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Analysis of branch misses in Quicksort

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These are the slides of my talk at the Meeting on Analytic Algorithmics and Combinatorics 2015 (ANALCO15) on branch mispredictions in classic Quicksort and Yaroslavskiy's dual-pivot Quicksort used in Java 7.

The talk is based on joint work with Conrado Martínez and Markus E. Nebel.

Find more information and the corresponding paper on my website: http://wwwagak.cs.uni-kl.de/sebastian-wild.html

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Analysis of branch misses in Quicksort

  1. 1. Analysis of Branch Misses in Quicksort Sebastian Wild wild@cs.uni-kl.de based on joint work with Conrado Martínez and Markus E. Nebel 04 January 2015 Meeting on Analytic Algorithmics and Combinatorics Sebastian Wild Branch Misses in Quicksort 2015-01-04 1 / 15
  2. 2. Instruction Pipelines Computers do not execute instructions fully sequentially Instead they use an “assembly line” Example: 42 43 44 45 46 47 41 48 ... i := i + 1 a := A[i] IF a < p GOTO 42 j := j - 1 a := A[j] IF a > p GOTO 45 ... each instruction broken in 4 stages simpler steps shorter CPU cycles one instruction per cycle finished ... ... except for branches! 1 undo wrong instructions 2 fill pipeline anew Pipeline stalls are costly ... can we avoid (some of) them? Sebastian Wild Branch Misses in Quicksort 2015-01-04 2 / 15
  3. 3. Instruction Pipelines Computers do not execute instructions fully sequentially Instead they use an “assembly line” Example: 42 43 44 45 46 47 41 48 ... i := i + 1 a := A[i] IF a < p GOTO 42 j := j - 1 a := A[j] IF a > p GOTO 45 ... each instruction broken in 4 stages simpler steps shorter CPU cycles one instruction per cycle finished ... ... except for branches! 1 undo wrong instructions 2 fill pipeline anew Pipeline stalls are costly ... can we avoid (some of) them? Sebastian Wild Branch Misses in Quicksort 2015-01-04 2 / 15
  4. 4. Instruction Pipelines Computers do not execute instructions fully sequentially Instead they use an “assembly line” Example: 42 43 44 45 46 47 41 48 ... i := i + 1 a := A[i] IF a < p GOTO 42 j := j - 1 a := A[j] IF a > p GOTO 45 ... each instruction broken in 4 stages simpler steps shorter CPU cycles one instruction per cycle finished ... ... except for branches! 1 undo wrong instructions 2 fill pipeline anew Pipeline stalls are costly ... can we avoid (some of) them? Sebastian Wild Branch Misses in Quicksort 2015-01-04 2 / 15
  5. 5. Instruction Pipelines Computers do not execute instructions fully sequentially Instead they use an “assembly line” Example: 42 43 44 45 46 47 41 48 ... i := i + 1 a := A[i] IF a < p GOTO 42 j := j - 1 a := A[j] IF a > p GOTO 45 ... each instruction broken in 4 stages simpler steps shorter CPU cycles one instruction per cycle finished ... ... except for branches! 1 undo wrong instructions 2 fill pipeline anew Pipeline stalls are costly ... can we avoid (some of) them? Sebastian Wild Branch Misses in Quicksort 2015-01-04 2 / 15
  6. 6. Instruction Pipelines Computers do not execute instructions fully sequentially Instead they use an “assembly line” Example: 42 43 44 45 46 47 41 48 ... i := i + 1 a := A[i] IF a < p GOTO 42 j := j - 1 a := A[j] IF a > p GOTO 45 ... each instruction broken in 4 stages simpler steps shorter CPU cycles one instruction per cycle finished ... ... except for branches! 1 undo wrong instructions 2 fill pipeline anew Pipeline stalls are costly ... can we avoid (some of) them? Sebastian Wild Branch Misses in Quicksort 2015-01-04 2 / 15
  7. 7. Instruction Pipelines Computers do not execute instructions fully sequentially Instead they use an “assembly line” Example: 42 43 44 45 46 47 41 48 ... i := i + 1 a := A[i] IF a < p GOTO 42 j := j - 1 a := A[j] IF a > p GOTO 45 ... each instruction broken in 4 stages simpler steps shorter CPU cycles one instruction per cycle finished ... ... except for branches! 1 undo wrong instructions 2 fill pipeline anew Pipeline stalls are costly ... can we avoid (some of) them? Sebastian Wild Branch Misses in Quicksort 2015-01-04 2 / 15
  8. 8. Instruction Pipelines Computers do not execute instructions fully sequentially Instead they use an “assembly line” Example: 42 43 44 45 46 47 41 48 ... i := i + 1 a := A[i] IF a < p GOTO 42 j := j - 1 a := A[j] IF a > p GOTO 45 ... each instruction broken in 4 stages simpler steps shorter CPU cycles one instruction per cycle finished ... ... except for branches! 1 undo wrong instructions 2 fill pipeline anew Pipeline stalls are costly ... can we avoid (some of) them? Sebastian Wild Branch Misses in Quicksort 2015-01-04 2 / 15
  9. 9. Instruction Pipelines Computers do not execute instructions fully sequentially Instead they use an “assembly line” Example: 42 43 44 45 46 47 41 48 ... i := i + 1 a := A[i] IF a < p GOTO 42 j := j - 1 a := A[j] IF a > p GOTO 45 ... each instruction broken in 4 stages simpler steps shorter CPU cycles one instruction per cycle finished ... ... except for branches! 1 undo wrong instructions 2 fill pipeline anew Pipeline stalls are costly ... can we avoid (some of) them? Sebastian Wild Branch Misses in Quicksort 2015-01-04 2 / 15
  10. 10. Instruction Pipelines Computers do not execute instructions fully sequentially Instead they use an “assembly line” Example: 42 43 44 45 46 47 41 48 ... i := i + 1 a := A[i] IF a < p GOTO 42 j := j - 1 a := A[j] IF a > p GOTO 45 ... each instruction broken in 4 stages simpler steps shorter CPU cycles one instruction per cycle finished ... ... except for branches! 1 undo wrong instructions 2 fill pipeline anew Pipeline stalls are costly ... can we avoid (some of) them? Sebastian Wild Branch Misses in Quicksort 2015-01-04 2 / 15
  11. 11. Instruction Pipelines Computers do not execute instructions fully sequentially Instead they use an “assembly line” Example: 42 43 44 45 46 47 41 48 ... i := i + 1 a := A[i] IF a < p GOTO 42 j := j - 1 a := A[j] IF a > p GOTO 45 ... each instruction broken in 4 stages simpler steps shorter CPU cycles one instruction per cycle finished ... ... except for branches! 1 undo wrong instructions 2 fill pipeline anew Pipeline stalls are costly ... can we avoid (some of) them? Sebastian Wild Branch Misses in Quicksort 2015-01-04 2 / 15
  12. 12. Instruction Pipelines Computers do not execute instructions fully sequentially Instead they use an “assembly line” Example: 42 43 44 45 46 47 41 48 ... i := i + 1 a := A[i] IF a < p GOTO 42 j := j - 1 a := A[j] IF a > p GOTO 45 ... each instruction broken in 4 stages simpler steps shorter CPU cycles one instruction per cycle finished ... ... except for branches! 1 undo wrong instructions 2 fill pipeline anew Pipeline stalls are costly ... can we avoid (some of) them? Sebastian Wild Branch Misses in Quicksort 2015-01-04 2 / 15
  13. 13. Instruction Pipelines Computers do not execute instructions fully sequentially Instead they use an “assembly line” Example: 42 43 44 45 46 47 41 48 ... i := i + 1 a := A[i] IF a < p GOTO 42 j := j - 1 a := A[j] IF a > p GOTO 45 ... each instruction broken in 4 stages simpler steps shorter CPU cycles one instruction per cycle finished ... ... except for branches! 1 undo wrong instructions 2 fill pipeline anew Pipeline stalls are costly ... can we avoid (some of) them? Sebastian Wild Branch Misses in Quicksort 2015-01-04 2 / 15
