Approximating the Optimal Replacement Algorithm Ben Juurlink

Plan Motivation LRU and OPT algorithms Related work TNRP algorithm TNRP tuning TNRP comparison with LRU and OPT

Motivation It takes millions of cycles to serve a page fault Small reduction of the miss rate will pay for the cost of advanced replacement algorithm

LRU Algorithm LRU: Replaces the page that hasnt been used for the longest time The optimal algorithm (OPT) evicts the page that will not be used for the longest time LRU approximates OPT At times LRU is far from optimal: –4 page frames –1,2,3,4,5,1,2,3,4,5,1,2,3,4,5,1,2,3,4,5… –LRUs miss rate: 100% –OPTs miss rate: 40% LRU-2: replaces the page whose second to last access is least recent

Related work: Fiat and Rosen Algorithm Dynamically build weighted access graph An edge is created the first time the two pages are requested in succession Each time the edge is traversed, the weight is decreases by a constant factor Forgetfulness: the weight of all edges multiplied by a constant factor every 10n pages accesses The page to be evicted on a page fault: the furthest page from the page just accessed Expensive algorithm

Time of Next Reference Predictor Three parameters recorded for each page: –TLAST(p) = time of last reference –STRIDE(p) = # of page accesses between the last and previous to last reference –State: Transient initially Steady when –STRIDE(p) – SD STRIDE curr (p) STRIDE(p) + SD (SD = Stride Deviation) –Time – consecutive accesses to one page replaced by one

Time of Next Reference Predictor On a page fault: –Replace a page with the largest expected time of next reference EXP-TNEXT(p) –EXP-TNEXT(t)= TLAST(p) + STRIDE(p) if State=Steady current_time + [current_time – TLAST(p)] if State=Transient

Time of Next Reference Predictor-Example Reference String: SD = 2 Time: Page: Stride: State: T T S T S T T

Time of Next Reference Predictor A correction is needed for EXP-TNEXT (p) –1,2,3,4,5,6,7,8,9,5,11,12,13,14,5,16,17,18,19,5,… –3 pages can be kept in memory –STRIDE(5)=5 –At time 16 13,14 and 5 are in memory EXP-TNEXT (5) = = 20 EXP-TNEXT (13) = 16 + (16 – 13) = 19 EXP-TNEXT (14) = 16 + (16 – 14) = 18 –Page 5 will be evicted

Time of Next Reference Predictor: EXP-TNEXT (p) - solution If the page hasnt been accessed before or the state is transient then –EXP-TNEXT (p) = current_time + TF * (current_time – TLAST(p)) –TF>=1 If the page hasnt been accessed within the expected time current_time > TLAST(p) + STRIDE(p) +SD, use the above formula for EXP-TNEXT(p) and return its state to Transient In our example, take TF=2 –EXP-TNEXT (5) = = 20 –EXP-TNEXT (13) = * (16 – 13) = 22 –EXP-TNEXT (14) = * (16 – 14) = will be evicted

Implementation Issues How to find a victim page –Priority queue (priority = EXP-TNEXT(p)) Takes O(log n) to find a victim page But it takes O(log n) to update the queue –Updates occur frequently –Scan all the paged in pages on a page fault

Experiments - Parameters Benchmarks from the SPEC 2000 suite Stride Deviation: –Investigate the distribution of |STRIDE curr – STRIDE prev | –The distribution depends on benchmark –SD of 5 captures over 60% of all stride values Time of next reference Factor –Check the miss rate for 1<= TF <= 3 with 4 and 8 page frames –Performance rather independent of the exact values 4 page frames, the difference is at most 5% 8 page frames, the difference is at most 0.8% For some benchmarks the miss rate decreases as TF increases For other, it decreases and then increases –Optimal value between 2 and 2.75

Experiments – Miss Rate Reduction TNRP incurs fewer misses than LRU for almost all benchmarks –4 page frames Improvement: Avg: 13.5%, range from 6% to 28.2% –8 page frames Improvement: Avg: 4%, 20.7 for one benchmark –16 page frames Improvement less than 1% for 7 out of 9 benchmarks Double the number of misses for 1 benchmark Performance improvement significant when # of pages is small –Works best in multi-tasking environment when the physical memory needs to be shared among several processes TNRP outperforms LRU-2 for most workloads and number of pages