Accelerated Parallel Training of Logistic Regression using OpenCL

This paper presents an accelerated approach for training logistic regression in parallel and running on Graphics Processing Units (GPU). Many prediction applications employed logistic regression for building an accomplished prediction model. This process requires a long time of training and building an accurate prediction model. Many scientists have worked out in boosting performance of logistic regression using different techniques. Our study focuses on showing the tremendous capabilities of GPU processing and OpenCL framework. GPU and OpenCL are the low cost and high performance solution for scaling up logistic regression to handle large datasets. We implemented the proposed approach in OpenCL C/C++ and tested on different data sets. All results showed a significant improvement in execution time in large datasets, which is reduced almost with the available GPU devices.