TLB Hit Ratio Simulation

Problem

Simulate a fully associative TLB with LRU replacement and count hits and misses.

pages is the sequence of page references and tlb_size is the number of TLB entries. A reference that is present is a hit and refreshes the entry's recency; otherwise it is a miss and the least recently used entry is evicted.

Return [hits, misses]. A non-positive TLB size misses on every reference.

Hint

Store TLB entries from least to most recently used and trim the front on a miss.

Test cases

case 1

in: [[1,2,3,1,2],3]

expected: [2,3]

case 2

in: [[1,1,1],1]

expected: [2,1]

case 3

in: [[1,2,3,4],2]

expected: [0,4]

case 4

in: [[],3]

expected: [0,0]

case 5

in: [[5],1]

expected: [0,1]

Starter codePython

def tlb_hit_misses(pages, tlb_size):
    # Your code here
    pass

5 test cases · run your code against them in the editor