external memory algorithm
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## External Memory Algorithms: Handling Datasets Too Big for RAM
When dealing with datasets that exceed the available RAM, standard in-memory algorithms become inefficient due to excessive swapping between RAM and disk, leading to significant performance degradation. This is where *external memory algorithms* (also known as *out-of-core algorithms*) come into play. They are designed to minimize disk I/O operations, which are orders of magnitude slower than RAM access, and thus provide a practical solution for processing massive datasets.
This tutorial will delve into the core principles of external memory algorithms, cover common techniques, and provide a practical code example (in Python) illustrating a fundamental external sorting algorithm.
**1. The I/O Model and Key Concepts**
The analysis and design of external memory algorithms often rely on the **I/O model** (also known as the Disk Access Model or DAM) which simplifies the analysis by focusing solely on the number of I/O operations. The model defines the following parameters:
* **N:** The number of elements in the input dataset.
* **M:** The size of the available RAM (in number of elements).
* **B:** The block size or page size (in number of elements) - the amount of data transferred in a single disk I/O operation.
* **Disk I/O:** Reading or writing a block of size *B* from/to disk.
The primary goal of external memory algorithms is to minimize the number of disk I/O operations, since that's the bottleneck. We often aim to achieve I/O complexity in terms of *Scanning* and *Sorting*:
* **Scanning (SCAN(N)):** Reading all *N* elements of the dataset sequentially from disk. This typically takes O(N/B) I/O operations.
* **Sorting (SORT(N)):** Sorting the *N* elements. This typically takes O((N/B) logsub(M/B)/sub (N/B)) I/O operations.
**Key Ideas for Designing External Memory Algorithms:**
* **Block-Awareness:** Algorithms are designed to work with blocks of data rather than individu ...
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Видео external memory algorithm канала CodeDash
## External Memory Algorithms: Handling Datasets Too Big for RAM
When dealing with datasets that exceed the available RAM, standard in-memory algorithms become inefficient due to excessive swapping between RAM and disk, leading to significant performance degradation. This is where *external memory algorithms* (also known as *out-of-core algorithms*) come into play. They are designed to minimize disk I/O operations, which are orders of magnitude slower than RAM access, and thus provide a practical solution for processing massive datasets.
This tutorial will delve into the core principles of external memory algorithms, cover common techniques, and provide a practical code example (in Python) illustrating a fundamental external sorting algorithm.
**1. The I/O Model and Key Concepts**
The analysis and design of external memory algorithms often rely on the **I/O model** (also known as the Disk Access Model or DAM) which simplifies the analysis by focusing solely on the number of I/O operations. The model defines the following parameters:
* **N:** The number of elements in the input dataset.
* **M:** The size of the available RAM (in number of elements).
* **B:** The block size or page size (in number of elements) - the amount of data transferred in a single disk I/O operation.
* **Disk I/O:** Reading or writing a block of size *B* from/to disk.
The primary goal of external memory algorithms is to minimize the number of disk I/O operations, since that's the bottleneck. We often aim to achieve I/O complexity in terms of *Scanning* and *Sorting*:
* **Scanning (SCAN(N)):** Reading all *N* elements of the dataset sequentially from disk. This typically takes O(N/B) I/O operations.
* **Sorting (SORT(N)):** Sorting the *N* elements. This typically takes O((N/B) logsub(M/B)/sub (N/B)) I/O operations.
**Key Ideas for Designing External Memory Algorithms:**
* **Block-Awareness:** Algorithms are designed to work with blocks of data rather than individu ...
#duplicatedetection #duplicatedetection #duplicatedetection
Видео external memory algorithm канала CodeDash
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