＆QUOT;数据位＆QUOT;容量VS＆QUOT;开销比特＆QUOT;尺寸？ [英] "data bit" capacity vs "overhead bit" size?

问题描述

我有点卡住，因为我无法找到任何东西覆盖缓存的数据的一部分，我用Google搜索交易99.9％，与缓存解决一切。有人问我这个问题的措辞这样

I am a little stuck because I cannot find anything which covers the "data" part of the cache, everything that I have googled deals 99.9% with the addressing of cache. The question I was asked is worded as such

Contrast the difference between "data bit" capacity and "overhead bit" size
for the two caches.

我不想要的答案，所以我不打算发布实际集大小并没有什么，我只是在寻找一种可能是网站或解释方向如何对比度两项。任何可能的帮助，很好AP preciated！

I don't want the answer so I am not going to post the actual set sizes and what not, I am just looking for a direction to maybe a website or an explanation of how to "contrast" the two. Any possible help is well appreciated!

推荐答案

我不知道你已经给了我们足够的上下文这个问题，但在这里不用。

I'm not sure you've given us enough context for this question, but here goes.

缓存必须存储不仅实际的缓存数据，也 - 对每一块的数据 - 的指数，它指的是。所以，当你查找记录N，缓存不仅要持有创纪录的N值，而且N - 这样你实际上可以查找数据。这就是看它一个pretty简单的方式。高速缓存可能有其他的元数据来表示有效性，并最后访问时间等。

Caches have to store not only the actual cached data, but also - for every piece of data - the "index" that it refers to. So when you lookup record N, the cache has to hold not only the value of record N, but also N - so that you can actually look up the data. And that's a pretty simplistic way of looking at it. Caches may have other metadata to indicate validity and last access time, etc.

示例1：字节的32位地址空间的缓存

每个高速缓存条目具有存储的数据值（8比特）加上地址（32位）= 40位，

Each cache entry has to store the data value (8 bits) plus the address (32bits) = 40 bits,

示例2：32位字的32位地址空间的缓存

每个高速缓存条目具有存储的数据值（32位）加的地址（32位）= 64位，

Each cache entry has to store the data value (32 bits) plus the address (32bits) = 64 bits,

您可以看到，例如＃1具有显著更高的开销。

You can see that example #1 has a significantly higher overhead.

与往常一样，维基百科可能会有帮助。 http://en.wikipedia.org/wiki/Cache_(computing ）

As always, Wikipedia may help. http://en.wikipedia.org/wiki/Cache_(computing)

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