Description
In a multiprocessor system executing a single set of instructions (SIMD), data parallelism is achieved when each processor performs the same task on different pieces of distributed data. In some situations, a single execution thread controls operations on all pieces of data. In others, different threads control the operation, but they execute the same code.
For instance, consider a 2-processor system (CPUs A and B) in a parallel environment, and we wish to do a task on some data d. It is possible to tell CPU A to do that task on one part of d and CPU B on another part simultaneously, thereby reducing the duration of the execution. The data can be assigned using conditional statements as described below. As a specific example, consider adding two matrices. In a data parallel implementation, CPU A could add all elements from the top half of the matrices, while CPU B could add all elements from the bottom half of the matrices. Since the two processors work in parallel, the job of performing matrix addition would take one half the time of performing the same operation in serial using one CPU alone.
Data parallelism emphasizes the distributed (parallelized) nature of the data, as opposed to the processing (task parallelism). Most real programs fall somewhere on a continuum between task parallelism and data parallelism.
Read more about this topic: Data Parallelism
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