high performance computing on graphics processing units: hgpu.org

Neural networks allow the implementation of complicated applications such as stock market predictions on low-end PCs. However, the training of neural networks can take many hours on a PC. In this paper we propose a technique for training complicated neural networks on a commodity GPU (available in a low-end PC) that completes 6 times faster than training on a multi core. Using the Proben1 benchmark for our analysis we use 15 datasets from 12 different domains to explore our solution. Our technique allows the training to be done with minimal CPU utilization time. This allows the user to carry out other tasks while the training is in progress. We compare several avenues of neural network training on a general purpose computer. The benchmark we use, covers problems of pattern classification from real life and hence is best suited for our tests as we aim to solve the problem of stock market predictions.