@techreport {,
	title = {Communication Avoiding LU with Tournament Pivoting in SLATE},
	journal = {SLATE Working Notes},
	number = {18, ICL-UT-22-01},
	year = {2022},
	month = {2022-01},
	author = {Rabab Alomairy and Mark Gates and Sebastien Cayrols and Dalal Sukkari and Kadir Akbudak and Asim YarKhan and Paul Bagwell and Jack Dongarra}
}
@conference {,
	title = {A Framework to Exploit Data Sparsity in Tile Low-Rank Cholesky Factorization},
	booktitle = {IEEE International Parallel and Distributed Processing Symposium (IPDPS)},
	year = {2022},
	month = {2022-07},
	doi = {10.1109/IPDPS53621.2022.00047},
	url = {https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=\&arnumber=9820680\&isnumber=9820610},
	author = {Qinglei Cao and Rabab Alomairy and Yu Pei and George Bosilca and Hatem Ltaief and David Keyes and Jack Dongarra}
}
@inproceedings {,
	title = {Reshaping Geostatistical Modeling and Prediction for Extreme-Scale Environmental Applications},
	journal = {2022 International Conference for High Performance Computing, Networking, Storage and Analysis (SC22)},
	year = {2022},
	month = {2022-11},
	publisher = {IEEE Press},
	address = {Dallas, TX},
	abstract = {We extend the capability of space-time geostatistical modeling using algebraic approximations, illustrating application-expected accuracy worthy of double precision from majority low-precision computations and low-rank matrix approximations. We exploit the mathematical structure of the dense covariance matrix whose inverse action and determinant are repeatedly required in Gaussian log-likelihood optimization. Geostatistics augments first-principles modeling approaches for the prediction of environmental phenomena given the availability of measurements at a large number of locations; however, traditional Cholesky-based approaches grow cubically in complexity, gating practical extension to continental and global datasets now available. We combine the linear algebraic contributions of mixed-precision and low-rank computations within a tilebased Cholesky solver with on-demand casting of precisions and dynamic runtime support from PaRSEC to orchestrate tasks and data movement. Our adaptive approach scales on various systems and leverages the Fujitsu A64FX nodes of Fugaku to achieve up to 12X performance speedup against the highly optimized dense Cholesky implementation.},
	keywords = {climate/weather prediction, dynamic runtime systems, high performance computing., low- rank matrix approximations, mixed-precision computations, space-time geospatial statistics, Task-based programming models},
	isbn = {9784665454445},
	url = {https://dl.acm.org/doi/abs/10.5555/3571885.3571888},
	author = {Cao, Qinglei and Abdulah, Sameh and Rabab Alomairy and Pei, Yu and Pratik Nag and George Bosilca and Dongarra, Jack and Genton, Marc G. and Keyes, David and Ltaief, Hatem and Sun, Ying}
}
