Create codistributed sparse array of normally distributed pseudo-random values
CS = codistributed.sprandn(m,n,density)
CS = sprandn(n,codist)
CS = codistributed.sprandn(m,n,density) creates an
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
'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
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
spmd(4) codist = codistributor1d(2,1:spmdSize); 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-
spmdIndex local piece of
When you use
sprandn on the workers in the parallel pool, or in an independent or communicating job, each worker sets its random generator seed to a value that depends only on the
spmdIndex 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.
Introduced in R2009b