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In computer science, locality of reference, also known as the principle of locality, [1] is the tendency of a processor to access the same set of memory locations repetitively over a short period of time. [2] There are two basic types of reference locality – temporal and spatial locality.
The cache is divided into a Low Inter-reference Recency (LIR) and a High Inter-reference Recency (HIR) partition. The LIR partition is to store the most highly ranked pages (LIR pages) and the HIR partition is to store some of the other pages (HIR pages). The LIR partition holds the majority of the cache, and all LIR pages are resident in the ...
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.
Data layout is critical for correctly passing arrays between programs written in different programming languages. It is also important for performance when traversing an array because modern CPUs process sequential data more efficiently than nonsequential data. This is primarily due to CPU caching which exploits spatial locality of reference. [1]
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.
Set-associative cache is a trade-off between direct-mapped cache and fully associative cache. A set-associative cache can be imagined as a n × m matrix. The cache is divided into ‘n’ sets and each set contains ‘m’ cache lines. A memory block is first mapped onto a set and then placed into any cache line of the set.