External memory algorithms

External memory algorithms are a class of algorithms designed to optimize the processing of data that cannot fit into a computer's main memory (RAM) and instead must be managed using external storage, such as hard disks or solid-state drives. This scenario is common in applications involving large datasets, such as those found in data mining, database management, and scientific computing.

Cache-oblivious algorithms are designed to take advantage of the hierarchical memory structure of modern computer architectures without needing to know the specific parameters of that hierarchy, such as cache sizes and block sizes. In the case of distribution sorting, the goal is to sort a collection of data elements efficiently by leveraging these cache characteristics. ### Cache-Oblivious Distribution Sort Cache-oblivious distribution sort is a type of sorting algorithm that uses a distribution-based approach while being cache-efficient.

External memory graph traversal refers to techniques and algorithms designed for traversing and processing graphs that are too large to fit entirely in a computer's main memory (RAM). Given the growing size of data and the rise of big data applications, external memory algorithms have become increasingly important for efficiently handling large datasets stored on slower external memory devices, like hard drives or SSDs. ### Key Concepts 1.

External sorting is a technique used for sorting large amounts of data that cannot fit into the computer's main memory (RAM) at once. This is common in cases where datasets are larger than the available RAM, such as sorting files stored on disk, databases, or processing large data streams. ### Key Features and Concepts of External Sorting: 1. **External Storage**: External sorting typically involves data that resides on external storage devices, such as hard drives or SSDs, rather than being held in RAM.

Funnelsort is a comparison-based sorting algorithm that uses a data structure called a "funnel" to sort a list of elements. It is notable for its efficiency in certain scenarios, particularly when dealing with large datasets. ### Key Features of Funnelsort: 1. **Funnel Data Structure**: The algorithm utilizes a funnel, which can conceptually be thought of as a series of channels that direct incoming elements based on comparisons. The funnel structure allows the algorithm to efficiently merge elements as they are processed.