Date of Award


Degree Type


Degree Name

Master of Science (MS)


Computer and Information Technology

Committee Chair

Baijian Yang

Committee Member 1

Tonglin Zhang

Committee Member 2

Byung-Cheol Min


This study investigates utilizing the characteristics of Intel Xeon to improve the performance of training generalized linear models. The classic approach to fnd the maximum likelihood estimation of linear model requires loading entire data into memory for computation which is infeasible when data size is bigger than memory size. With the approach analyzed by Zhang and Yang (2017), the process of model fitting will be achieved iteratively through iterating each row. However, one limitation of this approach could be the iterative manner will impact performance when implementing it on Intel Xeon processor which delivers parallelism and vectorization. The study will focus on the tuning of application process and configuration on Xeon family processor based on the architecture of GLM model fitting algorithm.