@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}
}
@article {icl:691,
	title = {Implementing a Blocked Aasen{\textquoteright}s Algorithm with a Dynamic Scheduler on Multicore Architectures},
	journal = {IPDPS 2013 (submitted)},
	year = {2013},
	month = {2013-00},
	address = {Boston, MA},
	abstract = {Factorization of a dense symmetric indefinite matrix is a key computational kernel in many scientific and engineering simulations. However, there is no scalable factorization algorithm that takes advantage of the symmetry and guarantees numerical stability through pivoting at the same time. This is because such an algorithm exhibits many of the fundamental challenges in parallel programming like irregular data accesses and irregular task dependencies. In this paper, we address these challenges in a tiled implementation of a blocked Aasen{\textquoteright}s algorithm using a dynamic scheduler. To fully exploit the limited parallelism in this left-looking algorithm, we study several performance enhancing techniques; e.g., parallel reduction to update a panel, tall-skinny LU factorization algorithms to factorize the panel, and a parallel implementation of symmetric pivoting. Our performance results on up to 48 AMD Opteron processors demonstrate that our implementation obtains speedups of up to 2.8 over MKL, while losing only one or two digits in the computed residual norms.},
	author = {Ichitaro Yamazaki and Dulceneia Becker and Jack Dongarra and Alex Druinsky and I. Peled and Sivan Toledo and Grey Ballard and James Demmel and Oded Schwartz}
}
