Algorithms and Data Structures for External Memory

Key themes

Minimizing I/O Operations

This is the central overriding theme of the entire book. The core challenge addressed is that accessing data from external storage (like hard drives) is orders of magnitude slower than accessing internal memory. Therefore, all algorithmic designs and data structure adaptations presented in the book are geared towards reducing the number of input/output operations as much as possible, often by processing data in large blocks.

Locality of Reference and Block-wise Processing

A direct consequence of minimizing I/O, this theme emphasizes the importance of structuring data and algorithms to exploit spatial and temporal locality. By reading and writing data in large, contiguous blocks, the overhead per unit of data transferred is minimized. Algorithms are designed to perform as much useful work as possible on a block of data once it's brought into internal memory, before needing to access another block from external storage.

Scalability and Generality of Paradigms

The book not only provides specific algorithms but also general paradigms and techniques that can be applied across a wide range of computational problems. The emphasis is on designing solutions that scale effectively as the input size grows, often to sizes far exceeding available main memory. This theme highlights that the principles of EM algorithm design are universal and can be adapted to various problem domains, demonstrating the robustness and transferability of the core ideas.

Worth discussing

How does the external memory model fundamentally change the way we think about algorithm design compared to the RAM model?