Creates a sparse parallel matrix in
MATAIJ format (the default parallel PETSc format). For good matrix assembly performance the user should preallocate the matrix storage by setting the parameters
comm - MPI communicator
m - number of local rows (or
PETSC_DECIDEto have calculated if M is given) This value should be the same as the local size used in creating the y vector for the matrix-vector product y = Ax.
n - This value should be the same as the local size used in creating the x vector for the matrix-vector product y = Ax. (or
PETSC_DECIDEto have calculated if N is given) For square matrices n is almost always m.
M - number of global rows (or
PETSC_DETERMINEto have calculated if m is given)
N - number of global columns (or
PETSC_DETERMINEto have calculated if n is given)
d_nz - number of nonzeros per row in DIAGONAL portion of local submatrix (same value is used for all local rows)
d_nnz - array containing the number of nonzeros in the various rows of the DIAGONAL portion of the local submatrix (possibly different for each row) or
d_nzis used to specify the nonzero structure. The size of this array is equal to the number of local rows, i.e ‘m’.
o_nz - number of nonzeros per row in the OFF-DIAGONAL portion of local submatrix (same value is used for all local rows).
o_nnz - array containing the number of nonzeros in the various rows of the OFF-DIAGONAL portion of the local submatrix (possibly different for each row) or
o_nzis used to specify the nonzero structure. The size of this array is equal to the number of local rows, i.e ‘m’.
A - the matrix
Options Database Keys#
-mat_no_inode - Do not use inodes
-Sets inode limit (max limit=5)
-View the vecscatter (i.e., communication pattern) used in
MatMult()of sparse parallel matrices. See viewer types in manual of
MatView(). Of them, ascii_matlab, draw or binary cause the vecscatter be viewed as a matrix. Entry (i,j) is the size of message (in bytes) rank i sends to rank j in one
It is recommended that one use
MatCreateFromOptions() or the
MatXXXXSetPreallocation() paradigm instead of this routine directly.
[MatXXXXSetPreallocation() is, for example,
If the *_nnz parameter is given then the *_nz parameter is ignored
N parameters specify the size of the matrix, and its partitioning across
o_nnz parameters specify the approximate
storage requirements for this matrix.
The user MUST specify either the local or global matrix dimensions (possibly both).
The parallel matrix is partitioned across processors such that the first m0 rows belong to process 0, the next m1 rows belong to process 1, the next m2 rows belong to process 2 etc.. where m0,m1,m2,.. are the input parameter ‘m’. i.e each processor stores values corresponding to [m x N] submatrix.
The columns are logically partitioned with the n0 columns belonging to 0th partition, the next n1 columns belonging to the next partition etc.. where n0,n1,n2… are the input parameter ‘n’.
The DIAGONAL portion of the local submatrix on any given processor is the submatrix corresponding to the rows and columns m,n corresponding to the given processor. i.e diagonal matrix on process 0 is [m0 x n0], diagonal matrix on process 1 is [m1 x n1] etc. The remaining portion of the local submatrix [m x (N-n)] constitute the OFF-DIAGONAL portion. The example below better illustrates this concept.
For a square global matrix we define each processor’s diagonal portion to be its local rows and the corresponding columns (a square submatrix); each processor’s off-diagonal portion encompasses the remainder of the local matrix (a rectangular submatrix).
d_nnz are specified, then
d_nz are ignored.
When calling this routine with a single process communicator, a matrix of
MATSEQAIJ is returned. If a matrix of type
MATMPIAIJ is desired for this
type of communicator, use the construction mechanism
By default, this format uses inodes (identical nodes) when possible. We search for consecutive rows with the same nonzero structure, thereby reusing matrix information to achieve increased efficiency.
Consider the following 8x8 matrix with 34 non-zero values, that is assembled across 3 processors. Lets assume that proc0 owns 3 rows, proc1 owns 3 rows, proc2 owns 2 rows. This division can be shown as follows
1 2 0 | 0 3 0 | 0 4 Proc0 0 5 6 | 7 0 0 | 8 0 9 0 10 | 11 0 0 | 12 0 ------------------------------------- 13 0 14 | 15 16 17 | 0 0 Proc1 0 18 0 | 19 20 21 | 0 0 0 0 0 | 22 23 0 | 24 0 ------------------------------------- Proc2 25 26 27 | 0 0 28 | 29 0 30 0 0 | 31 32 33 | 0 34
This can be represented as a collection of submatrices as
A B C D E F G H I
Where the submatrices A,B,C are owned by proc0, D,E,F are owned by proc1, G,H,I are owned by proc2.
The ‘m’ parameters for proc0,proc1,proc2 are 3,3,2 respectively. The ‘n’ parameters for proc0,proc1,proc2 are 3,3,2 respectively. The ‘M’,’N’ parameters are 8,8, and have the same values on all procs.
The DIAGONAL submatrices corresponding to proc0,proc1,proc2 are
submatrices [A], [E], [I] respectively. The OFF-DIAGONAL submatrices
corresponding to proc0,proc1,proc2 are [BC], [DF], [GH] respectively.
Internally, each processor stores the DIAGONAL part, and the OFF-DIAGONAL
MATSEQAIJ matrices. For example, proc1 will store [E] as a
matrix, ans [DF] as another SeqAIJ matrix.
o_nz parameters are specified,
d_nz storage elements are
allocated for every row of the local diagonal submatrix, and
storage locations are allocated for every row of the OFF-DIAGONAL submat.
One way to choose
o_nz is to use the max nonzerors per local
rows for each of the local DIAGONAL, and the OFF-DIAGONAL submatrices.
In this case, the values of
proc0 dnz = 2, o_nz = 2 proc1 dnz = 3, o_nz = 2 proc2 dnz = 1, o_nz = 4
We are allocating m*(
o_nz) storage locations for every proc. This
translates to 3*(2+2)=12 for proc0, 3*(3+2)=15 for proc1, 2*(1+4)=10
for proc3. i.e we are using 12+15+10=37 storage locations to store
o_nnz parameters are specified, the storage is specified
for every row, corresponding to both DIAGONAL and OFF-DIAGONAL submatrices.
In the above case the values for d_nnz,o_nnz are
proc0 d_nnz = [2,2,2] and o_nnz = [2,2,2] proc1 d_nnz = [3,3,2] and o_nnz = [2,1,1] proc2 d_nnz = [1,1] and o_nnz = [4,4]
Here the space allocated is sum of all the above values i.e 34, and hence pre-allocation is perfect.