What This Document Is
This document is a comprehensive survey of techniques used in designing and analyzing algorithms and data structures specifically for situations where data is too large to fit into a computer’s main memory. It delves into the field of “external memory” computing, focusing on optimizing performance when dealing with massive datasets stored on slower storage devices. The material explores how to minimize the impact of data transfer bottlenecks between fast processors and slower external storage.
Why This Document Matters
This resource is invaluable for computer science students and professionals tackling large-scale data processing challenges. It’s particularly relevant when working with applications like databases, data mining, scientific simulations, and any scenario involving datasets that exceed available RAM. Understanding these techniques can significantly improve the efficiency and scalability of your programs. It’s ideal for those seeking a deeper understanding of how to manage and process truly large amounts of information.
Topics Covered
* External Memory (EM) Paradigms for batched and online problems
* Sorting and related problems (permuting, Fast Fourier Transform)
* Disk striping and its optimization
* Distribution and merging techniques for independent disk usage
* Algorithms for matrix operations (multiplication, transposition)
* Geometric data processing (intersections, convex hulls)
* Graph algorithms (ranking, connectivity, sorting, pathfinding)
* Indexed data structures (extendible hashing, B-trees)
* Locality exploitation for reduced Input/Output (I/O) costs
What This Document Provides
* A detailed overview of the state-of-the-art in external memory algorithm design.
* Analysis of various approaches to minimize I/O communication costs.
* Categorization and subject descriptors for easy referencing within the broader field of computer science.
* Discussion of the trade-offs between different external memory techniques.
* Contextualization of the material within the broader landscape of hierarchical memory systems.