  14. 14. Instruction Pipelines Computers do not execute instructions fully sequentially Instead they use an “assembly line” Example: 42 43 44 45 46 47 41 48 ... i := i + 1 a := A[i] IF a < p GOTO 42 j := j - 1 a := A[j] IF a > p GOTO 45 ... each instruction broken in 4 stages simpler steps shorter CPU cycles one instruction per cycle finished ... ... except for branches! 1 undo wrong instructions 2 fill pipeline anew Pipeline stalls are costly ... can we avoid (some of) them? Sebastian Wild Branch Misses in Quicksort 2015-01-04 2 / 15
  15. 15. Instruction Pipelines Computers do not execute instructions fully sequentially Instead they use an “assembly line” Example: 42 43 44 45 46 47 41 48 ... i := i + 1 a := A[i] IF a < p GOTO 42 j := j - 1 a := A[j] IF a > p GOTO 45 ... each instruction broken in 4 stages simpler steps shorter CPU cycles one instruction per cycle finished ... ... except for branches! 1 undo wrong instructions 2 fill pipeline anew Pipeline stalls are costly ... can we avoid (some of) them? Sebastian Wild Branch Misses in Quicksort 2015-01-04 2 / 15
  16. 16. Instruction Pipelines Computers do not execute instructions fully sequentially Instead they use an “assembly line” Example: 42 43 44 45 46 47 41 48 ... i := i + 1 a := A[i] IF a < p GOTO 42 j := j - 1 a := A[j] IF a > p GOTO 45 ... each instruction broken in 4 stages simpler steps shorter CPU cycles one instruction per cycle finished ... ... except for branches! 1 undo wrong instructions 2 fill pipeline anew Pipeline stalls are costly ... can we avoid (some of) them? Sebastian Wild Branch Misses in Quicksort 2015-01-04 2 / 15
  17. 17. Instruction Pipelines Computers do not execute instructions fully sequentially Instead they use an “assembly line” Example: 42 43 44 45 46 47 41 48 ... i := i + 1 a := A[i] IF a < p GOTO 42 j := j - 1 a := A[j] IF a > p GOTO 45 ... each instruction broken in 4 stages simpler steps shorter CPU cycles one instruction per cycle finished ... ... except for branches! 1 undo wrong instructions 2 fill pipeline anew Pipeline stalls are costly ... can we avoid (some of) them? Sebastian Wild Branch Misses in Quicksort 2015-01-04 2 / 15
  18. 18. Instruction Pipelines Computers do not execute instructions fully sequentially Instead they use an “assembly line” Example: 42 43 44 45 46 47 41 48 ... i := i + 1 a := A[i] IF a < p GOTO 42 j := j - 1 a := A[j] IF a > p GOTO 45 ... each instruction broken in 4 stages simpler steps shorter CPU cycles one instruction per cycle finished ... ... except for branches! 1 undo wrong instructions 2 fill pipeline anew Pipeline stalls are costly ... can we avoid (some of) them? Sebastian Wild Branch Misses in Quicksort 2015-01-04 2 / 15
  19. 19. Branch Prediction We could avoid stalls if we knew whether a branch will be taken or not in general not possible prediction with heuristics: Predict same outcome as last time. (1-bit predictor) 1 2 predict taken predict not taken taken not t. not t. taken Predict most frequent outcome with finite memory (2-bit saturating counter) 1 2 3 4 predict taken predict not taken taken not t. not t. not t. not t. takentakentaken Flip prediction only after two consecutive errors (2-bit flip-consecutive) predicttaken predictnottaken 1 2 3 4 taken not t. taken not t. not t. taken not t. taken wilder heuristics exist out there ... not considered here prediction can be wrong branch miss (BM) Sebastian Wild Branch Misses in Quicksort 2015-01-04 3 / 15
  20. 20. Branch Prediction We could avoid stalls if we knew whether a branch will be taken or not in general not possible prediction with heuristics: Predict same outcome as last time. (1-bit predictor) 1 2 predict taken predict not taken taken not t. not t. taken Predict most frequent outcome with finite memory (2-bit saturating counter) 1 2 3 4 predict taken predict not taken taken not t. not t. not t. not t. takentakentaken Flip prediction only after two consecutive errors (2-bit flip-consecutive) predicttaken predictnottaken 1 2 3 4 taken not t. taken not t. not t. taken not t. taken wilder heuristics exist out there ... not considered here prediction can be wrong branch miss (BM) Sebastian Wild Branch Misses in Quicksort 2015-01-04 3 / 15
  21. 21. Branch Prediction We could avoid stalls if we knew whether a branch will be taken or not in general not possible prediction with heuristics: Predict same outcome as last time. (1-bit predictor) 1 2 predict taken predict not taken taken not t. not t. taken Predict most frequent outcome with finite memory (2-bit saturating counter) 1 2 3 4 predict taken predict not taken taken not t. not t. not t. not t. takentakentaken Flip prediction only after two consecutive errors (2-bit flip-consecutive) predicttaken predictnottaken 1 2 3 4 taken not t. taken not t. not t. taken not t. taken wilder heuristics exist out there ... not considered here prediction can be wrong branch miss (BM) Sebastian Wild Branch Misses in Quicksort 2015-01-04 3 / 15
  22. 22. Branch Prediction We could avoid stalls if we knew whether a branch will be taken or not in general not possible prediction with heuristics: Predict same outcome as last time. (1-bit predictor) 1 2 predict taken predict not taken taken not t. not t. taken Predict most frequent outcome with finite memory (2-bit saturating counter) 1 2 3 4 predict taken predict not taken taken not t. not t. not t. not t. takentakentaken Flip prediction only after two consecutive errors (2-bit flip-consecutive) predicttaken predictnottaken 1 2 3 4 taken not t. taken not t. not t. taken not t. taken wilder heuristics exist out there ... not considered here prediction can be wrong branch miss (BM) Sebastian Wild Branch Misses in Quicksort 2015-01-04 3 / 15
  23. 23. Branch Prediction We could avoid stalls if we knew whether a branch will be taken or not in general not possible prediction with heuristics: Predict same outcome as last time. (1-bit predictor) 1 2 predict taken predict not taken taken not t. not t. taken Predict most frequent outcome with finite memory (2-bit saturating counter) 1 2 3 4 predict taken predict not taken taken not t. not t. not t. not t. takentakentaken Flip prediction only after two consecutive errors (2-bit flip-consecutive) predicttaken predictnottaken 1 2 3 4 taken not t. taken not t. not t. taken not t. taken wilder heuristics exist out there ... not considered here prediction can be wrong branch miss (BM) Sebastian Wild Branch Misses in Quicksort 2015-01-04 3 / 15
  24. 24. Branch Prediction We could avoid stalls if we knew whether a branch will be taken or not in general not possible prediction with heuristics: Predict same outcome as last time. (1-bit predictor) 1 2 predict taken predict not taken taken not t. not t. taken Predict most frequent outcome with finite memory (2-bit saturating counter) 1 2 3 4 predict taken predict not taken taken not t. not t. not t. not t. takentakentaken Flip prediction only after two consecutive errors (2-bit flip-consecutive) predicttaken predictnottaken 1 2 3 4 taken not t. taken not t. not t. taken not t. taken wilder heuristics exist out there ... not considered here prediction can be wrong branch miss (BM) Sebastian Wild Branch Misses in Quicksort 2015-01-04 3 / 15
