Sparse MatrixVector Multiplication - PowerPoint PPT Presentation

1 / 15
About This Presentation
Title:

Sparse MatrixVector Multiplication

Description:

RMAT Approximate Power-Law Graph. 8. Strongly Connected Components. 9 ... Breadth-First Search: Sparse mat * vec. x. ATx. 1. 2. 3. 4. 7. 6. 5. AT ... – PowerPoint PPT presentation

Number of Views:45
Avg rating:3.0/5.0
Slides: 16
Provided by: csU45
Category:

less

Transcript and Presenter's Notes

Title: Sparse MatrixVector Multiplication


1
Sparse Matrix-Vector Multiplication
2
Clustering benchmark graph
3
Link analysis of the web
  • Web page vertex
  • Link directed edge
  • Link matrix Aij 1 if page i links to page j

4
Web graph PageRank (Google)
Brin, Page
An important page is one that many important
pages point to.
  • Markov process follow a random link most of the
    time otherwise, go to any page at random.
  • Importance stationary distribution of Markov
    process.
  • Transition matrix is pA (1-p)ones(size(A)),
    scaled so each column sums to 1.
  • Importance of page i is the i-th entry in the
    principal eigenvector of the transition matrix.
  • But, the matrix is 8,000,000,000 by 8,000,000,000.

5
A Page Rank Matrix
  • Importance ranking of web pages
  • Stationary distribution of a Markov chain
  • Power method matvec and vector arithmetic
  • MatlabP page ranking demo (from SC03) on
    a web crawl of mit.edu (170,000 pages)

6
Strongly connected components
  • Symmetric permutation to block triangular form
  • Find P in linear time by depth-first search
    Tarjan

7
RMAT Approximate Power-Law Graph
8
Strongly Connected Components
9
Sparse Adjacency Matrix and Graph
?
AT
x
ATx
  • Adjacency matrix sparse array w/ nonzeros for
    graph edges
  • Storage-efficient implementation from sparse data
    structures

10
Breadth-First Search Sparse mat vec
?
AT
x
ATx
  • Multiply by adjacency matrix ? step to neighbor
    vertices
  • Work-efficient implementation from sparse data
    structures

11
Breadth-First Search Sparse mat vec
?
AT
x
ATx
  • Multiply by adjacency matrix ? step to neighbor
    vertices
  • Work-efficient implementation from sparse data
    structures

12
Breadth-First Search Sparse mat vec
?
AT
x
ATx
  • Multiply by adjacency matrix ? step to neighbor
    vertices
  • Work-efficient implementation from sparse data
    structures

13
Sparse Matrix times Sparse Matrix
  • Shows up often as a primitive.
  • Graphs are mostly not mesh-like, i.e. geometric
    locality and good separators.
  • On a 2D processor grid, the parallel sparse
    algorithm looks much like the parallel dense
    algorithm.
  • Redistribute to round-robin cyclic or random
    distribution for load balance.

14
Load Balance Without Redistribution
15
Load Balance With Redistribution
Write a Comment
User Comments (0)
About PowerShow.com