Parallel Computing, Data Parallelism
Data parallelism is a form of parallelization which relies on splitting the computation by subdividing data across multiple processors in parallel computing environments. A data parallel algorithm focuses on distributing the data across different parallel computing nodes, in contrast to task parallelism which aims at subdividing the operations to perform. In a multiprocessor system, data parallelism is achieved when each processor performs the same task on different pieces of distributed data.
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