  25. 25. Why Should We Care? misprediction rates of “typical” programs < 10% (Comparison-based) sorting is different! Branch based on comparison result Comparisons reduce entropy (uncertainty about input) The less comparisons we use, the less predictable they become for classic Quicksort: misprediction rate 25 % with median-of-3: 31.25 % Practical Importance (KALIGOSI & SANDERS, ESA 2006): on Pentium 4 Prescott: very skewed pivot faster than median branch misses dominated running time Sebastian Wild Branch Misses in Quicksort 2015-01-04 4 / 15
  26. 26. Why Should We Care? misprediction rates of “typical” programs < 10% (Comparison-based) sorting is different! Branch based on comparison result Comparisons reduce entropy (uncertainty about input) The less comparisons we use, the less predictable they become for classic Quicksort: misprediction rate 25 % with median-of-3: 31.25 % Practical Importance (KALIGOSI & SANDERS, ESA 2006): on Pentium 4 Prescott: very skewed pivot faster than median branch misses dominated running time Sebastian Wild Branch Misses in Quicksort 2015-01-04 4 / 15
  27. 27. Why Should We Care? misprediction rates of “typical” programs < 10% (Comparison-based) sorting is different! Branch based on comparison result Comparisons reduce entropy (uncertainty about input) The less comparisons we use, the less predictable they become for classic Quicksort: misprediction rate 25 % with median-of-3: 31.25 % Practical Importance (KALIGOSI & SANDERS, ESA 2006): on Pentium 4 Prescott: very skewed pivot faster than median branch misses dominated running time Sebastian Wild Branch Misses in Quicksort 2015-01-04 4 / 15
  28. 28. Why Should We Care? misprediction rates of “typical” programs < 10% (Comparison-based) sorting is different! Branch based on comparison result Comparisons reduce entropy (uncertainty about input) The less comparisons we use, the less predictable they become for classic Quicksort: misprediction rate 25 % with median-of-3: 31.25 % Practical Importance (KALIGOSI & SANDERS, ESA 2006): on Pentium 4 Prescott: very skewed pivot faster than median branch misses dominated running time Sebastian Wild Branch Misses in Quicksort 2015-01-04 4 / 15
  29. 29. Track Record of Dual-Pivot Quicksort Since 2009, Java uses YAROSLAVSKIY’s dual-pivot Quicksort (YQS) faster than previously used classic Quicksort (CQS) in practice traditional cost measures do not explain this! CQS YQS Relative Running Time (from various experiments) −10±2% Comparisons 2 1.9 −5% Swaps 0.3 0.6 +80% Bytecode Instructions 18 21.7 +20.6% MMIX oops υ 11 13.1 +19.1% MMIX mems µ 2.6 2.8 +5% scanned elements1 (≈ cache misses) 2 1.6 −20% ·n ln n + O(n) , average case results What about branch misses? Can they explain YQS’s success? ... stay tuned. 1KUSHAGRA, LÓPEZ-ORTIZ, MUNRO, QIAO; ALENEX 2014 Sebastian Wild Branch Misses in Quicksort 2015-01-04 5 / 15
  30. 30. Track Record of Dual-Pivot Quicksort Since 2009, Java uses YAROSLAVSKIY’s dual-pivot Quicksort (YQS) faster than previously used classic Quicksort (CQS) in practice traditional cost measures do not explain this! CQS YQS Relative Running Time (from various experiments) −10±2% Comparisons 2 1.9 −5% Swaps 0.3 0.6 +80% Bytecode Instructions 18 21.7 +20.6% MMIX oops υ 11 13.1 +19.1% MMIX mems µ 2.6 2.8 +5% scanned elements1 (≈ cache misses) 2 1.6 −20% ·n ln n + O(n) , average case results What about branch misses? Can they explain YQS’s success? ... stay tuned. 1KUSHAGRA, LÓPEZ-ORTIZ, MUNRO, QIAO; ALENEX 2014 Sebastian Wild Branch Misses in Quicksort 2015-01-04 5 / 15
  31. 31. Track Record of Dual-Pivot Quicksort Since 2009, Java uses YAROSLAVSKIY’s dual-pivot Quicksort (YQS) faster than previously used classic Quicksort (CQS) in practice traditional cost measures do not explain this! CQS YQS Relative Running Time (from various experiments) −10±2% Comparisons 2 1.9 −5% Swaps 0.3 0.6 +80% Bytecode Instructions 18 21.7 +20.6% MMIX oops υ 11 13.1 +19.1% MMIX mems µ 2.6 2.8 +5% scanned elements1 (≈ cache misses) 2 1.6 −20% ·n ln n + O(n) , average case results What about branch misses? Can they explain YQS’s success? ... stay tuned. 1KUSHAGRA, LÓPEZ-ORTIZ, MUNRO, QIAO; ALENEX 2014 Sebastian Wild Branch Misses in Quicksort 2015-01-04 5 / 15
  32. 32. Track Record of Dual-Pivot Quicksort Since 2009, Java uses YAROSLAVSKIY’s dual-pivot Quicksort (YQS) faster than previously used classic Quicksort (CQS) in practice traditional cost measures do not explain this! CQS YQS Relative Running Time (from various experiments) −10±2% Comparisons 2 1.9 −5% Swaps 0.3 0.6 +80% Bytecode Instructions 18 21.7 +20.6% MMIX oops υ 11 13.1 +19.1% MMIX mems µ 2.6 2.8 +5% scanned elements1 (≈ cache misses) 2 1.6 −20% ·n ln n + O(n) , average case results What about branch misses? Can they explain YQS’s success? ... stay tuned. 1KUSHAGRA, LÓPEZ-ORTIZ, MUNRO, QIAO; ALENEX 2014 Sebastian Wild Branch Misses in Quicksort 2015-01-04 5 / 15
  33. 33. Track Record of Dual-Pivot Quicksort Since 2009, Java uses YAROSLAVSKIY’s dual-pivot Quicksort (YQS) faster than previously used classic Quicksort (CQS) in practice traditional cost measures do not explain this! CQS YQS Relative Running Time (from various experiments) −10±2% Comparisons 2 1.9 −5% Swaps 0.3 0.6 +80% Bytecode Instructions 18 21.7 +20.6% MMIX oops υ 11 13.1 +19.1% MMIX mems µ 2.6 2.8 +5% scanned elements1 (≈ cache misses) 2 1.6 −20% ·n ln n + O(n) , average case results What about branch misses? Can they explain YQS’s success? ... stay tuned. 1KUSHAGRA, LÓPEZ-ORTIZ, MUNRO, QIAO; ALENEX 2014 Sebastian Wild Branch Misses in Quicksort 2015-01-04 5 / 15
  34. 34. Track Record of Dual-Pivot Quicksort Since 2009, Java uses YAROSLAVSKIY’s dual-pivot Quicksort (YQS) faster than previously used classic Quicksort (CQS) in practice traditional cost measures do not explain this! CQS YQS Relative Running Time (from various experiments) −10±2% Comparisons 2 1.9 −5% Swaps 0.3 0.6 +80% Bytecode Instructions 18 21.7 +20.6% MMIX oops υ 11 13.1 +19.1% MMIX mems µ 2.6 2.8 +5% scanned elements1 (≈ cache misses) 2 1.6 −20% ·n ln n + O(n) , average case results What about branch misses? Can they explain YQS’s success? ... stay tuned. 1KUSHAGRA, LÓPEZ-ORTIZ, MUNRO, QIAO; ALENEX 2014 Sebastian Wild Branch Misses in Quicksort 2015-01-04 5 / 15
  35. 35. Track Record of Dual-Pivot Quicksort Since 2009, Java uses YAROSLAVSKIY’s dual-pivot Quicksort (YQS) faster than previously used classic Quicksort (CQS) in practice traditional cost measures do not explain this! CQS YQS Relative Running Time (from various experiments) −10±2% Comparisons 2 1.9 −5% Swaps 0.3 0.6 +80% Bytecode Instructions 18 21.7 +20.6% MMIX oops υ 11 13.1 +19.1% MMIX mems µ 2.6 2.8 +5% scanned elements1 (≈ cache misses) 2 1.6 −20% ·n ln n + O(n) , average case results What about branch misses? Can they explain YQS’s success? ... stay tuned. 1KUSHAGRA, LÓPEZ-ORTIZ, MUNRO, QIAO; ALENEX 2014 Sebastian Wild Branch Misses in Quicksort 2015-01-04 5 / 15
  36. 36. Random Model n i. i. d. elements chosen uniformly in [0, 1] 0 1 U1 U2U3 U4U5U6 U7U8 pairwise distinct almost surely relative ranking is a random permutation equivalent to classic model Consider pivot value P fixed: Pr U < P = P Pr U > P = 1 − P 0 1P Similarly for dual-pivot Quicksort with pivots P Q Pr U < P = D1 Pr P < U < Q = D2 Pr U > Q = D3 0 1P Q Sebastian Wild Branch Misses in Quicksort 2015-01-04 6 / 15
  37. 37. Random Model n i. i. d. elements chosen uniformly in [0, 1] 0 1 U1 U2U3 U4U5U6 U7U8 pairwise distinct almost surely relative ranking is a random permutation equivalent to classic model Consider pivot value P fixed: Pr U < P = P Pr U > P = 1 − P 0 1P Similarly for dual-pivot Quicksort with pivots P Q Pr U < P = D1 Pr P < U < Q = D2 Pr U > Q = D3 0 1P Q Sebastian Wild Branch Misses in Quicksort 2015-01-04 6 / 15
