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CS 290H Lecture 11 BLAS, Supernodes, and SuperLU

This lecture covers topics such as the BLAS, supernodes, and SuperLU. It also includes a reading assignment on SuperLU_DIST, a distributed-memory solver for unsymmetric linear systems. Homework 3 is due soon, and there will be no class on November 9th and 11th. Make sure to inform the assistant about your final project.

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CS 290H Lecture 11 BLAS, Supernodes, and SuperLU

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  1. CS 290H Lecture 11BLAS, Supernodes, and SuperLU • Read “SuperLU_DIST: A scalable distributed-memory sparse direct solver for unsymmetric linear systems” (reader #5) • Homework 3 due Sunday 21 November • No class next Tue 9 Nov (SC 2004) or Thu 11 Nov (holiday) • If you haven’t told me what your final project is, do so ASAP • See Kathy Yelick’s slides on matrix multiplication and BLAS

  2. for column j = 1 to n do solve pivot: swap ujj and an elt of lj scale:lj = lj / ujj j U L A ( ) L 0L I ( ) ujlj L = aj for uj, lj Left-looking Column LU Factorization • Column j of A becomes column j of L and U

  3. j k r r = fill Symmetric pruning:Set Lsr=0 if LjrUrj 0 Justification:Ask will still fill in j = pruned = nonzero s Symmetric Pruning [Eisenstat, Liu] Idea: Depth-first search in a sparser graph with the same path structure • Use (just-finished) column j of L to prune earlier columns • No column is pruned more than once • The pruned graph is the elimination tree if A is symmetric

  4. GP-Mod Algorithm [Matlab 5] • Left-looking column-by-column factorization • Depth-first search to predict structure of each column • Symmetric pruning to reduce symbolic cost +: Much cheaper symbolic factorization than GP (~4x) -: Indirect addressing for each flop (sparse vector kernel) -: Poor reuse of data in cache (BLAS-1 kernel) => Supernodes

  5. { Symmetric supernodes for Cholesky [GLN section 6.5] • Supernode = group of adjacent columns of L with same nonzero structure • Related to clique structureof filled graph G+(A) • Supernode-column update: k sparse vector ops become 1 dense triangular solve + 1 dense matrix * vector + 1 sparse vector add • Sparse BLAS 1 => Dense BLAS 2 • Only need row numbers for first column in each supernode • For model problem, integer storage for L is O(n) not O(n log n)

  6. 1 1 2 2 3 3 4 4 5 5 6 6 7 7 8 8 9 9 10 10 Factors L+U Nonsymmetric Supernodes Original matrix A

  7. for each panel do Symbolic factorization:which supernodes update the panel; Supernode-panel update:for each updating supernode do for each panel column dosupernode-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 j j+w-1 } } supernode panel Supernode-Panel Updates

  8. Sequential SuperLU • Depth-first search, symmetric pruning • Supernode-panel updates • 1D or 2D blocking chosen per supernode • Blocking parameters can be tuned to cache architecture • Condition estimation, iterative refinement, componentwise error bounds

  9. 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)

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