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Sparse Matrices: Structured vs. Unstructured Representations

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daulton
21 diapositives · 304 Views · Jan 06, 2020

Learn about sparse matrices, their structured and unstructured representations, and various methods for storing and accessing data efficiently.

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Sparse Matrices: Structured vs. Unstructured Representations
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  1. Sparse Matrices sparse … many elements are zero dense … few elements are zero

  2. Example Of Sparse Matrices diagonal tridiagonal lower triangular (?) These are structured sparse matrices. May be mapped into a 1D array so that a mapping function can be used to locate an element.

  3. Unstructured Sparse Matrices Airline flight matrix. airports are numbered 1 through n flight(i,j)= list of nonstop flights from airport i to airport j n = 1000 (say) n x n array of list references => 4 million bytes total number of flights = 20,000 (say) need at most 20,000 list references => at most 80,000 bytes

  4. Unstructured Sparse Matrices Web page matrix. web pages are numbered 1 through n web(i,j)= number of links from page i to page j Web analysis. authority page … page that has many links to it hub page … links to many authority pages

  5. Web Page Matrix n = 2 billion (and growing by 1 million a day) n x n array of ints => 16 * 1018 bytes (16 * 109 GB) each page links to 10 (say) other pages on average on average there are 10 nonzero entries per row space needed for nonzero elements is approximately 20 billion x 4 bytes = 80 billion bytes (80 GB)

  6. Representation Of Unstructured Sparse Matrices Single linear list in row-major order. scan the nonzero elements of the sparse matrix in row-major order each nonzero element is represented by a triple (row, column, value) the list of triples may be an array list or a linked list (chain)

  7. list = row 1 1 2 2 4 4 column 3 5 3 4 2 3 value 3 4 5 7 2 6 Single Linear List Example 0 0 3 0 4 0 0 5 7 0 0 0 0 0 0 0 2 6 0 0

  8. element 0 1 2 3 4 5 row 1 1 2 2 4 4 column 3 5 3 4 2 3 value 3 4 5 7 2 6 Array Linear List Representation row 1 1 2 2 4 4 list = column 3 5 3 4 2 3 value 3 4 5 7 2 6

  9. row col value next Chain Representation Node structure.

  10. 1 3 1 5 2 3 2 4 4 2 4 3 3 4 5 7 2 6 null firstNode Single Chain row 1 1 2 2 4 4 list = column 3 5 3 4 2 3 value 3 4 5 7 2 6

  11. One Linear List Per Row row1 = [(3, 3), (5,4)] row2 = [(3,5), (4,7)] row3 = [] row4 = [(2,2), (3,6)] 0 0 3 0 4 0 0 5 7 0 0 0 0 0 0 0 2 6 0 0

  12. next col value Array Of Row Chains Node structure.

  13. null 3 3 5 4 null 3 5 4 7 null 2 2 3 6 row[] Array Of Row Chains 0 0 3 0 4 0 0 5 7 0 0 0 0 0 0 0 2 6 0 0 null

  14. row col value down next Orthogonal List Representation Both row and column lists. Node structure.

  15. 1 3 1 5 3 4 n 2 3 2 4 5 7 n null 4 2 4 3 2 6 n Row Lists 0 0 3 0 4 0 0 5 7 0 0 0 0 0 0 0 2 6 0 0

  16. 1 3 1 5 3 4 n 2 3 2 4 5 7 4 2 4 3 2 6 n n Column Lists 0 0 3 0 4 0 0 5 7 0 0 0 0 0 0 0 2 6 0 0

  17. 1 3 1 5 3 4 n n 2 3 2 4 5 7 n null 4 2 4 3 2 6 n n n row[] Orthogonal Lists 0 0 3 0 4 0 0 5 7 0 0 0 0 0 0 0 2 6 0 0

  18. Variations May use circular lists instead of chains.

  19. Approximate Memory Requirements 500 x 500 matrix with 1994 nonzero elements 2D array 500 x 500 x 4 = 1million bytes Single Array List 3 x 1994 x 4 = 23,928 bytes One Chain Per Row 23928 + 500 x 4 = 25,928

  20. Runtime Performance Matrix Transpose 500 x 500 matrix with 1994 nonzero elements 2D array 210 ms Single Array List 6 ms One Chain Per Row 12 ms

  21. Performance Matrix Addition. 500 x 500 matrices with 1994 and 999 nonzero elements 2D array 880 ms Single Array List 18 ms One Chain Per Row 29 ms