 | This article needs additional citations for verification .(January 2021) |
Pseudo-LRU or PLRU is a family of cache algorithms which improve on the performance of the Least Recently Used (LRU) algorithm by replacing values using approximate measures of age rather than maintaining the exact age of every value in the cache.
PLRU usually refers to two cache replacement algorithms: tree-PLRU and bit-PLRU.
Tree-PLRU is an efficient algorithm to select an item that most likely has not been accessed very recently, given a set of items and a sequence of access events to the items.
This technique is used in the CPU cache of the Intel 486 and in many processors in the PowerPC family, such as Freescale's PowerPC G4 used by Apple Computer.
The algorithm works as follows: consider a binary search tree for the items in question. Each node of the tree has a one-bit flag denoting "go left to insert a pseudo-LRU element" or "go right to insert a pseudo-LRU element". To find a pseudo-LRU element, traverse the tree according to the values of the flags. To update the tree with an access to an item N, traverse the tree to find N and, during the traversal, set the node flags to denote the direction that is opposite to the direction taken.
This algorithm can be sub-optimal since it is an approximation. For example, in the above diagram with A, C, B, D cache lines, if the access pattern was: C, B, D, A, on an eviction, B would be chosen instead of C. This is because both A and C are in the same half and accessing A directs the algorithm to the other half that does not contain cache line C.
Bit-PLRU stores one status bit for each cache line. These bits are called MRU-bits. Every access to a line sets its MRU-bit to 1, indicating that the line was recently used. Whenever the last remaining 0 bit of a set's status bits is set to 1, all other bits are reset to 0. At cache misses, the leftmost line whose MRU-bit is 0 is replaced. [1]
In computer science, a B-tree is a self-balancing tree data structure that maintains sorted data and allows searches, sequential access, insertions, and deletions in logarithmic time. The B-tree generalizes the binary search tree, allowing for nodes with more than two children. Unlike other self-balancing binary search trees, the B-tree is well suited for storage systems that read and write relatively large blocks of data, such as databases and file systems.
In computer science, a data structure is a data organization, and storage format that is usually chosen for efficient access to data. More precisely, a data structure is a collection of data values, the relationships among them, and the functions or operations that can be applied to the data, i.e., it is an algebraic structure about data.
In computing, a hash table, also known as a hash map or a hash set, is a data structure that implements an associative array, also called a dictionary, which is an abstract data type that maps keys to values. A hash table uses a hash function to compute an index, also called a hash code, into an array of buckets or slots, from which the desired value can be found. During lookup, the key is hashed and the resulting hash indicates where the corresponding value is stored.
In computer science, a linked list is a linear collection of data elements whose order is not given by their physical placement in memory. Instead, each element points to the next. It is a data structure consisting of a collection of nodes which together represent a sequence. In its most basic form, each node contains data, and a reference to the next node in the sequence. This structure allows for efficient insertion or removal of elements from any position in the sequence during iteration. More complex variants add additional links, allowing more efficient insertion or removal of nodes at arbitrary positions. A drawback of linked lists is that data access time is linear in respect to the number of nodes in the list. Because nodes are serially linked, accessing any node requires that the prior node be accessed beforehand. Faster access, such as random access, is not feasible. Arrays have better cache locality compared to linked lists.
In computer science, a priority queue is an abstract data-type similar to a regular queue or stack data structure. Each element in a priority queue has an associated priority. In a priority queue, elements with high priority are served before elements with low priority. In some implementations, if two elements have the same priority, they are served in the same order in which they were enqueued. In other implementations, the order of elements with the same priority is undefined.
In computer science, a red–black tree is a specialised binary search tree data structure noted for fast storage and retrieval of ordered information, and a guarantee that operations will complete within a known time. Compared to other self-balancing binary search trees, the nodes in a red-black tree hold an extra bit called "color" representing "red" and "black" which is used when re-organising the tree to ensure that it is always approximately balanced.
A splay tree is a binary search tree with the additional property that recently accessed elements are quick to access again. Like self-balancing binary search trees, a splay tree performs basic operations such as insertion, look-up and removal in O(log n) amortized time. For random access patterns drawn from a non-uniform random distribution, their amortized time can be faster than logarithmic, proportional to the entropy of the access pattern. For many patterns of non-random operations, also, splay trees can take better than logarithmic time, without requiring advance knowledge of the pattern. According to the unproven dynamic optimality conjecture, their performance on all access patterns is within a constant factor of the best possible performance that could be achieved by any other self-adjusting binary search tree, even one selected to fit that pattern. The splay tree was invented by Daniel Sleator and Robert Tarjan in 1985.
In computer science, a trie, also called digital tree or prefix tree, is a type of k-ary search tree, a tree data structure used for locating specific keys from within a set. These keys are most often strings, with links between nodes defined not by the entire key, but by individual characters. In order to access a key, the trie is traversed depth-first, following the links between nodes, which represent each character in the key.
