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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.
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 ...
Transactional memory model [7] is the combination of cache coherency and memory consistency models as a communication model for shared memory systems supported by software or hardware; a transactional memory model provides both memory consistency and cache coherency. A transaction is a sequence of operations executed by a process that ...
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 ...
Similar to the way in which the RAM model neglects practical issues, such as access time to cache memory versus main memory, the PRAM model neglects such issues as synchronization and communication, but provides any (problem-size-dependent) number of processors. Algorithm cost, for instance, is estimated using two parameters O(time) and O(time ...
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.
The memory model specifies synchronization barriers that are established via special, well-defined synchronization operations such as acquiring a lock by entering a synchronized block or method. The memory model stipulates that changes to the values of shared variables only need to be made visible to other threads when such a synchronization ...
Limited-memory BFGS (L-BFGS or LM-BFGS) is an optimization algorithm in the family of quasi-Newton methods that approximates the Broyden–Fletcher–Goldfarb–Shanno algorithm (BFGS) using a limited amount of computer memory. [1] It is a popular algorithm for parameter estimation in machine learning.