Abstract
Multilevel Toeplitz linear systems appear in a wide range of scientific and engineering applications. While several fast direct solvers exist for the basic 1-level Toeplitz matrices, in the multilevel case an iterative solver provides the most general and practical solution. This paper proposes several parallelization techniques that enable an efficient implementation of the conjugate gradient algorithm for solving multilevel Toeplitz systems on distributed-memory machines. The two major differences between this implementation and that for a general sparse linear solver are (1) a communication-efficient approach to handle data expansion and truncation and data transpose simultaneously; (2) the interleaving of matrix-vector multiplications and vector inner product calculations to reduce synchronization cost and latency. Similar ideas can be applied to the implementation of other iterative methods for Toeplitz systems that are not necessarily symmetric positive definite. Scaling results are shown to demonstrate the usefulness of the proposed techniques.
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.