Big data in multi-block data analysis: An approach to parallelizing Partial Least Squares Mode B algorithm

• Main Authors: Alba Martinez-Ruiz, Cristina Montañola-Sales
• Format: Article
• Language: English
• Published: Elsevier 2019-04-01
• Series: Heliyon
• Subjects: Computer science; Computational mathematics
• Online Access: http://www.sciencedirect.com/science/article/pii/S2405844018367616

Partial Least Squares (PLS) Mode B is a multi-block method and a tightly coupled algorithm for estimating structural equation models (SEMs). Describing key aspects of parallel computing, we approach the parallelization of the PLS Mode B algorithm to operate on large distributed data. We show the scalability and performance of the algorithm at a very fine-grained level thanks to the versatility of pbdR, a R-project library for parallel computing. We vary several factors under different data distribution schemes in a supercomputing environment. Shorter elapsed times are obtained for the square-blocking factor 16×16 using a grid of processors as square as possible and non-square blocking factors 1000×4 and 10000×4 using an one-column grid of processors. Depending on the configuration, distributing data in a larger number of cores allows reaching speedups of up to 121 over the CPU implementation. Moreover, we show that SEMs can be estimated with big data sets using current state-of-the-art algorithms for multi-block data analysis.