@article {,
	title = {Ginkgo: A High Performance Numerical Linear Algebra Library},
	journal = {Journal of Open Source Software},
	volume = {5},
	year = {2020},
	month = {2020-08},
	abstract = {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, and AMD GPUs. The heavy use of modern C++ features simplifies the addition of new executor paradigms and algorithmic functionality without introducing significant performance overhead.

Solving linear systems is usually one of the most computationally and memory intensive aspects of any application. Hence there has been a significant amount of effort in this direction with software libraries such as UMFPACK (Davis, 2004) and CHOLMOD (Chen, Davis, Hager, \& Rajamanickam, 2008) for solving linear systems with direct methods and PETSc (Balay et al., 2020), Trilinos ({\textquotedblleft}The Trilinos Project Website,{\textquotedblright} 2020), Eigen (Guennebaud, Jacob, \& others, 2010) and many more to solve linear systems with iterative methods. With Ginkgo, we aim to ensure high performance while not compromising portability. Hence, we provide very efficient low level kernels optimized for different architectures and separate these kernels from the algorithms thereby ensuring extensibility and ease of use.

Ginkgo is also a part of the xSDK effort (Bartlett et al., 2017) and available as a Spack (Gamblin et al., 2015) package. xSDK aims to provide infrastructure for and interoperability between a collection of related and complementary software elements to foster rapid and efficient development of scientific applications using High Performance Computing. Within this effort, we provide interoperability with application libraries such as deal.ii (Arndt et al., 2019) and mfem (Anderson et al., 2020). Ginkgo provides wrappers within these two libraries so that they can take advantage of the features of Ginkgo.},
	doi = {https://doi.org/10.21105/joss.02260},
	author = {Hartwig Anzt and Terry Cojean and Yen-Chen Chen and Fritz Goebel and Thomas Gruetzmacher and Pratik Nayak and Tobias Ribizel and Yu-Hsiang Tsai}
}
@article {,
	title = {Ginkgo: A Node-Level Sparse Linear Algebra Library for HPC (Poster)},
	year = {2020},
	month = {2020-02},
	publisher = {2020 Exascale Computing Project Annual Meeting},
	address = {Houston, TX},
	author = {Hartwig Anzt and Terry Cojean and Yen-Chen Chen and Fritz Goebel and Thomas Gruetzmacher and Pratik Nayak and Tobias Ribizel and Yu-Hsiang Tsai and Jack Dongarra}
}
@conference {,
	title = {Multiprecision Block-Jacobi for Iterative Triangular Solves},
	booktitle = {European Conference on Parallel Processing (Euro-Par 2020)},
	year = {2020},
	month = {2020-08},
	publisher = {Springer},
	organization = {Springer},
	abstract = {Recent research efforts have shown that Jacobi and block-Jacobi relaxation methods can be used as an effective and highly parallel approach for the solution of sparse triangular linear systems arising in the application of ILU-type preconditioners. Simultaneously, a few independent works have focused on designing efficient high performance adaptive-precision block-Jacobi preconditioning (block-diagonal scaling), in the context of the iterative solution of sparse linear systems, on manycore architectures. In this paper, we bridge the gap between relaxation methods based on regular splittings and preconditioners by demonstrating that iterative refinement can be leveraged to construct a relaxation method from the preconditioner. In addition, we exploit this insight to construct a highly-efficient sparse triangular system solver for graphics processors that combines iterative refinement with the block-Jacobi preconditioner available in the Ginkgo library.},
	keywords = {Block-Jacobi, graphics processing units (GPUs), incomplete factorization preconditioning, multiprecision, sparse linear algebra},
	doi = {https://doi.org/10.1007/978-3-030-57675-2_34},
	author = {Fritz Goebel and Hartwig Anzt and Terry Cojean and Goran Flegar and Enrique S. Quintana-Orti}
}