  38. 38. Random Model n i. i. d. elements chosen uniformly in [0, 1] 0 1 U1 U2U3 U4U5U6 U7U8 pairwise distinct almost surely relative ranking is a random permutation equivalent to classic model Consider pivot value P fixed: Pr U < P = P Pr U > P = 1 − P 0 1P Similarly for dual-pivot Quicksort with pivots P Q Pr U < P = D1 Pr P < U < Q = D2 Pr U > Q = D3 0 1P Q Sebastian Wild Branch Misses in Quicksort 2015-01-04 6 / 15
  39. 39. Random Model n i. i. d. elements chosen uniformly in [0, 1] 0 1 U1 U2U3 U4U5U6 U7U8 pairwise distinct almost surely relative ranking is a random permutation equivalent to classic model Consider pivot value P fixed: Pr U < P = P Pr U > P = 1 − P 0 1P Similarly for dual-pivot Quicksort with pivots P Q Pr U < P = D1 Pr P < U < Q = D2 Pr U > Q = D3 0 1P Q Sebastian Wild Branch Misses in Quicksort 2015-01-04 6 / 15
  40. 40. Random Model n i. i. d. elements chosen uniformly in [0, 1] 0 1 U1 U2U3 U4U5U6 U7U8 pairwise distinct almost surely relative ranking is a random permutation equivalent to classic model Consider pivot value P fixed: Pr U < P = P Pr U > P = 1 − P 0 1P Similarly for dual-pivot Quicksort with pivots P Q Pr U < P = D1 Pr P < U < Q = D2 Pr U > Q = D3 0 1P Q Sebastian Wild Branch Misses in Quicksort 2015-01-04 6 / 15
  41. 41. Random Model n i. i. d. elements chosen uniformly in [0, 1] 0 1 U1 U2U3 U4U5U6 U7U8 pairwise distinct almost surely relative ranking is a random permutation equivalent to classic model Consider pivot value P fixed: Pr U < P = P Pr U > P = 1 − P 0 1P Similarly for dual-pivot Quicksort with pivots P Q Pr U < P = D1 Pr P < U < Q = D2 Pr U > Q = D3 0 1P Q Sebastian Wild Branch Misses in Quicksort 2015-01-04 6 / 15
  42. 42. Random Model n i. i. d. elements chosen uniformly in [0, 1] 0 1 U1 U2U3 U4U5U6 U7U8 pairwise distinct almost surely relative ranking is a random permutation equivalent to classic model Consider pivot value P fixed: Pr U < P = P = D1 Pr U > P = 1 − P = D2 0 1P D1 D2 Similarly for dual-pivot Quicksort with pivots P Q Pr U < P = D1 Pr P < U < Q = D2 Pr U > Q = D3 0 1P Q D1 D2 D3 Sebastian Wild Branch Misses in Quicksort 2015-01-04 6 / 15
  43. 43. Random Model n i. i. d. elements chosen uniformly in [0, 1] 0 1 U1 U2U3 U4U5U6 U7U8 pairwise distinct almost surely relative ranking is a random permutation equivalent to classic model Consider pivot value P fixed: Pr U < P = P = D1 Pr U > P = 1 − P = D2 0 1P D1 D2 Similarly for dual-pivot Quicksort with pivots P Q Pr U < P = D1 Pr P < U < Q = D2 Pr U > Q = D3 0 1P Q D1 D2 D3 Sebastian Wild Branch Misses in Quicksort 2015-01-04 6 / 15
  44. 44. Random Model n i. i. d. elements chosen uniformly in [0, 1] 0 1 U1 U2U3 U4U5U6 U7U8 pairwise distinct almost surely relative ranking is a random permutation equivalent to classic model Consider pivot value P fixed: Pr U < P = P = D1 Pr U > P = 1 − P = D2 0 1P D1 D2 Similarly for dual-pivot Quicksort with pivots P Q Pr U < P = D1 Pr P < U < Q = D2 Pr U > Q = D3 0 1P Q D1 D2 D3 These probabilities hold for all elements U, independent of all other elements! Sebastian Wild Branch Misses in Quicksort 2015-01-04 6 / 15
  45. 45. Branches in CQS How many branches in first partitioning step of CQS? one comparison branch per element U: U < P left partition U > P right partition other branches (loop logic etc.) easy to predict only constant number of mispredictions can be ignored (for leading term asymptotics) Sebastian Wild Branch Misses in Quicksort 2015-01-04 7 / 15
  46. 46. Branches in CQS How many branches in first partitioning step of CQS? one comparison branch per element U: U < P left partition U > P right partition other branches (loop logic etc.) easy to predict only constant number of mispredictions can be ignored (for leading term asymptotics) Sebastian Wild Branch Misses in Quicksort 2015-01-04 7 / 15
  47. 47. Branches in CQS How many branches in first partitioning step of CQS? one comparison branch per element U: U < P left partition U > P right partition other branches (loop logic etc.) easy to predict only constant number of mispredictions can be ignored (for leading term asymptotics) Sebastian Wild Branch Misses in Quicksort 2015-01-04 7 / 15
  48. 48. Branches in CQS How many branches in first partitioning step of CQS? one comparison branch per element U: U < P left partition U > P right partition     other branches (loop logic etc.) easy to predict only constant number of mispredictions can be ignored (for leading term asymptotics)     Sebastian Wild Branch Misses in Quicksort 2015-01-04 7 / 15
  49. 49. Branches in CQS How many branches in first partitioning step of CQS? Consider pivot value P fixed. D = (D1, D2) = (P, 1 − P) fixed. one comparison branch per element U: U < P left partition U > P right partition     other branches (loop logic etc.) easy to predict only constant number of mispredictions can be ignored (for leading term asymptotics)     Sebastian Wild Branch Misses in Quicksort 2015-01-04 7 / 15
  50. 50. Branches in CQS How many branches in first partitioning step of CQS? Consider pivot value P fixed. D = (D1, D2) = (P, 1 − P) fixed. one comparison branch per element U: U < P left partition U > P right partition branch taken with prob. P i. i. d. for all elements U! memoryless source     other branches (loop logic etc.) easy to predict only constant number of mispredictions can be ignored (for leading term asymptotics)     Sebastian Wild Branch Misses in Quicksort 2015-01-04 7 / 15
  51. 51. Branches in CQS How many branches in first partitioning step of CQS? Consider pivot value P fixed. D = (D1, D2) = (P, 1 − P) fixed. one comparison branch per element U: U < P left partition U > P right partition branch taken with prob. P i. i. d. for all elements U! memoryless source     other branches (loop logic etc.) easy to predict only constant number of mispredictions can be ignored (for leading term asymptotics)     Sebastian Wild Branch Misses in Quicksort 2015-01-04 7 / 15
  52. 52. Misprediction Rate for Memoryless Sources Branches taken i. i. d. with probability p. Information theoretic lower bound: Miss rate: fOPT(p) = min{p, 1 − p} Can approach lower bound by estimating p. ˆp ≥ 1 2 taken ˆp < 1 2 not taken But: Actual predictors have very little memory! 1-bit Predictor Wrong prediction whenever value changes Miss rate: f1bit(p) = 2p(1 − p) 1 2 predict taken predict not taken p 1 − p 1 − p p Sebastian Wild Branch Misses in Quicksort 2015-01-04 8 / 15
  53. 53. Misprediction Rate for Memoryless Sources Branches taken i. i. d. with probability p. Information theoretic lower bound: Miss rate: fOPT(p) = min{p, 1 − p} Can approach lower bound by estimating p. ˆp ≥ 1 2 taken ˆp < 1 2 not taken But: Actual predictors have very little memory! 1-bit Predictor Wrong prediction whenever value changes Miss rate: f1bit(p) = 2p(1 − p) 1 2 predict taken predict not taken p 1 − p 1 − p p Sebastian Wild Branch Misses in Quicksort 2015-01-04 8 / 15
  54. 54. Misprediction Rate for Memoryless Sources Branches taken i. i. d. with probability p. Information theoretic lower bound: Miss rate: fOPT(p) = min{p, 1 − p} Can approach lower bound by estimating p. ˆp ≥ 1 2 taken ˆp < 1 2 not taken But: Actual predictors have very little memory! 1-bit Predictor Wrong prediction whenever value changes Miss rate: f1bit(p) = 2p(1 − p) 1 2 predict taken predict not taken p 1 − p 1 − p p Sebastian Wild Branch Misses in Quicksort 2015-01-04 8 / 15
