Parallel Computing Toolbox™

NaN - Create distributed array of NaN values

Syntax

D = NaN(n, dist)
D = NaN(m, n, dist)
D = NaN([m, n], dist)
D = NaN(..., classname, dist)

Description

D = NaN(n, dist) creates an n-by-n distributed 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 distributor where both dim and PAR are unspecified (dist = distributor()) as input to eye.

D = NaN(m, n, dist) and D = NaN([m, n], dist) create an m-by-n distributed array of underlying class double. The distribution dimension dim and partition PAR can 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.

D = NaN(..., classname, dist) optionally specifies the class of the distributed array D. Valid choices are the same as for the regular NaN function: 'double' (the default), and 'single'.

Examples

With four labs,

D = NaN(1000, distributor())

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

D = NaN(10, 10, 'single', distributor('1d', 2, 1:numlabs))

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

See Also

NaN MATLAB^® function reference page

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

mpiSettings

numlabs