In computer science, the treap and the randomized binary search tree are two closely related forms of binary search tree data structures that maintain a dynamic set of ordered keys and allow binary searches among the keys. After any sequence of insertions and deletions of keys, the shape of the tree is a random variable with the same probability distribution as a random binary tree; in particular, with high probability its height is proportional to the logarithm of the number of keys, so that each search, insertion, or deletion operation takes logarithmic time to perform.
An XOR linked list is a type of data structure used in computer programming. It takes advantage of the bitwise XOR operation to decrease storage requirements for doubly linked lists by storing the composition of both addresses in one field. While the composed address is not meaningful on its own, during traversal it can be combined with knowledge of the last-visited node address to deduce the address of the following node.
The Lempel–Ziv–Markov chain algorithm (LZMA) is an algorithm used to perform lossless data compression. It has been under development since either 1996 or 1998 by Igor Pavlov and was first used in the 7z format of the 7-Zip archiver. This algorithm uses a dictionary compression scheme somewhat similar to the LZ77 algorithm published by Abraham Lempel and Jacob Ziv in 1977 and features a high compression ratio and a variable compression-dictionary size, while still maintaining decompression speed similar to other commonly used compression algorithms.
In a computer operating system that uses paging for virtual memory management, page replacement algorithms decide which memory pages to page out, sometimes called swap out, or write to disk, when a page of memory needs to be allocated. Page replacement happens when a requested page is not in memory and a free page cannot be used to satisfy the allocation, either because there are none, or because the number of free pages is lower than some threshold.
In computing, cache replacement policies 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 which are faster, or computationally cheaper to access, than normal memory stores. When the cache is full, the algorithm must choose which items to discard to make room for new data.
A B+ tree is an m-ary tree with a variable but often large number of children per node. A B+ tree consists of a root, internal nodes and leaves. The root may be either a leaf or a node with two or more children.
In computer science, a radix tree is a data structure that represents a space-optimized trie in which each node that is the only child is merged with its parent. The result is that the number of children of every internal node is at most the radix r of the radix tree, where r is a positive integer and a power x of 2, having x ≥ 1. Unlike regular trees, edges can be labeled with sequences of elements as well as single elements. This makes radix trees much more efficient for small sets and for sets of strings that share long prefixes.
In computer science, a leftist tree or leftist heap is a priority queue implemented with a variant of a binary heap. Every node x has an s-value which is the distance to the nearest leaf in subtree rooted at x. In contrast to a binary heap, a leftist tree attempts to be very unbalanced. In addition to the heap property, leftist trees are maintained so the right descendant of each node has the lower s-value.
In computer science, a linked data structure is a data structure which consists of a set of data records (nodes) linked together and organized by references. The link between data can also be called a connector.
In computer science, a queap is a priority queue data structure. The data structure allows insertions and deletions of arbitrary elements, as well as retrieval of the highest-priority element. Each deletion takes amortized time logarithmic in the number of items that have been in the structure for a longer time than the removed item. Insertions take constant amortized time.
In computer science, a fractal tree index is a tree data structure that keeps data sorted and allows searches and sequential access in the same time as a B-tree but with insertions and deletions that are asymptotically faster than a B-tree. Like a B-tree, a fractal tree index is a generalization of a binary search tree in that a node can have more than two children. Furthermore, unlike a B-tree, a fractal tree index has buffers at each node, which allow insertions, deletions and other changes to be stored in intermediate locations. The goal of the buffers is to schedule disk writes so that each write performs a large amount of useful work, thereby avoiding the worst-case performance of B-trees, in which each disk write may change a small amount of data on disk. Like a B-tree, fractal tree indexes are optimized for systems that read and write large blocks of data. The fractal tree index has been commercialized in databases by Tokutek. Originally, it was implemented as a cache-oblivious lookahead array, but the current implementation is an extension of the Bε tree. The Bε is related to the Buffered Repository Tree. The Buffered Repository Tree has degree 2, whereas the Bε tree has degree Bε. The fractal tree index has also been used in a prototype filesystem. An open source implementation of the fractal tree index is available, which demonstrates the implementation details outlined below.
In computing, cache algorithms are optimizing instructions—or algorithms—that a computer program or a hardware-maintained structure can follow in order to manage a cache of information stored on the computer. When the cache is full, the algorithm must choose which items to discard to make room for the new ones. Due to the inherent caching capability of nodes in Information-centric networking ICN, the ICN can be viewed as a loosely connect network of caches, which has unique requirements of Caching policies. Unlike proxy servers, in Information-centric networking the cache is a network level solution. Therefore, it has rapidly changing cache states and higher request arrival rates; moreover, smaller cache sizes further impose different kind of requirements on the content eviction policies. In particular, eviction policies for Information-centric networking should be fast and lightweight. Various cache replication and eviction schemes for different Information-centric networking architectures and applications are proposed.
 | This computer science article is a stub. You can help Wikipedia by expanding it. |