@article {anzt2018incomplete,
	title = {Incomplete Sparse Approximate Inverses for Parallel Preconditioning},
	journal = {Parallel Computing},
	volume = {71},
	year = {2018},
	month = {2018-01},
	pages = {1{\textendash}22},
	abstract = {In this paper, we propose a new preconditioning method that can be seen as a generalization of block-Jacobi methods, or as a simplification of the sparse approximate inverse (SAI) preconditioners. The {\textquotedblleft}Incomplete Sparse Approximate Inverses{\textquotedblright} (ISAI) is in particular efficient in the solution of sparse triangular linear systems of equations. Those arise, for example, in the context of incomplete factorization preconditioning. ISAI preconditioners can be generated via an algorithm providing fine-grained parallelism, which makes them attractive for hardware with a high concurrency level. In a study covering a large number of matrices, we identify the ISAI preconditioner as an attractive alternative to exact triangular solves in the context of incomplete factorization preconditioning.},
	issn = {01678191},
	doi = {10.1016/j.parco.2017.10.003},
	url = {http://www.sciencedirect.com/science/article/pii/S016781911730176X},
	author = {Hartwig Anzt and Thomas Huckle and J{\"u}rgen Br{\"a}ckle and Jack Dongarra}
}
