Ginkgo: A high performance numerical linear algebra library

Ginkgo is a production-ready sparse linear algebra library for high performance computing on GPU-centric architectures with a high level of performance portability and focuses on software sustainability.

The library focuses on solving sparse linear systems and accommodates a large variety of matrix formats, state-of-the-art iterative (Krylov) solvers and preconditioners, which make the library suitable for a variety of scientific applications. Ginkgo supports many architectures such as multi-threaded CPU, NVIDIA GPUs, AMD GPUs and Intel GPUs and includes multi-node support with MPI. The heavy use of modern C++ features simplifies the addition of new executor paradigms and algorithmic functionality without introducing significant performance overhead.

The Ginkgo team is proud to announce the new Ginkgo minor release 1.9.0.
This release brings new features such as:

• Support for half precision (IEEE FP16). The type gko::half can now be selected in most instances as the value type
  of a matrix, solver, preconditioner, etc. If the selected backend supports FP16 as a native type, the native type is
  used within the kernels, otherwise an overhead might occur. The new behavior is enabled by default, but it can be
  turned off during configuration.
• New implementations of the ILU and IC factorization for CUDA, HIP, OpenMP, and Reference backends. These are
  available in addition to the existing implementations based on the vendor libraries cuSPARSE and hipSPARSE.
• New (S)SOR and Gauss-Seidel preconditioners.
• Simplified distributed matrix assembly by exchanging local rows between neighboring processes.

And more!

Please refer the release notes for more details (https://github.com/ginkgo-project/ginkgo/releases/tag/v1.9.0).