Parallel Hybrid-Heterogeneous Single Value Decomposition Factorization

SVD factorization is a fundamental operation to solve problems in chemistry, biology, physics, and engineering. These problems are image processing, data mining, and big data, among others. There are several methods to get SVD factorization. One of these methods involve the use of Householder transformation, so it is possible to parallelize this task. Furthermore, novel computer architectures are oriented to use heterogeneous computing, such as CPUs and GPUs, in order to increase the performance and reduce the energy consumption. In this work, an heterogeneous parallel implementation of SVD based on Householder transformation is presented. Some strategies for matrix partition are presented in order to scale the program in the use of GPU cards. The speedup is increased when several GPU cards are used.