Least squares solvers for distributed-memory machines with GPU accelerators
Jakub Kurzak, Mark Gates, Ali Charara, Asim YarKhan, Jack J. Dongarra · 2019
This work presents an implementation of a linear least squares solver for distributed-memory machines with GPU accelerators, developed as part of the Software for Linear Algebra Targeting Exascale (SLATE) package. From the algorithmic standpoint, the work leverages recent advances in dense linear algebra, specifically the communication-avoiding QR factorization. From the implementation standpoint, the work represents a sharp departure from the traditional conventions established by legacy packages, such as LAPACK and ScaLAPACK. It is based on representing the matrix as a collection of individual tiles, and using batch operations for offloading work to accelerators. The article lays out the principles of the new approach, discusses the implementation details and presents the performance results.