Many large-scale scientific applications rely heavily on preconditioned iterative solvers for large linear systems. For these solvers to efficiently exploit extreme-scale hardware, both the solver algorithms and the implementations must be redesigned to address challenges like extreme concurrency, complex memory hierarchies, costly data movement, heterogeneous node architectures, and the increasing adoption of low-precision processor technology.
The PEEKS effort aims to tackle these challenges and advance the capabilities of the ECP software stack by making the new scalable algorithms accessible within the Trilinos and Ginkgo software ecosystems. Targeting exascale-enabled Krylov solvers, incomplete factorization routines, and parallel preconditioning techniques will ensure successful delivery of scalable Krylov solvers in robust, production-quality software that can be relied on by ECP applications.
Find out more at http://icl.utk.edu/peeks/
In Collaboration With
- Sandia National Laboratories