@article {1131,
	title = {Comparing performance of s-step and pipelined GMRES on distributed-memory multicore CPUs},
	year = {2017},
	month = {2017-07},
	publisher = {SIAM Annual Meeting},
	address = {Pittsburgh, Pennsylvania},
	author = {Ichitaro Yamazaki and Mark Hoemmen and Piotr Luszczek and Jack Dongarra}
}
@article {851,
	title = {Computing Low-rank Approximation of a Dense Matrix on Multicore CPUs with a GPU and its Application to Solving a Hierarchically Semiseparable Linear System of Equations},
	journal = {Scientific Programming},
	year = {2015},
	abstract = {Low-rank matrices arise in many scientific and engineering computation. Both computational and storage costs of manipulating such matrices may be reduced by taking advantages of their low-rank properties. To compute a low-rank approximation of a dense matrix, in this paper, we study the performance of QR factorization with column pivoting or with restricted pivoting on multicore CPUs with a GPU. We first propose several techniques to reduce the postprocessing time, which is required for restricted pivoting, on a modern CPU. We then examine the potential of using a GPU to accelerate the factorization process with both column and restricted pivoting. Our performance results on two eight-core Intel Sandy Bridge CPUs with one NVIDIA Kepler GPU demonstrate that using the GPU, the factorization time can be reduced by a factor of more than two. In addition, to study the performance of our implementations in practice, we integrate them into a recently-developed software StruMF which algebraically exploits such low-rank structures for solving a general sparse linear system of equations. Our performance results for solving Poisson{\textquoteright}s equations demonstrate that the proposed techniques can significantly reduce the preconditioner construction time of StruMF on the CPUs, and the construction time can be further reduced by 10\%-50\% using the GPU.},
	author = {Ichitaro Yamazaki and Stanimire Tomov and Jack Dongarra}
}
@article {815,
	title = {Communication-Avoiding Symmetric-Indefinite Factorization},
	journal = {SIAM Journal on Matrix Analysis and Application},
	volume = {35},
	year = {2014},
	month = {2014-07},
	pages = {1364-1406},
	abstract = {We describe and analyze a novel symmetric triangular factorization algorithm. The algorithm is essentially a block version of Aasen{\textquoteright}s triangular tridiagonalization. It factors a dense symmetric matrix A as the product A = P LT L T P T where P is a permutation matrix, L is lower triangular, and T is block tridiagonal and banded. The algorithm is the first symmetric-indefinite communication-avoiding factorization: it performs an asymptotically optimal amount of communication in a two-level memory hierarchy for almost any cache-line size. Adaptations of the algorithm to parallel computers are likely to be communication efficient as well; one such adaptation has been recently published. The current paper describes the algorithm, proves that it is numerically stable, and proves that it is communication optimal.},
	keywords = {plasma},
	author = {Grey Ballard and Dulceneia Becker and James Demmel and Jack Dongarra and Alex Druinsky and I Peled and Oded Schwartz and Sivan Toledo and Ichitaro Yamazaki}
}
