codistributed.sprand - Create codistributed sparse array of uniformly distributed pseudo-random values - MATLAB (original) (raw)
Create codistributed sparse array of uniformly distributed pseudo-random values
Syntax
CS = codistributed.sprand(m,n,density) CS = sprand(n,codist) DS = distributed.sprand(___,typename)
Description
CS = codistributed.sprand(m,n,density) creates an m-by-n sparse codistributed array with approximately density*m*n uniformly distributed nonzero double entries.
Optional arguments to codistributed.sprand 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 = sprand(n,codist) is the same as CS = codistributed.sprand(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.sprand(8,8,0.2,codistributor1d); end
DS = distributed.sprand(___,typename) also specifies the data type (class) for any of the previous syntaxes. The typename input can be either "single" or "double". (since R2025a)
Examples
With four workers, create a 1000-by-1000 sparse codistributed double arrayCS with approximately 1000 nonzeros. CS is distributed by its second dimension (columns).
spmd(4) CS = codistributed.sprand(1000,1000,0.001); end
Create 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 CS.
spmd(4) codist = codistributor1d(2,1:spmdSize); CS = sprand(10,10,0.1,codist); end
Create a random 500-by-1000 sparse codistributed single-precision array with density 0.1.
spmd(5) CS = codistributed.sprand(500,1000,0.1,"single"); end
Tips
When you use sprand 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.
Version History
Introduced in R2009b
You can specify the output data type by specifying thetypename argument as "double" or"single".