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In computer file systems, a cluster (sometimes also called allocation unit or block) is a unit of disk space allocation for files and directories.To reduce the overhead of managing on-disk data structures, the filesystem does not allocate individual disk sectors by default, but contiguous groups of sectors, called clusters.
The granular nature results in unused space, sometimes called slack space, for each file except for those that have the rare size that is a multiple of the granular allocation. [10] For a 512-byte allocation, the average unused space is 256 bytes. For 64 KB clusters, the average unused space is 32 KB. Generally, the allocation unit size is set ...
A block of data of size 2 (n+1) − 1 always has one sub-block of size 2 n aligned on 2 n bytes. This is how a dynamic allocator that has no knowledge of alignment, can be used to provide aligned buffers, at the price of a factor two in space loss.
Memory management (also dynamic memory management, dynamic storage allocation, or dynamic memory allocation) is a form of resource management applied to computer memory.The essential requirement of memory management is to provide ways to dynamically allocate portions of memory to programs at their request, and free it for reuse when no longer needed.
In computer science, manual memory management refers to the usage of manual instructions by the programmer to identify and deallocate unused objects, or garbage.Up until the mid-1990s, the majority of programming languages used in industry supported manual memory management, though garbage collection has existed since 1959, when it was introduced with Lisp.
Memory pools allow memory allocation with constant execution time. The memory release for thousands of objects in a pool is just one operation, not one by one if malloc is used to allocate memory for each object. Memory pools can be grouped in hierarchical tree structures, which is suitable for special programming structures like loops and ...
This work was completed in 1995 [9] and integrated into the ML Kit, a version of ML based on region allocation in place of garbage collection. This permitted a direct comparison between the two on medium-sized test programs, yielding widely varying results ("between 10 times faster and four times slower") depending on how "region-friendly" the ...
The original Java memory model developed in 1995, was widely perceived as broken, [1] preventing many runtime optimizations and not providing strong enough guarantees for code safety. It was updated through the Java Community Process, as Java Specification Request 133 (JSR-133), which took effect back in 2004, for Tiger (Java 5.0). [2] [3]