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Distributed parallel computing is designed to perform tasks by splitting them across multiple nodes (often in close geographical proximity), which can efficiently share resources and execute processes simultaneously. This approach is particularly effective for computationally intensive tasks that can be parallelized. In contrast, cloud computing encompasses a broader model where resources are not only shared among local nodes but can also be accessed from remote locations, allowing for dynamic scaling and flexibility. While both models involve distributed resources, distributed parallel computing typically emphasizes local resource sharing, whereas cloud computing prioritizes global access and on-demand resource provisioning. Why Other Options are Incorrect: A) Distributed parallel computing requires a centralized management system: This is not accurate; distributed parallel systems often have decentralized management structures. B) Cloud computing is inherently less scalable than distributed parallel computing: This is incorrect; cloud computing is generally highly scalable due to its ability to leverage vast resources from different locations. D) Cloud computing does not support resource pooling like distributed parallel computing: Cloud computing actively supports resource pooling, enabling efficient allocation across multiple users. E) Distributed parallel computing is limited to specific hardware configurations: This statement is misleading; while certain configurations may optimize performance, distributed parallel computing can operate across diverse hardware setups
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