  55. 55. Misprediction Rate for Memoryless Sources Branches taken i. i. d. with probability p. Information theoretic lower bound: Miss rate: fOPT(p) = min{p, 1 − p} Can approach lower bound by estimating p. ˆp ≥ 1 2 taken ˆp < 1 2 not taken But: Actual predictors have very little memory! 1-bit Predictor Wrong prediction whenever value changes Miss rate: f1bit(p) = 2p(1 − p) 1 2 predict taken predict not taken p 1 − p 1 − p p Sebastian Wild Branch Misses in Quicksort 2015-01-04 8 / 15
  56. 56. Misprediction Rate for Memoryless Sources Branches taken i. i. d. with probability p. Information theoretic lower bound: Miss rate: fOPT(p) = min{p, 1 − p} Can approach lower bound by estimating p. ˆp ≥ 1 2 taken ˆp < 1 2 not taken But: Actual predictors have very little memory! 1-bit Predictor Wrong prediction whenever value changes Miss rate: f1bit(p) = 2p(1 − p) 1 2 predict taken predict not taken p 1 − p 1 − p p Sebastian Wild Branch Misses in Quicksort 2015-01-04 8 / 15
  57. 57. Misprediction Rate for Memoryless Sources Branches taken i. i. d. with probability p. Information theoretic lower bound: Miss rate: fOPT(p) = min{p, 1 − p} Can approach lower bound by estimating p. ˆp ≥ 1 2 taken ˆp < 1 2 not taken But: Actual predictors have very little memory! 1-bit Predictor Wrong prediction whenever value changes Miss rate: f1bit(p) = 2p(1 − p) 1 2 predict taken predict not taken p 1 − p 1 − p p Sebastian Wild Branch Misses in Quicksort 2015-01-04 8 / 15
  58. 58. Misprediction Rate for Memoryless Sources Branches taken i. i. d. with probability p. Information theoretic lower bound: Miss rate: fOPT(p) = min{p, 1 − p} Can approach lower bound by estimating p. ˆp ≥ 1 2 taken ˆp < 1 2 not taken But: Actual predictors have very little memory! 1-bit Predictor Wrong prediction whenever value changes Miss rate: f1bit(p) = 2p(1 − p) 1 2 predict taken predict not taken p 1 − p 1 − p p Sebastian Wild Branch Misses in Quicksort 2015-01-04 8 / 15
  59. 59. Misprediction Rate for Memoryless Sources Branches taken i. i. d. with probability p. Information theoretic lower bound: Miss rate: fOPT(p) = min{p, 1 − p} Can approach lower bound by estimating p. ˆp ≥ 1 2 taken ˆp < 1 2 not taken But: Actual predictors have very little memory! 1-bit Predictor Wrong prediction whenever value changes Miss rate: f1bit(p) = 2p(1 − p) 1 2 predict taken predict not taken p 1 − p 1 − p p Sebastian Wild Branch Misses in Quicksort 2015-01-04 8 / 15
  60. 60. Misprediction Rate for Memoryless Sources [2] 2-bit Saturating Counter Miss rate? ... depends on state! 1 2 3 4 predict taken predict not taken p 1 − p 1 − p 1 − p 1 − p ppp But: Very fast convergence to steady state different initial state distributions 20 iterations for p = 2 3 use steady-state miss-rate: expected miss rate over states in stationary distribution here: f2-bit-sc(p) = q 1 − 2q with q = p(1 − p). similarly for 2-bit Flip-Consecutive f2-bit-fc(p) = q(1 + 2q) 1 − q . Sebastian Wild Branch Misses in Quicksort 2015-01-04 9 / 15
  61. 61. Misprediction Rate for Memoryless Sources [2] 2-bit Saturating Counter Miss rate? ... depends on state! 1 2 3 4 predict taken predict not taken p 1 − p 1 − p 1 − p 1 − p ppp But: Very fast convergence to steady state different initial state distributions 20 iterations for p = 2 3 use steady-state miss-rate: expected miss rate over states in stationary distribution here: f2-bit-sc(p) = q 1 − 2q with q = p(1 − p). similarly for 2-bit Flip-Consecutive f2-bit-fc(p) = q(1 + 2q) 1 − q . Sebastian Wild Branch Misses in Quicksort 2015-01-04 9 / 15
  62. 62. Misprediction Rate for Memoryless Sources [2] 2-bit Saturating Counter Miss rate? ... depends on state! 1 2 3 4 predict taken predict not taken p 1 − p 1 − p 1 − p 1 − p ppp But: Very fast convergence to steady state different initial state distributions 20 iterations for p = 2 3 use steady-state miss-rate: expected miss rate over states in stationary distribution here: f2-bit-sc(p) = q 1 − 2q with q = p(1 − p). similarly for 2-bit Flip-Consecutive f2-bit-fc(p) = q(1 + 2q) 1 − q . Sebastian Wild Branch Misses in Quicksort 2015-01-04 9 / 15
  63. 63. Misprediction Rate for Memoryless Sources [2] 2-bit Saturating Counter Miss rate? ... depends on state! 1 2 3 4 predict taken predict not taken p 1 − p 1 − p 1 − p 1 − p ppp But: Very fast convergence to steady state different initial state distributions 20 iterations for p = 2 3 use steady-state miss-rate: expected miss rate over states in stationary distribution here: f2-bit-sc(p) = q 1 − 2q with q = p(1 − p). similarly for 2-bit Flip-Consecutive f2-bit-fc(p) = q(1 + 2q) 1 − q . Sebastian Wild Branch Misses in Quicksort 2015-01-04 9 / 15
  64. 64. Misprediction Rate for Memoryless Sources [2] 2-bit Saturating Counter Miss rate? ... depends on state! 1 2 3 4 predict taken predict not taken p 1 − p 1 − p 1 − p 1 − p ppp But: Very fast convergence to steady state different initial state distributions 20 iterations for p = 2 3 use steady-state miss-rate: expected miss rate over states in stationary distribution here: f2-bit-sc(p) = q 1 − 2q with q = p(1 − p). similarly for 2-bit Flip-Consecutive f2-bit-fc(p) = q(1 + 2q) 1 − q . Sebastian Wild Branch Misses in Quicksort 2015-01-04 9 / 15
  65. 65. Misprediction Rate for Memoryless Sources [2] 2-bit Saturating Counter Miss rate? ... depends on state! 1 2 3 4 predict taken predict not taken p 1 − p 1 − p 1 − p 1 − p ppp But: Very fast convergence to steady state different initial state distributions 20 iterations for p = 2 3 use steady-state miss-rate: expected miss rate over states in stationary distribution here: f2-bit-sc(p) = q 1 − 2q with q = p(1 − p). similarly for 2-bit Flip-Consecutive f2-bit-fc(p) = q(1 + 2q) 1 − q . Sebastian Wild Branch Misses in Quicksort 2015-01-04 9 / 15
  66. 66. Misprediction Rate for Memoryless Sources [2] 2-bit Saturating Counter Miss rate? ... depends on state! 1 2 3 4 predict taken predict not taken p 1 − p 1 − p 1 − p 1 − p ppp But: Very fast convergence to steady state different initial state distributions 20 iterations for p = 2 3 use steady-state miss-rate: expected miss rate over states in stationary distribution here: f2-bit-sc(p) = q 1 − 2q with q = p(1 − p). similarly for 2-bit Flip-Consecutive f2-bit-fc(p) = q(1 + 2q) 1 − q . Sebastian Wild Branch Misses in Quicksort 2015-01-04 9 / 15
  67. 67. Misprediction Rate for Memoryless Sources [2] 2-bit Saturating Counter Miss rate? ... depends on state! 1 2 3 4 predict taken predict not taken p 1 − p 1 − p 1 − p 1 − p ppp But: Very fast convergence to steady state different initial state distributions 20 iterations for p = 2 3 use steady-state miss-rate: expected miss rate over states in stationary distribution here: f2-bit-sc(p) = q 1 − 2q with q = p(1 − p). similarly for 2-bit Flip-Consecutive f2-bit-fc(p) = q(1 + 2q) 1 − q . Sebastian Wild Branch Misses in Quicksort 2015-01-04 9 / 15
  68. 68. Misprediction Rate for Memoryless Sources [2] 2-bit Saturating Counter Miss rate? ... depends on state! 1 2 3 4 predict taken predict not taken p 1 − p 1 − p 1 − p 1 − p ppp But: Very fast convergence to steady state different initial state distributions 20 iterations for p = 2 3 use steady-state miss-rate: expected miss rate over states in stationary distribution here: f2-bit-sc(p) = q 1 − 2q with q = p(1 − p). similarly for 2-bit Flip-Consecutive f2-bit-fc(p) = q(1 + 2q) 1 − q . Sebastian Wild Branch Misses in Quicksort 2015-01-04 9 / 15
