@article {,
	title = {Using Ginkgo{\textquoteright}s memory accessor for improving the accuracy of memory-bound low precision BLAS},
	journal = {Software: Practice and Experience},
	volume = {532},
	year = {2023},
	month = {Jan-01-2023},
	pages = {81 - 98},
	issn = {0038-0644},
	doi = {10.1002/spe.v53.110.1002/spe.3041},
	url = {https://doi.org/10.1002/spe.3041},
	author = {Gr{\"u}tzmacher, Thomas and Anzt, Hartwig and Quintana-Ort{\'\i}, Enrique S.}
}
@article {,
	title = {Compressed basis GMRES on high-performance graphics processing units},
	journal = {The International Journal of High Performance Computing Applications},
	year = {2022},
	month = {2022-05},
	abstract = {Krylov methods provide a fast and highly parallel numerical tool for the iterative solution of many large-scale sparse linear systems. To a large extent, the performance of practical realizations of these methods is constrained by the communication bandwidth in current computer architectures, motivating the investigation of sophisticated techniques to avoid, reduce, and/or hide the message-passing costs (in distributed platforms) and the memory accesses (in all architectures). This article leverages Ginkgo{\textquoteright}s memory accessor in order to integrate a communication-reduction strategy into the (Krylov) GMRES solver that decouples the storage format (i.e., the data representation in memory) of the orthogonal basis from the arithmetic precision that is employed during the operations with that basis. Given that the execution time of the GMRES solver is largely determined by the memory accesses, the cost of the datatype transforms can be mostly hidden, resulting in the acceleration of the iterative step via a decrease in the volume of bits being retrieved from memory. Together with the special properties of the orthonormal basis (whose elements are all bounded by 1), this paves the road toward the aggressive customization of the storage format, which includes some floating-point as well as fixed-point formats with mild impact on the convergence of the iterative process. We develop a high-performance implementation of the {\textquotedblleft}compressed basis GMRES{\textquotedblright} solver in the Ginkgo sparse linear algebra library using a large set of test problems from the SuiteSparse Matrix Collection. We demonstrate robustness and performance advantages on a modern NVIDIA V100 graphics processing unit (GPU) of up to 50\% over the standard GMRES solver that stores all data in IEEE double-precision.},
	issn = {1094-3420},
	doi = {10.1177/10943420221115140},
	url = {http://journals.sagepub.com/doi/10.1177/10943420221115140},
	author = {Aliaga, Jos{\'e} I and Anzt, Hartwig and Gr{\"u}tzmacher, Thomas and Quintana-Ort{\'\i}, Enrique S and Andres E. Thomas}
}
@article {,
	title = {Compression and load balancing for efficient sparse matrix-vector product on multicore processors and graphics processing units},
	journal = {Concurrency and Computation: Practice and Experience},
	volume = {34},
	year = {2022},
	month = {2022-06},
	issn = {1532-0626},
	doi = {10.1002/cpe.6515},
	url = {https://doi.org/10.1002/cpe.6515},
	author = {Aliaga, Jos{\'e} I. and Anzt, Hartwig and Gr{\"u}tzmacher, Thomas and Quintana-Orti, Enrique S. and Andres E. Thomas}
}
@article {,
	title = {Ginkgo: A Modern Linear Operator Algebra Framework for High Performance Computing},
	journal = {ACM Transactions on Mathematical Software},
	volume = {48},
	year = {2022},
	month = {2022-03},
	pages = {1 - 33},
	abstract = {In this article, we present Ginkgo, a modern C++ math library for scientific high performance computing. While classical linear algebra libraries act on matrix and vector objects, Ginkgo{\textquoteright}s design principle abstracts all functionality as {\textquotedblleft}linear operators,{\textquotedblright} motivating the notation of a {\textquotedblleft}linear operator algebra library.{\textquotedblright} Ginkgo{\textquoteright}s current focus is oriented toward providing sparse linear algebra functionality for high performance graphics processing unit (GPU) architectures, but given the library design, this focus can be easily extended to accommodate other algorithms and hardware architectures. We introduce this sophisticated software architecture that separates core algorithms from architecture-specific backends and provide details on extensibility and sustainability measures. We also demonstrate Ginkgo{\textquoteright}s usability by providing examples on how to use its functionality inside the MFEM and deal.ii finite element ecosystems. Finally, we offer a practical demonstration of Ginkgo{\textquoteright}s high performance on state-of-the-art GPU architectures.},
	issn = {0098-3500},
	doi = {10.1145/3480935},
	url = {https://dl.acm.org/doi/10.1145/3480935},
	author = {Anzt, Hartwig and Cojean, Terry and Flegar, Goran and G{\"o}bel, Fritz and Gr{\"u}tzmacher, Thomas and Nayak, Pratik and Ribizel, Tobias and Tsai, Yuhsiang Mike and Quintana-Ort{\'\i}, Enrique S}
}
