [8] Tadeusiewicz R., Korohoda P.: Komputerowa analiza i przetwarzanie obrazów. Wyd. Fundacji Postępu Telekomunikacji, 1997.