Parallel Computing Toolbox™

speye - Create codistributed sparse identity matrix

Syntax

D = speye(n, dist)
D = speye(m, n, dist)
D = speye([m, n], dist)

Description

D = speye(n, dist) creates an n-by-n sparse codistributed array of underlying class double. D is distributed by dimension dim, where dim = distributionDimension(dist), and with partition PAR, where par=distributionPartition(dist). If dim is unspecified, then D is distributed by its second dimension. If PAR is unspecified, then D uses defaultPartition(n) as its partition. The easiest way to do this is to use a default codistributor where both dim and PAR are unspecified (dist=codistributor()) as input to speye.

D = speye(m, n, dist) and D = speye([m, n], dist) create an m-by-n sparse codistributed array of underlying class double. The distribution dimension dim and partition PAR may be specified by dist as above, but if they are not specified, dim is taken to be the last nonsingleton dimension of D and PAR is provided by defaultPartition over the size in that dimension.

Note   To create a sparse codistributed array of underlying class logical, first create an array of underlying class double and then cast it using the logical function: logical(speye(m, n, dist))

Examples

With four labs,

D = speye(1000, codistributor())

creates a 1000-by-1000 sparse codistributed double array D, distributed by its second dimension (columns). Each lab contains a 1000-by-250 local piece of D.

D = speye(10, 10, codistributor('1d', 2, 1:numlabs))

creates a 10-by-10 sparse codistributed double array D, distributed by its columns. Each lab contains a 10-by-labindex local piece of D.

See Also

speye MATLAB function reference page

cell, eye, false, Inf, NaN, ones, rand, randn, sparse, sprand, sprandn, true, zeros

sparse

spmd