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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.
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.
Examples of coherency protocols for cache memory are listed here. For simplicity, all "miss" Read and Write status transactions which obviously come from state "I" (or miss of Tag), in the diagrams are not shown. They are shown directly on the new state. Many of the following protocols have only historical value.
Rarely, but especially in algorithms, coherence can instead refer to the locality of reference. Multiple copies of the same data can exist in different cache simultaneously and if processors are allowed to update their own copies freely, an inconsistent view of memory can result.
Automated In-Memory Dynamic Caching for SQL Server OLTP applications and databases. Code-free, Dynamic Caching, Relational SAP HANA: SAP SE: 2012 Proprietary SAP HANA, short for 'High Performance Analytic Appliance' is an in-memory, column-oriented, relational database management system written in C, C++: solidDB: Unicom Global 1992 Proprietary
Caching is an effective manner of improving performance in situations where the principle of locality of reference applies. The methods used to determine which data is stored in progressively faster storage are collectively called caching strategies. Examples are ASP.NET cache, CPU cache, etc.
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]