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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]
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.
Redis is a source-available software project that implements data structure servers. It is networked, in-memory, and stores keys with optional durability. SafePeak: SafePeak Technologies Proprietary Automated In-Memory Dynamic Caching for SQL Server OLTP applications and databases. Code-free, Dynamic Caching, Relational SAP HANA: SAP SE: 2012 ...
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.
A CPU cache is a hardware cache used by the central processing unit (CPU) of a computer to reduce the average cost (time or energy) to access data from the main memory. [1] A cache is a smaller, faster memory, located closer to a processor core, which stores copies of the data from frequently used main memory locations.
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.
The most efficient caching algorithm would be to discard information which would not be needed for the longest time; this is known as Bélády's optimal algorithm, optimal replacement policy, or the clairvoyant algorithm. Since it is generally impossible to predict how far in the future information will be needed, this is unfeasible in practice.
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 ...