External memory algorithms optimize performance for large datasets from "summary" of Data Structures and Algorithms in Python by Michael T. Goodrich,Roberto Tamassia,Michael H. Goldwasser
External memory algorithms are designed to optimize performance for large datasets that cannot fit entirely in internal memory. These algorithms are crucial for handling massive amounts of data efficiently and effectively. In contrast to internal memory algorithms, which assume that all data fits in RAM, external memory algorithms take into account the limitations of external storage devices such as hard drives. The key idea behind external memory algorithms is to minimize the number of costly input/output operations between internal and external memory. Since accessing data from external storage is significantly slower than accessing data from internal memory, reducing the number of these operations is essential for improving performance. External memory algorithms achieve this by efficiently managing data transfers between internal and external memory. One common technique used in external memory algorithms is to read and write data in larger chunks rather than individual elements. By reading and writing data in larger blocks, external memory algorithms can reduce the number of input/output operations, thus improving performance. Another important strategy is to minimize the amount of data movement between internal and external memory, as moving large amounts of data can be time-consuming. In addition to optimizing data transfers, external memory algorithms often leverage data structures that are well-suited for external storage devices. For example, B-trees are commonly used in external memory algorithms due to their ability to efficiently store and access data on disk. By choosing appropriate data structures and algorithms, external memory algorithms can further enhance performance for large datasets.- External memory algorithms play a crucial role in managing and processing large amounts of data that exceed the capacity of internal memory. By optimizing data transfers, leveraging efficient data structures, and minimizing data movement, external memory algorithms enable efficient processing of massive datasets that would be impractical to handle with internal memory alone.
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