Parallelizing Locally-Weighted Regression

This paper focuses on a nonparametric regression technique known as locally-weighted regression or LOESS, LOESS is a computationally intensive technique which makes it naturally amenable to exploiting high performance computers. In this paper, we explore domain decomposition techniques for LOESS and study the performance of our algorithm on an Intel Paragon XP/S A4 machine. We study both speedup and efficiency as a function of the number of nodes. Certain segments of the LOESS computation are shown to be fruitfully parallelized while others are essentially sequential and cannot be parallelized effectively.