Data parallelism is a form of parallelism that involves concurrent execution of the same task on multiple data elements. It can be applied to regular data structures like arrays and matrices by working on each element or subarray in parallel.
Data parallelism can improve the performance and scalability of data-intensive applications by exploiting the parallel processing capabilities of modern hardware, such as multicore CPUs and GPUs.
Data parallelism can be implemented using various techniques and tools, such as vector processors, array processors, SIMD instructions, OpenMP directives, MPI functions, CUDA kernels, and more.
Data parallelism can also be combined with other forms of parallelism, such as task parallelism and model parallelism, to achieve higher levels of efficiency and flexibility.
Data parallelism differs from task parallelism in that data parallelism focuses on distributing the data across different processing units, while task parallelism focuses on distributing the tasks across different processing units.
Data parallelism is more suitable for applications that have a high degree of data locality and uniformity, while task parallelism is more suitable for applications that have a high degree of task diversity and independence.