Evaluating the Performance Acceleration of Generalized Linear Solver using Normal Equation on Three Architectures for Tall Skinny Datasets

In previous work, we effectively applied a Normal Equation method to solve the most draining task in the Generalized Linear Model training process on a tall-skinny real-world dataset. This paper generalizes this method by applying it to synthetic data in various sizes. Besides, we evaluated the method on a wider column of data to evaluate the scalability. In addition, we measured and made a comparison of the execution on three different architectures: Vector Machine, an up-to-date GPGPU, and x86 CPU, along with various compilers and BLAS implementations.