NaN
Create codistributed array of all NaN
values
Syntax
Description
creates an cN
= NaN(n
,codist
)n
-by-n
codistributed matrix of all
NaN
values and uses codist
to specify the
distribution of the array values across the workers.
Specify codist
as "codistributed"
to use the
default codistributor1d
distribution scheme or the distribution scheme
defined by a codistributor1d
or codistributor2dbc
object.
When you create the codistributed array in a communicating job or spmd
block, the function creates an array on each worker. If you create a codistributed array
outside of a communicating job or spmd
block, the array is stored only on
the worker or client that creates the codistributed array.
By default, the codistributed array has the underlying type double
.
creates an X
= NaN(sz1,...,szN
,codist
)sz1
-by-...-by-szN
codistributed array of
all NaN
values where sz1,...,szN
indicates the
size of each dimension.
creates a codistributed array of all cN
= NaN(___,datatype
,codist
)NaN
values with the underlying
type datatype
. For example,
NaN(1,"single","codistributed")
creates a codistributed single
integer NaN
. You can use this syntax with any of the size arguments in
the previous syntaxes. You must specify codist
after the array size
and data type arguments.
creates a codistributed array of all cN
= NaN(___,"noCommunication")NaN
values without using
communication between workers.
When you create very large arrays or your communicating job or spmd
block uses many workers, worker-worker communication can slow down array creation. Use this
syntax to improve the performance of your code by removing the time required for
worker-worker communication.
Tip
When you use this syntax, some error checking steps are skipped. Use this syntax
to improve the performance of your code after you prototype your code without
specifying "noCommunication"
.
uses the array cN
= NaN(___,like=p
)p
to create a codistributed array of all
NaN
values. You can also specify "noCommunication"
as part of the function call.
The returned array cI
has the same underlying type, sparsity, and
complexity (real or complex) as p
.
Examples
Input Arguments
Version History
Introduced in R2006b