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Prediction or explicit prefetching might also guess where future reads will come from and make requests ahead of time; if done correctly the latency is bypassed altogether.
The use of a cache also allows for higher throughput from the underlying resource, by assembling multiple fine grain transfers into larger, more efficient requests.
In the case of DRAM circuits, this might be served by having a wider data bus. Reading larger chunks reduces the fraction of bandwidth required for transmitting address information.
Hardware implements cache as a block of memory for temporary storage of data likely to be used again. A cache is made up of a pool of entries.
Each entry has associated data , which is a copy of the same data in some backing store. Each entry also has a tag , which specifies the identity of the data in the backing store of which the entry is a copy.
Tagging allows simultaneous cache-oriented algorithms to function in multilayered fashion without differential relay interference. When the cache client a CPU, web browser, operating system needs to access data 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 data, the data in the entry is used instead. This situation is known as a cache hit.
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 content of the web page is the data. 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 checked and found not to contain any entry with the desired tag, is known as a cache miss.
This requires a more expensive access of data from the backing store. Once the requested data is retrieved, it is typically copied into the cache, ready for the next access.
During a cache miss, some other previously existing cache entry is removed in order to make room for the newly retrieved data.
The heuristic used to select the entry to replace is known as the replacement policy. One popular replacement policy, "least recently used" LRU , replaces the oldest entry, the entry that was accessed less recently than any other entry see cache algorithm.
More efficient caching algorithms compute the use-hit 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 that experienced with hard drives and networks, but is not efficient for use within a CPU cache.
When a system writes data to cache, it must at some point write that data to the 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: . A 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 data.
Other policies may also trigger data write-back. The client may make many changes to data in the cache, and then explicitly notify the cache to write back the data.
Since no data is returned to the requester on write operations, a decision needs to be made on write misses, whether or not data would be loaded into the cache.
This is defined by these two approaches:. Both write-through and write-back policies can use either of these write-miss policies, but usually they are paired in this way: .
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. Small memories on or close to the CPU can operate faster than the much larger main memory.
At the moment, Microsoft does not have a way to clear the local cache in Microsoft Teams. At the moment, there is no way to clear the local cache directly from within the Microsoft Teams app until Microsoft pushes out an update.
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