| dc.creator | Martinez Ruiz, Alba | |
| dc.creator | Montanola Sales, Cristina | |
| dc.date | 2020-08-26T23:19:56Z | |
| dc.date | 2020-08-26T23:19:56Z | |
| dc.date | 2019-04 | |
| dc.identifier | Heliyon, Volume 5, Issue 4, April 2019, | |
| dc.identifier | http://repositoriodigital.ucsc.cl/handle/25022009/2068 | |
| dc.description | Artículo de publicación WOS | |
| dc.description | 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. | |
| dc.language | en | |
| dc.publisher | Heliyon | |
| dc.source | https://doi.org/10.1016/j.heliyon.2019.e01451 | |
| dc.subject | Computer science | |
| dc.subject | Computational mathematics | |
| dc.title | Big data in multi-block data analysis: An approach to parallelizing Partial Least Squares Mode B algorithm | |
| dc.type | Article | |
