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<a target="_blank" rel="noopener" href="https://fatcat.wiki/container/sv4mpg7lmfaqdp24ohp5qqiobm" style="color: black;">Parallel Computing</a>
Applications that explore, query, analyze, visualize, and, in general, process very large scale data sets are known as Data Intensive Applications. Large scale data intensive computing plays an increasingly important role in many scientific activities and commercial applications, whether it involves data mining of commercial transactions, experimental data analysis and visualization, or intensive simulation such as climate modeling. By combining high performance computation, very large data<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1016/s0167-8191(02)00091-1">doi:10.1016/s0167-8191(02)00091-1</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/qetjnt73xrbf3eil5p6suwx4ua">fatcat:qetjnt73xrbf3eil5p6suwx4ua</a> </span>
more »... age, high bandwidth access, and high-speed local and wide area networking, data intensive computing enhances the technical capabilities and usefulness of most systems. The integration of parallel and distributed computational environments will produce major improvements in performance for both computing intensive and data intensive applications in the future. The purpose of this introductory article is to provide an overview of the main issues in parallel data intensive computing in scientific and commercial applications and to encourage the reader to go into the more in-depth articles later in this special issue.
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