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There are several physical and practical limits to the amount of computation or data storage that can be performed with a given amount of mass, volume, or energy:
Several methods have been proposed for producing computing devices or data storage devices that approach physical and practical limits:
Computer Science studies abstract models of computation, for which it considers various computational tasks and notions of solving them. For example, some tasks, such as undecidable problems, cannot be solved in principle. Others, NP-hard tasks cannot be solved quickly and accurately in all cases. Some tasks appear difficult to paralellize. These categories of tasks are called complexity classes and a large number of them have been classified and compared to each other. Some classes, defined in different ways, may coincide - providing a conclusive answer to such questions is a common pattern for unsolved challenges in Computer Science (see the P vs. NP challenge).
Many limits derived in terms of physical constants and abstract models of computation in Computer Science are loose.^{[5]} Very few known limits directly obstruct leading-edge technologies, but many engineering obstacles currently cannot be explained by closed-form limits.
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