  69. 69. Misprediction Rate for Memoryless Sources [2] 2-bit Saturating Counter Miss rate? ... depends on state! 1 2 3 4 predict taken predict not taken p 1 − p 1 − p 1 − p 1 − p ppp But: Very fast convergence to steady state different initial state distributions 20 iterations for p = 2 3 use steady-state miss-rate: expected miss rate over states in stationary distribution here: f2-bit-sc(p) = q 1 − 2q with q = p(1 − p). similarly for 2-bit Flip-Consecutive f2-bit-fc(p) = q(1 + 2q) 1 − q . Sebastian Wild Branch Misses in Quicksort 2015-01-04 9 / 15
  70. 70. Misprediction Rate for Memoryless Sources [2] 2-bit Saturating Counter Miss rate? ... depends on state! 1 2 3 4 predict taken predict not taken p 1 − p 1 − p 1 − p 1 − p ppp But: Very fast convergence to steady state different initial state distributions 20 iterations for p = 2 3 use steady-state miss-rate: expected miss rate over states in stationary distribution here: f2-bit-sc(p) = q 1 − 2q with q = p(1 − p). similarly for 2-bit Flip-Consecutive f2-bit-fc(p) = q(1 + 2q) 1 − q . Sebastian Wild Branch Misses in Quicksort 2015-01-04 9 / 15
  71. 71. Misprediction Rate for Memoryless Sources [2] 2-bit Saturating Counter Miss rate? ... depends on state! 1 2 3 4 predict taken predict not taken p 1 − p 1 − p 1 − p 1 − p ppp But: Very fast convergence to steady state different initial state distributions 20 iterations for p = 2 3 use steady-state miss-rate: expected miss rate over states in stationary distribution here: f2-bit-sc(p) = q 1 − 2q with q = p(1 − p). similarly for 2-bit Flip-Consecutive f2-bit-fc(p) = q(1 + 2q) 1 − q . Sebastian Wild Branch Misses in Quicksort 2015-01-04 9 / 15
  72. 72. Misprediction Rate for Memoryless Sources [2] 2-bit Saturating Counter Miss rate? ... depends on state! 1 2 3 4 predict taken predict not taken p 1 − p 1 − p 1 − p 1 − p ppp But: Very fast convergence to steady state different initial state distributions 20 iterations for p = 2 3 use steady-state miss-rate: expected miss rate over states in stationary distribution here: f2-bit-sc(p) = q 1 − 2q with q = p(1 − p). similarly for 2-bit Flip-Consecutive f2-bit-fc(p) = q(1 + 2q) 1 − q . Sebastian Wild Branch Misses in Quicksort 2015-01-04 9 / 15
  73. 73. Misprediction Rate for Memoryless Sources [2] 2-bit Saturating Counter Miss rate? ... depends on state! 1 2 3 4 predict taken predict not taken p 1 − p 1 − p 1 − p 1 − p ppp But: Very fast convergence to steady state different initial state distributions 20 iterations for p = 2 3 use steady-state miss-rate: expected miss rate over states in stationary distribution here: f2-bit-sc(p) = q 1 − 2q with q = p(1 − p). similarly for 2-bit Flip-Consecutive f2-bit-fc(p) = q(1 + 2q) 1 − q . Sebastian Wild Branch Misses in Quicksort 2015-01-04 9 / 15
  74. 74. Misprediction Rate for Memoryless Sources [2] 2-bit Saturating Counter Miss rate? ... depends on state! 1 2 3 4 predict taken predict not taken p 1 − p 1 − p 1 − p 1 − p ppp But: Very fast convergence to steady state different initial state distributions 20 iterations for p = 2 3 use steady-state miss-rate: expected miss rate over states in stationary distribution here: f2-bit-sc(p) = q 1 − 2q with q = p(1 − p). similarly for 2-bit Flip-Consecutive f2-bit-fc(p) = q(1 + 2q) 1 − q . Sebastian Wild Branch Misses in Quicksort 2015-01-04 9 / 15
  75. 75. Misprediction Rate for Memoryless Sources [2] 2-bit Saturating Counter Miss rate? ... depends on state! 1 2 3 4 predict taken predict not taken p 1 − p 1 − p 1 − p 1 − p ppp But: Very fast convergence to steady state different initial state distributions 20 iterations for p = 2 3 use steady-state miss-rate: expected miss rate over states in stationary distribution here: f2-bit-sc(p) = q 1 − 2q with q = p(1 − p). similarly for 2-bit Flip-Consecutive f2-bit-fc(p) = q(1 + 2q) 1 − q . Sebastian Wild Branch Misses in Quicksort 2015-01-04 9 / 15
  76. 76. Misprediction Rate for Memoryless Sources [2] 2-bit Saturating Counter Miss rate? ... depends on state! 1 2 3 4 predict taken predict not taken p 1 − p 1 − p 1 − p 1 − p ppp But: Very fast convergence to steady state different initial state distributions 20 iterations for p = 2 3 use steady-state miss-rate: expected miss rate over states in stationary distribution here: f2-bit-sc(p) = q 1 − 2q with q = p(1 − p). similarly for 2-bit Flip-Consecutive f2-bit-fc(p) = q(1 + 2q) 1 − q . Sebastian Wild Branch Misses in Quicksort 2015-01-04 9 / 15
  77. 77. Misprediction Rate for Memoryless Sources [2] 2-bit Saturating Counter Miss rate? ... depends on state! 1 2 3 4 predict taken predict not taken p 1 − p 1 − p 1 − p 1 − p ppp But: Very fast convergence to steady state different initial state distributions 20 iterations for p = 2 3 use steady-state miss-rate: expected miss rate over states in stationary distribution here: f2-bit-sc(p) = q 1 − 2q with q = p(1 − p). similarly for 2-bit Flip-Consecutive f2-bit-fc(p) = q(1 + 2q) 1 − q . Sebastian Wild Branch Misses in Quicksort 2015-01-04 9 / 15
  78. 78. Misprediction Rate for Memoryless Sources [2] 2-bit Saturating Counter Miss rate? ... depends on state! 1 2 3 4 predict taken predict not taken p 1 − p 1 − p 1 − p 1 − p ppp But: Very fast convergence to steady state different initial state distributions 20 iterations for p = 2 3 use steady-state miss-rate: expected miss rate over states in stationary distribution here: f2-bit-sc(p) = q 1 − 2q with q = p(1 − p). similarly for 2-bit Flip-Consecutive f2-bit-fc(p) = q(1 + 2q) 1 − q . Sebastian Wild Branch Misses in Quicksort 2015-01-04 9 / 15
  79. 79. Misprediction Rate for Memoryless Sources [2] 2-bit Saturating Counter Miss rate? ... depends on state! 1 2 3 4 predict taken predict not taken p 1 − p 1 − p 1 − p 1 − p ppp But: Very fast convergence to steady state different initial state distributions 20 iterations for p = 2 3 use steady-state miss-rate: expected miss rate over states in stationary distribution here: f2-bit-sc(p) = q 1 − 2q with q = p(1 − p). similarly for 2-bit Flip-Consecutive f2-bit-fc(p) = q(1 + 2q) 1 − q . Sebastian Wild Branch Misses in Quicksort 2015-01-04 9 / 15
  80. 80. Misprediction Rate for Memoryless Sources [2] 2-bit Saturating Counter Miss rate? ... depends on state! 1 2 3 4 predict taken predict not taken p 1 − p 1 − p 1 − p 1 − p ppp But: Very fast convergence to steady state different initial state distributions 20 iterations for p = 2 3 use steady-state miss-rate: expected miss rate over states in stationary distribution here: f2-bit-sc(p) = q 1 − 2q with q = p(1 − p). similarly for 2-bit Flip-Consecutive f2-bit-fc(p) = q(1 + 2q) 1 − q . Sebastian Wild Branch Misses in Quicksort 2015-01-04 9 / 15
  81. 81. Misprediction Rate for Memoryless Sources [2] 2-bit Saturating Counter Miss rate? ... depends on state! 1 2 3 4 predict taken predict not taken p 1 − p 1 − p 1 − p 1 − p ppp But: Very fast convergence to steady state different initial state distributions 20 iterations for p = 2 3 use steady-state miss-rate: expected miss rate over states in stationary distribution here: f2-bit-sc(p) = q 1 − 2q with q = p(1 − p). similarly for 2-bit Flip-Consecutive f2-bit-fc(p) = q(1 + 2q) 1 − q . Sebastian Wild Branch Misses in Quicksort 2015-01-04 9 / 15
