SC07MatlabWorkshop12nov2007

SC07MatlabWorkshop12nov2007 - Parallel Sparse Operations in...

Info iconThis preview shows pages 1–12. Sign up to view the full content.

View Full Document Right Arrow Icon
1   Parallel Sparse Operations in Matlab: Exploring Large Graphs John R. Gilbert University of California at Santa Barbara Aydin Buluc (UCSB) Brad McRae (NCEAS) Steve Reinhardt (Interactive Supercomputing) with thanks to Alan Edelman (MIT & ISC) and Jeremy Kepner (MIT-LL) Support: DOE, NSF, DARPA, SGI, ISC
Background image of page 1

Info iconThis preview has intentionally blurred sections. Sign up to view the full version.

View Full Document Right Arrow Icon
2 3D Spectral Coordinates
Background image of page 2
3 2D Histogram: RMAT Graph
Background image of page 3

Info iconThis preview has intentionally blurred sections. Sign up to view the full version.

View Full Document Right Arrow Icon
4 Strongly Connected Components
Background image of page 4
5 Social Network Analysis in Matlab: 1993 Co-author graph from 1993 Householder symposium
Background image of page 5

Info iconThis preview has intentionally blurred sections. Sign up to view the full version.

View Full Document Right Arrow Icon
6 Combinatorial Scientific Computing Emerging large scale, high-performance applications: Web search and information retrieval Knowledge discovery Computational biology Dynamical systems Machine learning Bioinformatics Sparse matrix methods Geometric modeling . . . How will combinatorial methods be used by nonexperts?
Background image of page 6
7 Outline Infrastructure: Array-based sparse graph computation An application: Computational ecology Some nuts and bolts: Sparse matrix multiplication
Background image of page 7

Info iconThis preview has intentionally blurred sections. Sign up to view the full version.

View Full Document Right Arrow Icon
8 Matlab*P A = rand(4000 *p , 4000 *p ); x = randn(4000 *p , 1); y = zeros(size(x)); while norm(x-y) / norm(x) > 1e-11 y = x; x = A*x; x = x / norm(x); end;
Background image of page 8
9 MATLAB ® Star-P Architecture Ordinary Matlab variables Star-P client manager server manager package manager processor #0 processor #n-1 processor #1 processor #2 processor #3 . . . ScaLAPACK FFTW FPGA interface matrix manager Distributed matrices sort dense/sparse UPC user code MPI user code
Background image of page 9

Info iconThis preview has intentionally blurred sections. Sign up to view the full version.

View Full Document Right Arrow Icon
10 P 0 P 1 P 2 P n 59 41 53 26 31 2 3 1 3 1 Each processor stores local vertices & edges in a compressed row structure. Has been scaled to >10 8 vertices, >10 9 edges in interactive session. Distributed Sparse Array Structure 1 2 3 26 53 41 31 59
Background image of page 10
11 Sparse Array and Matrix Operations dsparse layout, same semantics as ordinary full & sparse Matrix arithmetic: + , max , sum , etc. matrix * matrix
Background image of page 11

Info iconThis preview has intentionally blurred sections. Sign up to view the full version.

View Full Document Right Arrow Icon
Image of page 12
This is the end of the preview. Sign up to access the rest of the document.

{[ snackBarMessage ]}

Page1 / 35

SC07MatlabWorkshop12nov2007 - Parallel Sparse Operations in...

This preview shows document pages 1 - 12. Sign up to view the full document.

View Full Document Right Arrow Icon
Ask a homework question - tutors are online