1. Hit or Miss ? !!!

2.  Small size.  Simple and fast.  Implementable with hardware.  Does not need too much power.  Does not predict miss if we have a hit.  Have a high hit rate especially on misses. Hit or Miss ? !!!

3.  Bloom filter: is a method representing a set of N elements ( a 1,…,a n ) to support membership queries.  The idea is to allocate a vector v of m bits, initially all set to 0.  Choose k independent hash functions, h 1,…, h k,each with range 1… m.  For each element a, the bits at positions h 1 ( a ),..., h k ( a ) in v are set to 1.

4.  Given a query for b we check the bits at positions h 1 ( b ), h 2 ( b ),..., h k ( b ).  If any of them is 0, then certainly b is not in the set A.  Otherwise we conjecture that b is in the set although there is a certain probability that we are wrong. This is called a “false positive”.  The parameters k and m should be chosen such that the probability of a false positive (and hence a false hit) is acceptable.

5.  We used a separate predictor for each set in the L2 cache. Set 0 Set 1 Set N Set 0 Set 1 Set N Set 0 Set 1 Set N Set 0 Set 1 Set N Array 0 Array 1 Array N 0 0 1 1 0 0 1 1 0 0 0 0 1 1 1 1 1 1 0 0 1 1 0 0 1 1 0 0 1 1 0 0 1 1 0 0

6. SSmall size. SSimple and fast. IImplementable with hardware. DDoes not need too much power. DDoes not predict miss if we have a hit.

7.  If A is a dynamic group, and in our case it is a dynamic one, it will be too hard to update the array when removing an element “e” from A, we can’t simply turn off Bloom[H(e)], to do so we must check that there is no element “e1” in A such that H(e)=H(e1). And this take a lot of time.   If we don’t update the array the hit rate will become low. 