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 researchers have worked out in boosting the performance of logistic regression using different techniques. Our study focuses on showing the ultimate capabilities of GPU processing and OpenCL framework. GPU and OpenCL are the low cost and high-performance solutions for scaling up logistic regression to handle large datasets. The proposed approach is implemented in OpenCL C/C++ and tested with different datasets. All results showed a significant improvement in execution time in large datasets, which is reduced almost with the available GPU devices.