Out-of-core algorithm

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Out-of-core or external memory algorithms are algorithms that are designed to process data that is too large to fit into a computer's main memory at one time. Such algorithms must be optimized to efficiently fetch and access data stored in slow bulk memory (auxiliary memory) such as hard drives or tape drives.[1][2]

A typical example is geographic information systems, especially digital elevation models, where the full data set easily exceeds several gigabytes or even terabytes of data.

This notion naturally extends to a network connecting a data server to a treatment or visualization workstation. Popular mass-of-data based web applications such as Google Maps or Google Earth enter this topic.

This methodology extends beyond general purpose CPUs and also includes GPU computing as well as classical digital signal processing. In general-purpose computing on graphics processing units (GPGPU), powerful graphics cards (GPUs) with little memory (compared with the more familiar system memory, which is most often referred to simply as RAM) are utilized with relatively slow CPU to GPU memory transfer (when compared with computation bandwidth).

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References[edit]

  1. ^ Vitter, JS (2001). "External Memory Algorithms and Data Structures: Dealing with MASSIVE DATA". ACM Computing Surveys. 33 (2): 209–271. doi:10.1145/384192.384193. 
  2. ^ J. S. Vitter (2008). Algorithms and Data Structures for External Memory (PDF). Series on Foundations and Trends in Theoretical Computer Science. Hanover, MA: Now Publishers. doi:10.1561/0400000014. 

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