The way in which a matrix is distributed over the processes has a major impact on the load balance and communication characteristics of the concurrent algorithm, and hence largely determines its performance and scalability. The block cyclic distribution provides a simple, yet general-purpose way of distributing a block-partitioned matrix on distributed memory concurrent computers. It has been incorporated in the High Performance Fortran standard .
The block cyclic data distribution is parameterized by the four numbers , , , and , where is the process template and is the block size.
Suppose first that we have objects, indexed by an integer , to map onto processes, using block size . The -th item will be stored in the -th location of block on process , where
In the special case where and are powers of two, this mapping is really just bit extraction, with equal to the rightmost bits of , equal to the next bits of , and equal to the remaining leftmost bits of . The distribution of a block-partitioned matrix can be regarded as the tensor product of two such mappings: one that distributes the rows of the matrix over processes, and another that distributes the columns over processes. That is, the matrix element indexed globally by is stored in location
The nonscattered decomposition (or pure block distribution) is just the special case and . Similarly a purely scattered decomposition (or two dimensional wrapped distribution) is the special case .