Implementation of Massive Artificial Neural Networks with CUDA

Domen Verber
University of Maribor
Cutting Edge Research in New Technologies, ISBN: 978-953-51-0463-6, 2012


   title={Implementation of Massive Artificial Neural Networks with CUDA},

   author={Verber, D.},



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People have always been amazed with the inner-workings of the human brain. The brain is capable of solving variety of problems that are unsolvable by any computers. Is capable of detecting minute changes of light, sound or smell. It is capable of instantly recognizing a face, to accurately read the handwritten text, etc. The brain is the centre of what we call human intelligence and self-awareness. This is not limited only to the human brain. A bee, for example, has a brain that is only a fraction the size compared to the human brain. Nevertheless, the bee able of detecting nectar over long distances; it is capable to orient itself in space and find its way back to the beehive, and it is capable of transferring the information about nectar locations to other bees though a well-choreographed dance. The basic unit of the nervous system is the neuron. A group of neurons build a neuronal network. In general, a neural network is a parallel system, capable of resolving problems that linear-computing cannot. Neural nets are used for signal processing, pattern recognition, visual and speech processing, in medicine, in business, etc. The techniques of the neural networks are a part of a machine-learning paradigm. Using this, a system should find solutions for certain problems based only on empirical data, using unknown underlying probability distribution. In addition to this, a vast number of research has been done in the field of artificial neural networks, in order to better understand the human brain, itself. For example, in the Blue Brain Project, the goal is to reconstruct the brain piece by piece and build a virtual brain within supercomputer (BBP, 2011). This approach tries to emulate the human brain very accurately, and requires considerable computing power. Each simulated neuron requires the equivalent of a laptop computer. Several programming libraries and tools exists, which allow for building artificial neural networks of moderate sizes. In addition, several experiments have been where the neurons are emulated within hardware. This exposure presents a study how to use massive parallel programming on general PCs for artificial neural networks (ANN), which utilizes the processing power and highly parallel computer architectures of graphic processor units (GPU). GPUs on mass-market graphical cards may greatly outperform general processors for some type of applications, both in computation power and in memory bandwidth. The graphic processor consists of a large number of processing cores that may perform a large number of tasks, in parallel. The execution of artificial neural networks is an intrinsically parallel problem. Therefore, parallel computational architectures, such as GPUs, lead to a great improvement in speed. Until recently, the programmers of ANN could only harness this processing power with especially prepared graphical applications. What is new is that the newest GPU architectures allow for a more general approach to ANN programming, without taking into consideration the graphical aspects of GPUs. One general-purpose parallel computing architecture is CUDA (Compute Unified Device Architecture), as developed by the Nvidia GPU manufacturer. Different aspects of ANN implementation using CUDA are discussed later. A much greater performance of ANN can be achieved by better understanding the particularities and limitations of CUDA.
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