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Full Description
Data sets in large applications are often too massive to fit completely inside the computer's internal memory. The resulting input/output communication (or I/O) between fast internal memory and slower external memory (such as disks) can be a major performance bottleneck.
Algorithms and Data Structures for External Memory surveys the state of the art in the design and analysis of external memory (or EM) algorithms and data structures, where the goal is to exploit locality in order to reduce the I/O costs. A variety of EM paradigms are considered for solving batched and online problems efficiently in external memory.
The book describes several useful paradigms for the design and implementation of efficient EM algorithms and data structures. The problem domains considered include sorting, permuting, FFT, scientific computing, computational geometry, graphs, databases, geographic information systems, and text and string processing. It is an invaluable reference for anybody interested in, or conducting research in the design, analysis, and implementation of algorithms and data structures.
Contents
1: Introduction 2: Parallel Disk Model (PDM) 3: Fundamental I/O Operations and Bounds 4: Exploiting Locality and Load Balancing 5: External Sorting and Related Problems 6: Lower Bounds and I/O 7: Matrix and Grid Computations 8: Batched Problems in Computational Geometry 9: Batched Problems on Graphs 10: External Hashing for Online Dictionary Search 11: Multiway Tree Data Structures 12: Spatial Data Structures and Range Search 13: Dynamic and Kinetic Data Structures 14: String Processing 15: Compressed Data Structures 16: Dynamic Memory Allocation 17: External Memory Programming Environments. Conclusions. Notations and Acronyms. References
