Parallel algorithms for predictive modeling
Abstract
Parallel computing enables the analysis of very large data sets using large collections of flexible models with many variables. The computational methods are based on ideas from computational linear algebra and can draw on extensive research in parallel algorithms. Many algorithms for the direct and iterative solution of penalized least squares problems and for updating can be applied. Both methods for dense and sparse problems are applicable. An important property of the algorithms is their scalability, i.e., their ability to solve larger problems in the same time using hardware which grows linearly with the problem size. Whereas in most cases large granularity parallelism is to be preferred, it turns out that smaller granularity parallelism can also be exploited effectively in the problems considered.