Photo:1
Photo:2
Photo:3
Photo:4
Photo:1 Photo:2 Photo:3 Photo:4 |
| Operation | |
| 2>
Hardware implements cache as a block of memory for temporary storage of data likely to be used again. CPUs and hard drives frequently use a cache, as do web browsers and web servers.
A cache is made up of a pool of entries. Each entry has a datum (a nugget (piece) of data) - a copy of the same 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) needs to access a datum presumed to exist 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, has become 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 algorithm). 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. This works well for larger amounts of data, longer latencies and slower throughputs, such as experienced with a hard drive and the Internet, but is not efficient for use with a CPU cache.[citation needed]
[edit] Tags:Edit,Block,Cpus,Hard Drives,Datum,Heuristic,Replacement Policy,Cache Algorithm,Latencies,Cpu Cache,Cpu,Web Browsers,Web Servers,Web Server,Read,Contents, | |
| Writing policies | |
| 3>
A Write-Through cache with No-Write Allocation
A Write-Back cache with Write Allocation
When a system writes a datum to cache, it must at some point write that datum to backing store as well. The timing of this write is controlled by what is known as the write policy.
There are two basic writing approaches:
Write-through - Write is done synchronously both to the cache and to the backing store.
Write-back (or Write-behind) - Writing is done only to the cache. A modified cache block is written back to the store, just before it is replaced.
Write-back cache is more complex to implement, since it needs to track which of its locations have been written over, and mark them as dirty for later writing to the backing store. The data in these locations are written back to the backing store only when they are evicted from the cache, an effect referred to as a lazy write. For this reason, a read 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 write the replaced data from the cache back to the store, and then one to retrieve the needed datum.
Other policies may also trigger data write-back. The client may make many changes to a datum in the cache, and then explicitly notify the cache to write back the datum.
Since on write operations, no actual data are needed back, there are two approaches for situations of write-misses:
Write allocate (aka Fetch on write) - Datum at the missed-write location is loaded to cache, followed by a write-hit operation. In this approach, write misses are similar to read-misses.
No-write allocate (aka Write-no-allocate, Write around) - Datum at the missed-write location is not loaded to cache, and is written directly to the backing store. In this approach, actually only system reads are being cached.
Both write-through and write-back policies can use either of these write-miss policies, but usually they are paired in this way:
A write-back cache uses write allocate, hoping for a subsequent writes (or even reads) to the same location, which is now cached.
A write-through cache uses no-write allocate. Here, subsequent writes have no advantage, since they still need to be written directly to the backing store.
Entities other than the cache may change the data in the backing store, 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 those data in other caches will become stale. Communication protocols between the cache managers which keep the data consistent are known as coherency protocols.
[edit] Tags:Coherency Protocols,Coherency Protocol, | |
| CPU cache | |
| 3>
Main article: CPU cache
Small memories on or close to the CPU can operate faster than the much larger main memory. Most CPUs since the 1980s have used one or more caches, and modern high-end embedded, desktop and server microprocessors may have as many as half a dozen, each specialized for a specific function. Examples of caches with a specific function are the D-cache and I-cache (data cache and instruction cache).
[edit] Tags:Microprocessors,Main Memory,Article, | |
| Translation lookaside buffer | |
| 3>
Main article: Translation lookaside buffer
Memory management units that fetch page table entries from main memory have specialized caches that record the results of virtual address to physical address translation.
[edit] Tags:Translation Lookaside Buffer,Memory Management Units,Buffer, | |
| Disk cache | |
| 3>
Main article: Page cache
While CPU caches are generally managed entirely by hardware, a variety of software manages other caches. The page cache in main memory, which is an example of disk cache, is 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 the drive's capacity. However, high-end disk controllers often have their own on-board cache of hard disk data blocks.
Finally, fast local hard disk can also 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.
[edit] Tags:Page Cache,Kernel,Disk Buffer,Disk Controllers,Data Blocks,Web Cache,Tape Drives,Optical Jukeboxes,Hierarchical Storage Management, | |
| Web cache | |
| 3>
Main article: Web cache
Web browsers and web proxy servers employ web caches to store previous responses from web servers, such as web pages. Web caches reduce the amount of information that needs to be transmitted across the network, as information previously stored in the cache can often be re-used. This reduces bandwidth and processing requirements of the web server, and helps to improve responsiveness for users of the web.