  82. 82. Misprediction Rate for Memoryless Sources [2] 2-bit Saturating Counter Miss rate? ... depends on state! 1 2 3 4 predict taken predict not taken p 1 − p 1 − p 1 − p 1 − p ppp But: Very fast convergence to steady state different initial state distributions 20 iterations for p = 2 3 use steady-state miss-rate: expected miss rate over states in stationary distribution here: f2-bit-sc(p) = q 1 − 2q with q = p(1 − p). similarly for 2-bit Flip-Consecutive f2-bit-fc(p) = q(1 + 2q) 1 − q . Sebastian Wild Branch Misses in Quicksort 2015-01-04 9 / 15
  83. 83. Misprediction Rate for Memoryless Sources [2] 2-bit Saturating Counter Miss rate? ... depends on state! 1 2 3 4 predict taken predict not taken p 1 − p 1 − p 1 − p 1 − p ppp But: Very fast convergence to steady state different initial state distributions 20 iterations for p = 2 3 use steady-state miss-rate: expected miss rate over states in stationary distribution here: f2-bit-sc(p) = q 1 − 2q with q = p(1 − p). similarly for 2-bit Flip-Consecutive f2-bit-fc(p) = q(1 + 2q) 1 − q . Sebastian Wild Branch Misses in Quicksort 2015-01-04 9 / 15
  84. 84. Misprediction Rate for Memoryless Sources [2] 2-bit Saturating Counter Miss rate? ... depends on state! 1 2 3 4 predict taken predict not taken p 1 − p 1 − p 1 − p 1 − p ppp But: Very fast convergence to steady state different initial state distributions 20 iterations for p = 2 3 use steady-state miss-rate: expected miss rate over states in stationary distribution here: f2-bit-sc(p) = q 1 − 2q with q = p(1 − p). similarly for 2-bit Flip-Consecutive f2-bit-fc(p) = q(1 + 2q) 1 − q . Sebastian Wild Branch Misses in Quicksort 2015-01-04 9 / 15
  85. 85. Misprediction Rate for Memoryless Sources [2] 2-bit Saturating Counter Miss rate? ... depends on state! 1 2 3 4 predict taken predict not taken p 1 − p 1 − p 1 − p 1 − p ppp But: Very fast convergence to steady state different initial state distributions 20 iterations for p = 2 3 use steady-state miss-rate: expected miss rate over states in stationary distribution here: f2-bit-sc(p) = q 1 − 2q with q = p(1 − p). similarly for 2-bit Flip-Consecutive f2-bit-fc(p) = q(1 + 2q) 1 − q . Sebastian Wild Branch Misses in Quicksort 2015-01-04 9 / 15
  86. 86. Misprediction Rate for Memoryless Sources [2] 2-bit Saturating Counter Miss rate? ... depends on state! 1 2 3 4 predict taken predict not taken p 1 − p 1 − p 1 − p 1 − p ppp But: Very fast convergence to steady state different initial state distributions 20 iterations for p = 2 3 use steady-state miss-rate: expected miss rate over states in stationary distribution here: f2-bit-sc(p) = q 1 − 2q with q = p(1 − p). similarly for 2-bit Flip-Consecutive f2-bit-fc(p) = q(1 + 2q) 1 − q . Sebastian Wild Branch Misses in Quicksort 2015-01-04 9 / 15
  87. 87. Distribution of Pivot Values In (classic) Quicksort branch probability is random expected miss rate: E[f(P)]. (expectation over pivot values P) What is the distribution of P? without sampling: P D = Uniform(0, 1) Typical pivot choice: median of k (in practice: k = 3) or pseudomedian of 9 (“ninther”) Here: more general scheme with parameter t = (t1, t2) Example: k = 6 and t = (3, 2): P t1 t2 t = (0, 0) no sampling t = (t, t) gives median-of-(2t + 1) can also sample skewed pivots Distribution of pivot value: P D = Beta(t1 + 1, t2 + 1) Sebastian Wild Branch Misses in Quicksort 2015-01-04 10 / 15
  88. 88. Distribution of Pivot Values In (classic) Quicksort branch probability is random expected miss rate: E[f(P)]. (expectation over pivot values P) What is the distribution of P? without sampling: P D = Uniform(0, 1) Typical pivot choice: median of k (in practice: k = 3) or pseudomedian of 9 (“ninther”) Here: more general scheme with parameter t = (t1, t2) Example: k = 6 and t = (3, 2): P t1 t2 t = (0, 0) no sampling t = (t, t) gives median-of-(2t + 1) can also sample skewed pivots Distribution of pivot value: P D = Beta(t1 + 1, t2 + 1) Sebastian Wild Branch Misses in Quicksort 2015-01-04 10 / 15
  89. 89. Distribution of Pivot Values In (classic) Quicksort branch probability is random expected miss rate: E[f(P)]. (expectation over pivot values P) What is the distribution of P? without sampling: P D = Uniform(0, 1) Typical pivot choice: median of k (in practice: k = 3) or pseudomedian of 9 (“ninther”) Here: more general scheme with parameter t = (t1, t2) Example: k = 6 and t = (3, 2): P t1 t2 t = (0, 0) no sampling t = (t, t) gives median-of-(2t + 1) can also sample skewed pivots Distribution of pivot value: P D = Beta(t1 + 1, t2 + 1) Sebastian Wild Branch Misses in Quicksort 2015-01-04 10 / 15
  90. 90. Distribution of Pivot Values In (classic) Quicksort branch probability is random expected miss rate: E[f(P)]. (expectation over pivot values P) What is the distribution of P? without sampling: P D = Uniform(0, 1) Typical pivot choice: median of k (in practice: k = 3) or pseudomedian of 9 (“ninther”) Here: more general scheme with parameter t = (t1, t2) Example: k = 6 and t = (3, 2): P t1 t2 t = (0, 0) no sampling t = (t, t) gives median-of-(2t + 1) can also sample skewed pivots Distribution of pivot value: P D = Beta(t1 + 1, t2 + 1) Sebastian Wild Branch Misses in Quicksort 2015-01-04 10 / 15
  91. 91. Distribution of Pivot Values In (classic) Quicksort branch probability is random expected miss rate: E[f(P)]. (expectation over pivot values P) What is the distribution of P? without sampling: P D = Uniform(0, 1) Typical pivot choice: median of k (in practice: k = 3) or pseudomedian of 9 (“ninther”) Here: more general scheme with parameter t = (t1, t2) Example: k = 6 and t = (3, 2): P t1 t2 t = (0, 0) no sampling t = (t, t) gives median-of-(2t + 1) can also sample skewed pivots Distribution of pivot value: P D = Beta(t1 + 1, t2 + 1) Sebastian Wild Branch Misses in Quicksort 2015-01-04 10 / 15
  92. 92. Distribution of Pivot Values In (classic) Quicksort branch probability is random expected miss rate: E[f(P)]. (expectation over pivot values P) What is the distribution of P? without sampling: P D = Uniform(0, 1) Typical pivot choice: median of k (in practice: k = 3) or pseudomedian of 9 (“ninther”) Here: more general scheme with parameter t = (t1, t2) Example: k = 6 and t = (3, 2): P t1 t2 t = (0, 0) no sampling t = (t, t) gives median-of-(2t + 1) can also sample skewed pivots Distribution of pivot value: P D = Beta(t1 + 1, t2 + 1) Sebastian Wild Branch Misses in Quicksort 2015-01-04 10 / 15
  93. 93. Distribution of Pivot Values In (classic) Quicksort branch probability is random expected miss rate: E[f(P)]. (expectation over pivot values P) What is the distribution of P? without sampling: P D = Uniform(0, 1) Typical pivot choice: median of k (in practice: k = 3) or pseudomedian of 9 (“ninther”) Here: more general scheme with parameter t = (t1, t2) Example: k = 6 and t = (3, 2): P t1 t2 t = (0, 0) no sampling t = (t, t) gives median-of-(2t + 1) can also sample skewed pivots Distribution of pivot value: P D = Beta(t1 + 1, t2 + 1) Sebastian Wild Branch Misses in Quicksort 2015-01-04 10 / 15
  94. 94. Distribution of Pivot Values In (classic) Quicksort branch probability is random expected miss rate: E[f(P)]. (expectation over pivot values P) What is the distribution of P? without sampling: P D = Uniform(0, 1) Typical pivot choice: median of k (in practice: k = 3) or pseudomedian of 9 (“ninther”) Here: more general scheme with parameter t = (t1, t2) Example: k = 6 and t = (3, 2): P t1 t2 t = (0, 0) no sampling t = (t, t) gives median-of-(2t + 1) can also sample skewed pivots Distribution of pivot value: P D = Beta(t1 + 1, t2 + 1) Sebastian Wild Branch Misses in Quicksort 2015-01-04 10 / 15
