ICL Research Profile

Overview

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 Production-ready, Exascale-Enabled Krylov Solvers (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 Ginkgo software ecosystem. The PEEKS algorithms focus on communication-minimizing Krylov solvers, parallel incomplete factorization routines, and parallel preconditioning techniques, as these building blocks form the numerical core of many complex application codes. Ginkgo provides native support for NVIDIA GPUs, AMD GPUs, and Intel GPUs to ensure successful delivery of scalable Krylov solvers in robust, production-quality software that can be relied on by ECP applications.