@inbook {,
	title = {Approximate Computing for Scientific Applications},
	booktitle = {Approximate Computing Techniques},
	year = {2022},
	month = {2022-01},
	pages = {415 - 465},
	publisher = {Springer International Publishing},
	organization = {Springer International Publishing},
	edition = {322},
	abstract = {This chapter reviews the performance benefits that result from applying (software) approximate computing to scientific applications. For this purpose, we target two particular areas, linear algebra and deep learning, with the first one selected for being ubiquitous in scientific problems and the second one for its considerable and growing number of important applications both in industry and science.

The review of linear algebra in scientific computing is focused on the iterative solution of sparse linear systems, exposing the prevalent costs of memory accesses in these methods, and demonstrating how approximate computing can help to reduce these overheads, for example, in the case of stationary solvers themselves or the application of preconditioners for the solution of sparse linear systems via Krylov subspace methods.

The discussion of deep learning is focused on the use of approximate data transfer for cutting costs of host-to-device operations, as well as the use of adaptive precision for accelerating training of classical CNN architectures. Additionally we discuss model optimization and architecture search in presence of constraints for edge devices applications.},
	isbn = {978-3-030-94704-0},
	doi = {10.1007/978-3-030-94705-7_14},
	url = {https://link.springer.com/chapter/10.1007/978-3-030-94705-7_14},
	author = {Anzt, Hartwig and Casas, Marc and Malossi, ~Cristiano I. and Quintana-Ort{\'\i}, Enrique S and Scheidegger, Florian and Zhuang, Sicong},
	editor = {Bosio, Alberto and M{\'e}nard, Daniel and Sentieys, Olivier}
}
@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 = {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}
}
@article {,
	title = {Resiliency in numerical algorithm design for extreme scale simulations},
	journal = {The International Journal of High Performance Computing Applications},
	volume = {36371337212766180823},
	year = {2022},
	month = {2022-03},
	pages = {251 - 285},
	keywords = {Fault tolerance, Numerical algorithms, parallel computer architecture, resilience},
	issn = {1094-3420},
	doi = {10.1177/10943420211055188},
	url = {http://journals.sagepub.com/doi/10.1177/10943420211055188http://journals.sagepub.com/doi/pdf/10.1177/10943420211055188http://journals.sagepub.com/doi/pdf/10.1177/10943420211055188http://journals.sagepub.com/doi/full-xml/10.1177/10943420211055188},
	author = {Agullo, Emmanuel and Altenbernd, Mirco and Anzt, Hartwig and Bautista-Gomez, Leonardo and Benacchio, Tommaso and Bonaventura, Luca and Bungartz, Hans-Joachim and Chatterjee, Sanjay and Ciorba, Florina M and DeBardeleben, Nathan and Drzisga, Daniel and Eibl, Sebastian and Engelmann, Christian and Gansterer, Wilfried N and Giraud, Luc and G{\"o}ddeke, Dominik and Heisig, Marco and J{\'e}z{\'e}quel, Fabienne and Kohl, Nils and Li, Xiaoye Sherry and Lion, Romain and Mehl, Miriam and Mycek, Paul and Obersteiner, Michael and Quintana-Ort{\'\i}, Enrique S and Rizzi, Francesco and R{\"u}de, Ulrich and Schulz, Martin and Fung, Fred and Speck, Robert and Stals, Linda and Teranishi, Keita and Thibault, Samuel and Th{\"o}nnes, Dominik and Wagner, Andreas and Wohlmuth, Barbara}
}
