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. This release brings new features such as: Several building blocks for GPU-resident sparse direct solvers like symbolic and numerical LU and Cholesky factorization, ..., A distributed Schwarz preconditioner, New FGMRES and GCR solvers, Distributed benchmarks for the SpMV operation, solvers, ... Support for non-default streams in the CUDA and HIP backends, Mixed precision support for the CSR SpMV, A new profiling logger which integrates with NVTX, ROCTX, TAU and VTune to provide internal Ginkgo knowledge to most HPC profilers! and more!This work was supported by the ``Impuls und Vernetzungsfond of the Helmholtz Association" under grant VH-NG-1241. This research was also supported by the Exascale Computing Project (17-SC-20-SC), a collaborative effort of the U.S. Department of Energy Office of Science and the National Nuclear Security Administration