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Caches The principle that states that if data is used, its neighbor will likely be used soon.

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Presentation on theme: "Caches The principle that states that if data is used, its neighbor will likely be used soon."— Presentation transcript:

1 Caches The principle that states that if data is used, its neighbor will likely be used soon

2 Caches The principle that states that if data is used, its neighbor will likely be used soon Spatial Locality

3 Caches The time it takes a cache to receive the data from the lower level of memory

4 Caches The time it takes a cache to receive the data from the lower level of memory Miss Penalty

5 Caches The principle that states that if data is used, it will likely be used again soon

6 Caches The principle that states that if data is used, it will likely be used again soon Temporal Locality

7 Caches The time it takes to find out if an item is in the cache and return the data if it is.

8 Caches The time it takes to find out if an item is in the cache and return the data if it is. Access Time

9 Caches A cache configuration that requires multiple tag checks each access.

10 Caches A cache configuration that requires multiple tag checks each access. 2+ Associativity

11 Caches A cache attribute that takes advantage of spatial locality

12 Caches A cache attribute that takes advantage of spatial locality Large Block Size

13 Caches Cache attributes that decrease access time

14 Caches Cache attributes that decrease access time Small, Low Associativity

15 Caches Cache attributes that decrease miss penalty

16 Caches Cache attributes that decrease miss penalty Small Block Size, Multi-Level Caches

17 Caches Cache attributes that decrease miss rate

18 Caches Cache attributes that decrease miss rate Large Cachesize, Large Blocksize, High Associativity

19 Caches log 2 ( CacheSize / (BlockSize * Assoc)) =

20 Caches log 2 ( CacheSize / (BlockSize * Assoc)) # bits in the index

21 Caches log 2 ( BlockSize / WordSize ) =

22 Caches log 2 ( Blocksize / Wordsize ) = # bits in the Block Offset

23 Caches log 2 ( WordSize) =

24 Caches log 2 ( WordSize ) = # bits in the Byte Offset

25 Caches #address bits - log 2 ( CacheSize / Associativity ) =

26 Caches #address bits - log 2 ( CacheSize / Associativity ) = # bits in the Tag


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