codistributed.sprandn

Create codistributed sparse array of uniformly distributed pseudo-random values

Syntax

CS = codistributed.sprandn(m,n,density)
CS = sprandn(n,codist)

Description

CS = codistributed.sprandn(m,n,density) creates an m-by-n sparse codistributed array with approximately density*m*n normally distributed nonzero double entries.

Optional arguments to codistributed.sprandn must be specified after the required arguments, and in the following order:

  • codist — A codistributor object specifying the distribution scheme of the resulting array. If omitted, the array is distributed using the default distribution scheme. For information on constructing codistributor objects, see the reference pages for codistributor1d and codistributor2dbc.

  • 'noCommunication' — Specifies that no interworker communication is to be performed when constructing the array, skipping some error checking steps.

CS = sprandn(n,codist) is the same as CS = codistributed.sprandn(n, codist). You can also use the optional arguments with this syntax. To use the default distribution scheme, specify a codistributor constructor without arguments. For example:

spmd
    CS = codistributed.sprandn(8,8,0.2,codistributor1d());
end

Examples

With four workers,

spmd(4)
    CS = codistributed.sprandn(1000,1000,0.001);
end

creates a 1000-by-1000 sparse codistributed double array CS with approximately 1000 nonzeros. CS is distributed by its second dimension (columns), and each worker contains a 1000-by-250 local piece of CS.

spmd(4)
    codist = codistributor1d(2,1:numlabs);
    CS = sprandn(10,10,0.1,codist);
end

creates a 10-by-10 codistributed double array CS with approximately 10 nonzeros. CS is distributed by its columns, and each worker contains a 10-by-labindex local piece of CS.

Tips

When you use sprandn on the workers in the parallel pool, or in an independent or communicating job (including pmode), each worker sets its random generator seed to a value that depends only on the labindex or task ID. Therefore, the array on each worker is unique for that job. However, if you repeat the job, you get the same random data.

See Also

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Introduced in R2009b

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