Web browsers employ a built-in web cache, but some internet service providers or organizations also use a caching proxy server, which is a web cache that is shared among all users of that network.
Another form of cache is P2P caching, where the files most sought for by peer-to-peer applications are stored in an ISP cache to accelerate P2P transfers. Similarly, decentralised equivalents exist, which allow communities to perform the same task for P2P traffic, for example, Corelli. [2]
[edit] Tags:Web Proxy Servers,Web Pages,Responsiveness,Internet Service Providers,P2p Caching,Peer-to-peer,Isp,Help, | |
| Other caches | |
| 3>
The BIND DNS daemon caches a mapping of domain names to IP addresses, as does a resolver library.
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.
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 can prove useful when web pages from a web server are temporarily or permanently inaccessible.
Another type of caching is storing computed results that will likely be needed again, or memoization. ccache, a program that caches the output of the compilation to speed up the second-time compilation, exemplifies this type.
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.
Distributed caching[3] uses caches spread across different networked hosts, for example, Corelli
[edit] Tags:Dns,Ip Addresses,Client-side,Network File System,Nfs,Smb,Search Engines,Memoization,Ccache,Database Caching,Database,Indexes,Data Dictionaries,Distributed Caching, | |
| The difference between buffer and cache | |
| 3>
The terms "buffer" and "cache" 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 an 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 or reduces the variation or jitter of the transfer's latency as opposed to caching where the intent is to reduce the latency. 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. A 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.
[edit] Tags:Instructions,Abstraction Layer, | |
| Multi-ported cache | |
| 2>
A Multi-ported cache is a cache which can serve more than one request at a time. When referring to the traditional cache we normally use a specific memory address, while in the Multi-ported cache we may use N request at a time - where N is the number of ports that connected through the processor and the cache. The benefits the N>1 is that the pip-lined processor(such as MIPS) may access memory from different phase. Another benefit is that it allows the concept of super-scalar processor through different cache levels.
[edit] Tags: | |
| See also | |
| 2>
Cache algorithms
Cache coherence
Cache coloring
Cache-oblivious algorithm
Cache stampede
Cache language model
CPU cache
Database cache
Dirty bit
Disk buffer (Hardware-based cache)
Cache manifest in HTML5
Pipeline burst cache
[edit] Tags:Cache Algorithms,Cache Coherence,Cache Coloring,Cache-oblivious Algorithm,Cache Stampede,Cache Language Model,Database Cache,Dirty Bit,Cache Manifest In Html5,Pipeline Burst Cache, | |
| Further reading | |
| 2>
"What Every Programmer Should Know About Memory" by Ulrich Drepper
"Caching in the Distributed Environment"
[edit] Tags: | |
| References | |
| 2>
^ Definition |publisher=Merriam-Webster, Incorporated |work=Merriam-Webster Online Dictionary |accessdate=2 May 2011}}
^ Gareth Tyson, Andreas Mauthe, Sebastian Kaune, Mu Mu and Thomas Plagemann. Corelli: A Dynamic Replication Service for Supporting Latency-Dependent Content in Community Networks. In Proc. MMCN'09 [1]
^ Paul, S; Z Fei (2001-02-01). "Distributed caching with centralized control". Computer Communications 24 (2): 256–268. doi:10.1016/S0140-3664(00)00322-4.
Look up cache in Wiktionary, the free dictionary.
Retrieved from "http://en.wikipedia.org/w/index.php?title=Cache_(computing)&oldid=475030707"
Categories: Cache (computing)Hidden categories: Articles needing additional references from April 2011All articles needing additional referencesAll articles with unsourced statementsArticles with unsourced statements from May 2007
Personal tools
Log in / create account
Namespaces
Article
Talk
Variants
Views
Read
Edit
View history
Actions
Search
Navigation
Main page
Contents
Featured content
Current events
Random article
Donate to Wikipedia
Interaction
Help
About Wikipedia
Community portal
Recent changes
Contact Wikipedia
Toolbox
What links here
Related changes
Upload file
Special pages
Permanent link
Cite this page
Print/export
Create a bookDownload as PDFPrintable version
Languages
Afrikaans
Alemannisch
العربية
Bosanski
Български
Català
Česky
Dansk
Deutsch
Eesti
Ελληνικά
Esperanto
Euskara
فارسی
Français
Galego
한국어
Hrvatski
Magyar
Bahasa Indonesia
Interlingua
Íslenska
Italiano
עברית
Қазақша
Latviešu
मराठी
Bahasa Melayu
Nederlands
日本語
Олык Марий
Papiamentu
Polski
Português
Română
Русский
Shqip
Simple English
Slovenčina
Српски / Srpski
Basa Sunda
Suomi
Svenska
ไทย
Türkçe
Українська
اردو
中文
This page was last modified on 4 February 2012 at 22:17.
