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

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.