@conference {812,
	title = {Hybrid Multi-Elimination ILU Preconditioners on GPUs},
	booktitle = {International Heterogeneity in Computing Workshop (HCW), IPDPS 2014},
	year = {2014},
	month = {2014-05},
	publisher = {IEEE},
	organization = {IEEE},
	address = {Phoenix, AZ},
	abstract = {Abstract{\textemdash}Iterative solvers for sparse linear systems often benefit from using preconditioners. While there are implementations for many iterative methods that leverage the computing power of accelerators, porting the latest developments in preconditioners to accelerators has been challenging. In this paper we develop a selfadaptive multi-elimination preconditioner for graphics processing units (GPUs). The preconditioner is based on a multi-level incomplete LU factorization and uses a direct dense solver for the bottom-level system. For test matrices from the University of Florida matrix collection, we investigate the influence of handling the triangular solvers in the distinct iteration steps in either single or double precision arithmetic. Integrated into a Conjugate Gradient method, we show that our multi-elimination algorithm is highly competitive against popular preconditioners, including multi-colored symmetric Gauss-Seidel relaxation preconditioners, and (multi-colored symmetric) ILU for numerous problems.},
	author = {Dimitar Lukarski and Hartwig Anzt and Stanimire Tomov and Jack Dongarra}
}
@conference {714,
	title = {Self-Adaptive Multiprecision Preconditioners on Multicore and Manycore Architectures},
	booktitle = {VECPAR 2014},
	year = {2014},
	month = {2014-06},
	address = {Eugene, OR},
	abstract = {Based on the premise that preconditioners needed for scientific computing are not only required to be robust in the numerical sense, but also scalable for up to thousands of light-weight cores, we argue that this two-fold goal is achieved for the recently developed self-adaptive multi-elimination preconditioner. For this purpose, we revise the underlying idea and analyze the performance of implementations realized in the PARALUTION and MAGMA open-source software libraries on GPU architectures (using either CUDA or OpenCL), Intel{\textquoteright}s Many Integrated Core Architecture, and Intel{\textquoteright}s Sandy Bridge processor. The comparison with other well-established preconditioners like multi-coloured Gauss-Seidel, ILU(0) and multi-colored ILU(0), shows that the twofold goal of a numerically stable cross-platform performant algorithm is achieved.},
	author = {Hartwig Anzt and Dimitar Lukarski and Stanimire Tomov and Jack Dongarra}
}
