A cache has proven to be extremely effective in many areas of computing because access patterns in typical computer applications have locality of reference. There are several kinds of locality, but this article primarily deals with data that are accessed close together in time (temporal locality). The data might or might not be located physically close to each other (spatial locality).
A cache is made up of a pool of entries. Each entry has a datum (a nugget of data) which is a copy of the datum in some backing store. Each entry also has a tag, which specifies the identity of the datum in the backing store of which the entry is a copy.
When the cache client (a CPU, web browser, operating system) wishes to access a datum presumably in the backing store, it first checks the cache. If an entry can be found with a tag matching that of the desired datum, the datum in the entry is used instead. This situation is known as a cache hit. So, for example, a web browser program might check its local cache on disk to see if it has a local copy of the contents of a web page at a particular URL. In this example, the URL is the tag, and the contents of the web page is the datum. The percentage of accesses that result in cache hits is known as the hit rate or hit ratio of the cache.
The alternative situation, when the cache is consulted and found not to contain a datum with the desired tag, is known as a cache miss. The previously uncached datum fetched from the backing store during miss handling is usually copied into the cache, ready for the next access.
During a cache miss, the CPU usually ejects some other entry in order to make room for the previously uncached datum. The heuristic used to select the entry to eject is known as the replacement policy. One popular replacement policy, least recently used (LRU), replaces the least recently used entry (see cache algorithms). More efficient caches compute use frequency against the size of the stored contents, as well as the latencies and throughputs for both the cache and the backing store. While this works well for larger amounts of data, long latencies, and slow throughputs, such as experienced with a hard drive and the Internet, it's not efficient to use this for cached main memory (RAM).
When a datum is written to the cache, it must at some point be written to the backing store as well. The timing of this write is controlled by what is known as the write policy.
In a write-through cache, every write to the cache causes a synchronous write to the backing store.
Alternatively, in a write-back (or write-behind) cache, writes are not immediately mirrored to the store. Instead, the cache tracks which of its locations have been written over (these locations are marked dirty). The data in these locations is written back to the backing store when those data are evicted from the cache, an effect referred to as a lazy write. For this reason, a miss in a write-back cache (which requires a block to be replaced by another) will often require two memory accesses to service: one to retrieve the needed datum, and one to write replaced data from the cache to the store.
Data write-back may be triggered by other policies as well. The client may make many changes to a datum in the cache, and then explicitly notify the cache to write back the datum.
No-write allocation is a cache policy where only processor reads are cached, thus avoiding the need for write-back or write-through when the old value of the datum was absent from the cache prior to the write.
The data in the backing store may be changed by entities other than the cache, in which case the copy in the cache may become out-of-date or stale. Alternatively, when the client updates the data in the cache, copies of that data in other caches will become stale. Communication protocols between the cache managers which keep the data consistent are known as coherency protocols.
Small memories on or close to the CPU chip can be made faster than the much larger main memory. Most CPUs since the 1980s have used one or more caches, and modern general-purpose CPUs inside personal computers may have as many as half a dozen, each specialized to a different part of the task of executing programs.
While CPU caches are generally managed entirely by hardware, other caches are managed by a variety of software. The page cache in main memory, which is an example of disk cache, is usually managed by the operating system kernel.
While the hard drive's hardware disk buffer is sometimes misleadingly referred to as "disk cache", its main functions are write sequencing and read prefetching. Repeated cache hits are relatively rare, due to the small size of the buffer in comparison to HDD's capacity.
In turn, fast local hard disk can be used to cache information held on even slower data storage devices, such as remote servers (web cache) or local tape drives or optical jukeboxes. Such a scheme is the main concept of hierarchical storage management.
Write-through operation is common when operating over unreliable networks (like an Ethernet LAN), because of the enormous complexity of the coherency protocol required between multiple write-back caches when communication is unreliable. For instance, web page caches and client-side network file system caches (like those in NFS or SMB) are typically read-only or write-through specifically to keep the network protocol simple and reliable.
A cache of recently visited web pages can be managed by your web browser. Some browsers are configured to use an external proxy web cache, a server program through which all web requests are routed so that it can cache frequently accessed pages for everyone in an organization. Many internet service providers use proxy caches to save bandwidth on frequently-accessed web pages.
Search engines also frequently make web pages they have indexed available from their cache. For example, Google provides a "Cached" link next to each search result. This is useful when web pages are temporarily inaccessible from a web server.
Another type of caching is storing computed results that will likely be needed again, or memoization. An example of this type of caching is ccache, a program that caches the output of the compilation to speed up the second-time compilation.
Database caching can substantially improve the throughput of database applications, for example in the processing of indexes, data dictionaries, and frequently used subsets of data. TimesTen provides a mid-tier caching facility that can be integrated into Oracle databases.
The terms are not mutually exclusive and the functions are frequently combined; however, there is a difference in intent. A buffer is a temporary memory location, that is traditionally used because CPU instructions cannot directly address data stored in peripheral devices. Thus, addressable memory is used as intermediate stage. Additionally such a buffer may be feasible when a large block of data is assembled or disassembled (as required by a storage device), or when data may be delivered in a different order than that in which it is produced. Also a whole buffer of data is usually transferred sequentially (for example to hard disk), so buffering itself sometimes increases transfer performance. These benefits are present even if the buffered data are written to the buffer once and read from the buffer once.
A cache also increases transfer performance. A part of the increase similarly comes from the possibility that multiple small transfers will combine into one large block. But the main performance gain occurs because there is a good chance that the same datum will be read from cache multiple times, or that written data will soon be read. Cache's sole purpose is to reduce accesses to the underlying slower storage. Cache is also usually an abstraction layer that is designed to be invisible from the perspective of neighboring layers.
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