CS 290H Lecture 12 Column intersection graphs, Ordering for sparsity in LU with partial pivoting Read “Computing the block triangular form of a sparse.

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CS 290H Lecture 12 Column intersection graphs, Ordering for sparsity in LU with partial pivoting Read “Computing the block triangular form of a sparse matrix” (reader #6) Homework 3 due Sunday 21 November No class next Tue 9 Nov (SC 2004) or Thu 11 Nov (holiday)

Left-looking Column LU Factorization for column j = 1 to n do solve pivot: swap u jj and an elt of l j scale: l j = l j / u jj Column j of A becomes column j of L and U L 0 L I ( ) ujljujlj = a j for u j, l j L L U A j

Supernode-Panel Updates for each panel do Symbolic factorization: which supernodes update the panel; Supernode-panel update: for each updating supernode do for each panel column do supernode-column update; Factorization within panel: use supernode-column algorithm +: “BLAS-2.5” replaces BLAS-1 -: Very big supernodes don’t fit in cache => 2D blocking of supernode-column updates jj+w-1 supernode panel } }

SuperLU: Relative Performance Speedup over GP column-column 22 matrices: Order 765 to 76480; GP factor time 0.4 sec to 1.7 hr SGI R8000 (1995)

Nonsymmetric Ax = b: Gaussian elimination with partial pivoting PA = LU Sparse, nonsymmetric A Rows permuted by partial pivoting Columns may be preordered for sparsity = x P

Column Intersection Graph G  (A) = G(A T A) if no cancellation (otherwise  ) Permuting the rows of A does not change G  (A) AG  (A)ATAATA

Filled Column Intersection Graph G  (A) = symbolic Cholesky factor of A T A In PA=LU, G(U)  G  (A) and G(L)  G  (A) Tighter bound on L from symbolic QR Bounds are best possible if A is strong Hall A chol (A T A) G  (A)

Column Elimination Tree Elimination tree of A T A (if no cancellation) Depth-first spanning tree of G  (A) Represents column dependencies in various factorizations A chol (A T A) T  (A) +

Column Dependencies in PA = LU If column j modifies column k, then j  T  [k]. k j T[k]T[k] If A is strong Hall then, for some pivot sequence, every column modifies its parent in T  (A).

Efficient Structure Prediction Given the structure of (unsymmetric) A, one can find... column elimination tree T  (A) row and column counts for G  (A) supernodes of G  (A) nonzero structure of G  (A)... without forming G  (A) or A T A + + +

Column Preordering for Sparsity PAQ T = LU: Q preorders columns for sparsity, P is row pivoting Column permutation of A  Symmetric permutation of A T A (or G  (A)) Symmetric ordering: Approximate minimum degree But, forming A T A is expensive (sometimes bigger than L+U). = x P Q

Column Approximate Minimum Degree Column Approximate Minimum Degree [Matlab 6] Eliminate “row” nodes of aug(A) first Then eliminate “col” nodes by approximate min degree 4x speed and 1/3 better ordering than Matlab-5 min degree, 2x speed of AMD on A T A Can also use other orderings, e.g. nested dissection on aug(A) A A ATAT I I row col aug(A) G(aug(A))