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Flat memory model or linear memory model refers to a memory addressing paradigm in which "memory appears to the program as a single contiguous address space." [1] The CPU can directly (and linearly) address all of the available memory locations without having to resort to any sort of bank switching, memory segmentation or paging schemes.
On the x86-64 platform, a total of seven memory models exist, [7] as the majority of symbol references are only 32 bits wide, and if the addresses are known at link time (as opposed to position-independent code). This does not affect the pointers used, which are always flat 64-bit pointers, but only how values that have to be accessed via ...
The running time of an algorithm in the external memory model is defined by the number of reads and writes to memory required. [3] The model was introduced by Alok Aggarwal and Jeffrey Vitter in 1988. [4] The external memory model is related to the cache-oblivious model, but algorithms in the external memory model may know both the block size ...
In operating systems, memory management is the function responsible for managing the computer's primary memory. [1]: 105–208 The memory management function keeps track of the status of each memory location, either allocated or free. It determines how memory is allocated among competing processes, deciding which gets memory, when they receive ...
In computer science, region-based memory management is a type of memory management in which each allocated object is assigned to a region. A region, also called a zone , arena , area , or memory context , is a collection of allocated objects that can be efficiently reallocated or deallocated all at once.
A model describes how units of computations, memories, and communications are organized. [1] The computational complexity of an algorithm can be measured given a model of computation. Using a model allows studying the performance of algorithms independently of the variations that are specific to particular implementations and specific technology.
An example of a linear time series model is an autoregressive moving average model.Here the model for values {} in a time series can be written in the form = + + = + =. where again the quantities are random variables representing innovations which are new random effects that appear at a certain time but also affect values of at later times.
In 1974, Baddeley and Hitch [5] introduced and made popular the multicomponent model of working memory.This theory proposes a central executive that, among other things, is responsible for directing attention to relevant information, suppressing irrelevant information and inappropriate actions, and for coordinating cognitive processes when more than one task must be done at the same time.