Text is available under the Creative Commons Attribution-ShareAlike License;
additional terms may apply.
See Terms of use for details.
Wikipedia® is a registered trademark of the Wikimedia Foundation, Inc., a non-profit organization.Contact us
Privacy policy
About Wikipedia
Disclaimers
Mobile view
if ( window.isMSIE55 ) fixalpha();
if ( window.mediaWiki ) {
mw.loader.load(["mediawiki.user", "mediawiki.util", "mediawiki.page.ready", "mediawiki.legacy.wikibits", "mediawiki.legacy.ajax", "mediawiki.legacy.mwsuggest", "ext.gadget.wmfFR2011Style", "ext.vector.collapsibleNav", "ext.vector.collapsibleTabs", "ext.vector.editWarning", "ext.vector.simpleSearch", "ext.UserBuckets", "ext.articleFeedback.startup", "ext.articleFeedbackv5.startup", "ext.markAsHelpful"]);
}
if ( window.mediaWiki ) {
mw.user.options.set({"ccmeonemails":0,"cols":80,"date":"default","diffonly":0,"disablemail":0,"disablesuggest":0,"editfont":"default","editondblclick":0,"editsection":1,"editsectiononrightclick":0,"enotifminoredits":0,"enotifrevealaddr":0,"enotifusertalkpages":1,"enotifwatchlistpages":0,"extendwatchlist":0,"externaldiff":0,"externaleditor":0,"fancysig":0,"forceeditsummary":0,"gender":"unknown","hideminor":0,"hidepatrolled":0,"highlightbroken":1,"imagesize":2,"justify":0,"math":1,"minordefault":0,"newpageshidepatrolled":0,"nocache":0,"noconvertlink":0,"norollbackdiff":0,"numberheadings":0,"previewonfirst":0,"previewontop":1,"quickbar":5,"rcdays":7,"rclimit":50,"rememberpassword":0,"rows":25,"searchlimit":20,"showhiddencats":false,"showjumplinks":1,"shownumberswatching":1,"showtoc":1,"showtoolbar":1,"skin":"vector","stubthreshold":0,"thumbsize":4,"underline":2,"uselivepreview":0,"usenewrc":0,"watchcreations":1,"watchdefault":0,"watchdeletion":0,"watchlistdays":3,"watchlisthideanons":0,
"watchlisthidebots":0,"watchlisthideliu":0,"watchlisthideminor":0,"watchlisthideown":0,"watchlisthidepatrolled":0,"watchmoves":0,"wllimit":250,"flaggedrevssimpleui":1,"flaggedrevsstable":0,"flaggedrevseditdiffs":true,"flaggedrevsviewdiffs":false,"vector-simplesearch":1,"useeditwarning":1,"vector-collapsiblenav":1,"usebetatoolbar":1,"usebetatoolbar-cgd":1,"wikilove-enabled":1,"variant":"en","language":"en","searchNs0":true,"searchNs1":false,"searchNs2":false,"searchNs3":false,"searchNs4":false,"searchNs5":false,"searchNs6":false,"searchNs7":false,"searchNs8":false,"searchNs9":false,"searchNs10":false,"searchNs11":false,"searchNs12":false,"searchNs13":false,"searchNs14":false,"searchNs15":false,"searchNs100":false,"searchNs101":false,"searchNs108":false,"searchNs109":false,"gadget-wmfFR2011Style":1});;mw.user.tokens.set({"editToken":"+\\","watchToken":false});;mw.loader.state({"user.options":"ready","user.tokens":"ready"});
/* cache key: enwiki:resourceloader:filter:minify-js:4:b41a86ec4e0fe8329bc3ce917e792339 */
}
Tags:/,Categories,Cache (computing),Articles Needing Additional References From April 2011,All Articles Needing Additional References,All Articles With Unsourced Statements,Articles With Unsourced Statements From May 2007,Talk, | |
zote monety |