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Paging obviously benefits from temporal and spatial locality. A cache is a simple example of exploiting temporal locality, because it is a specially designed, faster but smaller memory area, generally used to keep recently referenced data and data near recently referenced data, which can lead to potential performance increases.
LIRS organizes metadata of cached pages and some uncached pages and conducts its replacement operations described as below, which are also illustrated with an example [3] in the graph. Replacement operations of LIRS. The cache is divided into a Low Inter-reference Recency (LIR) and a High Inter-reference Recency (HIR) partition.
In computing, a memory access pattern or IO access pattern is the pattern with which a system or program reads and writes memory on secondary storage.These patterns differ in the level of locality of reference and drastically affect cache performance, [1] and also have implications for the approach to parallelism [2] [3] and distribution of workload in shared memory systems. [4]
Most modern CPUs are so fast that for most program workloads, the bottleneck is the locality of reference of memory accesses and the efficiency of the caching and memory transfer between different levels of the hierarchy [citation needed]. As a result, the CPU spends much of its time idling, waiting for memory I/O to complete.
A major problem with this design is poor cache locality caused by the hash function. Tree-based designs avoid this by placing the page table entries for adjacent pages in adjacent locations, but an inverted page table destroys spatial locality of reference by scattering entries all over. An operating system may minimize the size of the hash ...
Cache coherence is the discipline which ensures that the changes in the values of shared operands (data) are propagated throughout the system in a timely fashion. [2] The following are the requirements for cache coherence: [3] Write Propagation Changes to the data in any cache must be propagated to other copies (of that cache line) in the peer ...
In computing, cache replacement policies (also known as cache replacement algorithms or cache algorithms) are optimizing instructions or algorithms which a computer program or hardware-maintained structure can utilize to manage a cache of information. Caching improves performance by keeping recent or often-used data items in memory locations ...
NUMA is beneficial for workloads with high memory locality of reference and low lock contention, because a processor may operate on a subset of memory mostly or entirely within its own cache node, reducing traffic on the memory bus. [2] NUMA architectures logically follow in scaling from symmetric multiprocessing (SMP) architectures.