缓存算法 LRU MRU PLRU RR SLRU..... via wiki
Bélády's Algorithm
The most efficient caching algorithm would be to always discard the information that will not be needed for the longest time in the future. This optimal result is referred to as Bélády's optimal algorithm or the clairvoyant algorithm. Since it is generally impossible to predict how far in the future information will be needed, this is generally not implementable in practice. The practical minimum can be calculated only after experimentation, and one can compare the effectiveness of the actually chosen cache algorithm.
Least Recently Used (LRU)
Discards the least recently used items first. This algorithm requires keeping track of what was used when, which is expensive if one wants to make sure the algorithm always discards the least recently used item. General implementations of this technique require keeping "age bits" for cache-lines and track the "Least Recently Used" cache-line based on age-bits. In such an implementation, every time a cache-line is used, the age of all other cache-lines changes. LRU is actually a family of caching algorithms with members including 2Q by Theodore Johnson and Dennis Shasha,[3] and LRU/K by Pat O'Neil, Betty O'Neil and Gerhard Weikum.[4]
Most Recently Used (MRU)
Discards, in contrast to LRU, the most recently used items first. In findings presented at the 11th VLDB conference, Chou and DeWitt noted that "When a file is being repeatedly scanned in a [Looping Sequential] reference pattern, MRU is the best replacement algorithm."[5] Subsequently other researchers presenting at the 22nd VLDB conference noted that for random access patterns and repeated scans over large datasets (sometimes known as cyclic access patterns) MRU cache algorithms have more hits than LRU due to their tendency to retain older data.[6] MRU algorithms are most useful in situations where the older an item is, the more likely it is to be accessed.
Pseudo-LRU (PLRU)
For CPU caches with large associativity (generally >4 ways), the implementation cost of LRU becomes prohibitive. In many CPU caches, a scheme that almost always discards one of the least recently used items is sufficient. So many CPU designers choose a PLRU algorithm which only needs one bit per cache item to work.
PLRU typically has a slightly worse miss ratio, has a slightly better latency, and uses slightly less power than LRU.
Which memory locations can be cached by which cache locations
Random Replacement (RR)
Randomly selects a candidate item and discards it to make space when necessary. This algorithm does not require keeping any information about the access history. For its simplicity, it has been used in ARM processors.[7] It admits efficient stochastic simulation.[8]
Segmented LRU (SLRU)
An SLRU cache is divided into two segments, a probationary segment and a protected segment. Lines in each segment are ordered from the most to the least recently accessed. Data from misses is added to the cache at the most recently accessed end of the probationary segment. Hits are removed from wherever they currently reside and added to the most recently accessed end of the protected segment. Lines in the protected segment have thus been accessed at least twice. The protected segment is finite, so migration of a line from the probationary segment to the protected segment may force the migration of the LRU line in the protected segment to the most recently used (MRU) end of the probationary segment, giving this line another chance to be accessed before being replaced. The size limit on the protected segment is an SLRU parameter that varies according to the I/O workload patterns. Whenever data must be discarded from the cache, lines are obtained from the LRU end of the probationary segment.[9]"
2-way set associative
Used for high-speed CPU caches where even PLRU is too slow. The address of a new item is used to calculate one of two possible locations in the cache where it is allowed to go. The LRU of the two is discarded. This requires one bit per pair of cache lines, to indicate which of the two was the least recently used.
Direct-mapped cache
Used for the highest-speed CPU caches where even 2-way set associative caches are too slow. The address of the new item is used to calculate the one location in the cache where it is allowed to go. Whatever was there before is discarded.
Least-Frequently Used (LFU)
Counts how often an item is needed. Those that are used least often are discarded first.
Low Inter-reference Recency Set (LIRS)
A page replacement algorithm with an improved performance over LRU and many other newer replacement algorithms. This is achieved by using reuse distance as a metric for dynamically ranking accessed pages to make a replacement decision. The algorithm was developed by Song Jiang and Xiaodong Zhang.
Adaptive Replacement Cache (ARC)
Constantly balances between LRU and LFU, to improve the combined result.[10] ARC improves on SLRU by using information about recently-evicted cache items to dynamically adjust the size of the protected segment and the probationary segment to make the best use of the available cache space.
Clock with Adaptive Replacement (CAR)
Combines Adaptive Replacement Cache (ARC) and CLOCK. CAR has performance comparable to ARC, and substantially outperforms both LRU and CLOCK. Like ARC, CAR is self-tuning and requires no user-specified magic parameters.
Multi Queue (MQ)
By Zhou, Philbin, and Li.[11]
The MQ cache contains multiple LRU queues, Q0, Q1, ..., Qm-1. Blocks stay in the LRU queues for a given lifetime, which is defined dynamically by the MQ algorithm to be the maximum temporal distance between two accesses to the same file or the number of cache blocks, whichever is larger. If a block has not been referenced within its lifetime, it is demoted from to Qi to Qi−1 or evicted from the cache if it is in Q0. Each queue also has a maximum access count; if a block in queue Qi is accessed more than 2i times, this block is promoted to Qi+1 until it is accessed more than 2i+1 times or its lifetime expires. Within a given queue, blocks are ranked by the recency of access, according to LRU.[12]
Other things to consider:
Items with different cost: keep items that are expensive to obtain, e.g. those that take a long time to get.
Items taking up more cache: If items have different sizes, the cache may want to discard a large item to store several smaller ones.
Items that expire with time: Some caches keep information that expires (e.g. a news cache, a DNS cache, or a web browser cache). The computer may discard items because they are expired. Depending on the size of the cache no further caching algorithm to discard items may be necessary.
Various algorithms also exist to maintain cache coherency. This applies only to situation where multiple independent caches are used for the same data (for example multiple database servers updating the single shared data file).
LRU 算法 Least Recently Used, a cache algorithm
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Cache-oblivious algorithm
Locality of reference
Distributed cache