  95. 95. Distribution of Pivot Values In (classic) Quicksort branch probability is random expected miss rate: E[f(P)]. (expectation over pivot values P) What is the distribution of P? without sampling: P D = Uniform(0, 1) Typical pivot choice: median of k (in practice: k = 3) or pseudomedian of 9 (“ninther”) Here: more general scheme with parameter t = (t1, t2) Example: k = 6 and t = (3, 2): P t1 t2 t = (0, 0) no sampling t = (t, t) gives median-of-(2t + 1) can also sample skewed pivots Distribution of pivot value: P D = Beta(t1 + 1, t2 + 1) Sebastian Wild Branch Misses in Quicksort 2015-01-04 10 / 15
  96. 96. Distribution of Pivot Values In (classic) Quicksort branch probability is random expected miss rate: E[f(P)]. (expectation over pivot values P) What is the distribution of P? without sampling: P D = Uniform(0, 1) Typical pivot choice: median of k (in practice: k = 3) or pseudomedian of 9 (“ninther”) Here: more general scheme with parameter t = (t1, t2) Example: k = 6 and t = (3, 2): P t1 t2 t = (0, 0) no sampling t = (t, t) gives median-of-(2t + 1) can also sample skewed pivots Distribution of pivot value: P D = Beta(t1 + 1, t2 + 1) Sebastian Wild Branch Misses in Quicksort 2015-01-04 10 / 15
  97. 97. Miss Rates for Quicksort Branch expected miss rate given by integral E[f(P)] = ˆ 1 0 f(p) · pt1 (1 − p)t2 B(t + 1) dp e. g. for 1-bit predictor E[f1-bit(P)] = ˆ 1 0 2p(1 − p) · pt1 (1 − p)t2 B(t + 1) dp no concise representation for other integrals ... (see paper) but: exact values for fixed t Sebastian Wild Branch Misses in Quicksort 2015-01-04 11 / 15
  98. 98. Miss Rates for Quicksort Branch expected miss rate given by integral E[f(P)] = ˆ 1 0 f(p) · pt1 (1 − p)t2 B(t + 1) dp e. g. for 1-bit predictor E[f1-bit(P)] = ˆ 1 0 2p(1 − p) · pt1 (1 − p)t2 B(t + 1) dp no concise representation for other integrals ... (see paper) but: exact values for fixed t Sebastian Wild Branch Misses in Quicksort 2015-01-04 11 / 15
  99. 99. Miss Rates for Quicksort Branch expected miss rate given by integral E[f(P)] = ˆ 1 0 f(p) · pt1 (1 − p)t2 B(t + 1) dp e. g. for 1-bit predictor E[f1-bit(P)] = ˆ 1 0 2p(1 − p) · pt1 (1 − p)t2 B(t + 1) dp = 2 (t1 + 1)(t2 + 1) (k + 2)(k + 1) no concise representation for other integrals ... (see paper) but: exact values for fixed t Sebastian Wild Branch Misses in Quicksort 2015-01-04 11 / 15
  100. 100. Miss Rates for Quicksort Branch expected miss rate given by integral E[f(P)] = ˆ 1 0 f(p) · pt1 (1 − p)t2 B(t + 1) dp e. g. for 1-bit predictor E[f1-bit(P)] = ˆ 1 0 2p(1 − p) · pt1 (1 − p)t2 B(t + 1) dp = 2 (t1 + 1)(t2 + 1) (k + 2)(k + 1) no concise representation for other integrals ... (see paper) but: exact values for fixed t Sebastian Wild Branch Misses in Quicksort 2015-01-04 11 / 15
  101. 101. Miss Rate and Branch Misses Miss Rate for CQS with median of 2t+1: 0 2 4 6 8 0.3 0.4 0.5 0.5 t miss rate OPT 1-bit 2-bit sc 2-bit fc miss rates quickly get bad (close to guessing!) but: less comparisons in total! 0 2 4 6 8 1.4 1.6 1.8 2 1/ ln 2 ·n ln n + O(n) t #cmps Consider number of branch misses: #BM = #comparisons · miss rate Overall BM still grows with t. 0 2 4 6 8 0.5 0.6 0.7 0.5/ ln 2 ·n ln n + O(n) t #BM Sebastian Wild Branch Misses in Quicksort 2015-01-04 12 / 15
  102. 102. Miss Rate and Branch Misses Miss Rate for CQS with median of 2t+1: 0 2 4 6 8 0.3 0.4 0.5 0.5 t miss rate OPT 1-bit 2-bit sc 2-bit fc miss rates quickly get bad (close to guessing!) but: less comparisons in total! 0 2 4 6 8 1.4 1.6 1.8 2 1/ ln 2 ·n ln n + O(n) t #cmps Consider number of branch misses: #BM = #comparisons · miss rate Overall BM still grows with t. 0 2 4 6 8 0.5 0.6 0.7 0.5/ ln 2 ·n ln n + O(n) t #BM Sebastian Wild Branch Misses in Quicksort 2015-01-04 12 / 15
  103. 103. Miss Rate and Branch Misses Miss Rate for CQS with median of 2t+1: 0 2 4 6 8 0.3 0.4 0.5 0.5 t miss rate OPT 1-bit 2-bit sc 2-bit fc miss rates quickly get bad (close to guessing!) but: less comparisons in total! 0 2 4 6 8 1.4 1.6 1.8 2 1/ ln 2 ·n ln n + O(n) t #cmps Consider number of branch misses: #BM = #comparisons · miss rate Overall BM still grows with t. 0 2 4 6 8 0.5 0.6 0.7 0.5/ ln 2 ·n ln n + O(n) t #BM Sebastian Wild Branch Misses in Quicksort 2015-01-04 12 / 15
  104. 104. Miss Rate and Branch Misses Miss Rate for CQS with median of 2t+1: 0 2 4 6 8 0.3 0.4 0.5 0.5 t miss rate OPT 1-bit 2-bit sc 2-bit fc miss rates quickly get bad (close to guessing!) but: less comparisons in total! 0 2 4 6 8 1.4 1.6 1.8 2 1/ ln 2 ·n ln n + O(n) t #cmps Consider number of branch misses: #BM = #comparisons · miss rate Overall BM still grows with t. 0 2 4 6 8 0.5 0.6 0.7 0.5/ ln 2 ·n ln n + O(n) t #BM Sebastian Wild Branch Misses in Quicksort 2015-01-04 12 / 15
  105. 105. Miss Rate and Branch Misses Miss Rate for CQS with median of 2t+1: 0 2 4 6 8 0.3 0.4 0.5 0.5 t miss rate OPT 1-bit 2-bit sc 2-bit fc miss rates quickly get bad (close to guessing!) but: less comparisons in total! 0 2 4 6 8 1.4 1.6 1.8 2 1/ ln 2 ·n ln n + O(n) t #cmps Consider number of branch misses: #BM = #comparisons · miss rate Overall BM still grows with t. 0 2 4 6 8 0.5 0.6 0.7 0.5/ ln 2 ·n ln n + O(n) t #BM Sebastian Wild Branch Misses in Quicksort 2015-01-04 12 / 15
  106. 106. Miss Rate and Branch Misses Miss Rate for CQS with median of 2t+1: 0 2 4 6 8 0.3 0.4 0.5 0.5 t miss rate OPT 1-bit 2-bit sc 2-bit fc miss rates quickly get bad (close to guessing!) but: less comparisons in total! 0 2 4 6 8 1.4 1.6 1.8 2 1/ ln 2 ·n ln n + O(n) t #cmps Consider number of branch misses: #BM = #comparisons · miss rate Overall BM still grows with t. 0 2 4 6 8 0.5 0.6 0.7 0.5/ ln 2 ·n ln n + O(n) t #BM Sebastian Wild Branch Misses in Quicksort 2015-01-04 12 / 15
  107. 107. Branch Misses in YQS Original question: Does YQS better than CQS w. r. t. branch misses? Complication for analysis: 4 branch locations how often they are executed depends on input < P ? swap < Q ? skip swap g Q ? P ? skip swap swap k P P ≤ ◦ ≤ Q ≥ QP Q Example: C(y1) executed ( D1 + D2 )n + O(1) times. (in expectation, conditional on D) branch taken i. i. d. with prob D1 . (conditional on D) expected #BM at C(y1) in first partitioning step: E[(D1 + D2) · f(D1)] · n + O(1) Integrals even more “fun” ... but doable Sebastian Wild Branch Misses in Quicksort 2015-01-04 13 / 15
  108. 108. Branch Misses in YQS Original question: Does YQS better than CQS w. r. t. branch misses? Complication for analysis: 4 branch locations how often they are executed depends on input P ? swap Q ? skip swap g Q ? P ? skip swap swap k P P ≤ ◦ ≤ Q ≥ QP Q Example: C(y1) executed ( D1 + D2 )n + O(1) times. (in expectation, conditional on D) branch taken i. i. d. with prob D1 . (conditional on D) expected #BM at C(y1) in first partitioning step: E[(D1 + D2) · f(D1)] · n + O(1) Integrals even more “fun” ... but doable Sebastian Wild Branch Misses in Quicksort 2015-01-04 13 / 15
  109. 109. Branch Misses in YQS Original question: Does YQS better than CQS w. r. t. branch misses? Complication for analysis: 4 branch locations how often they are executed depends on input P ? swap Q ? skip swap g Q ? P ? skip swap swap k P P ≤ ◦ ≤ Q ≥ QP Q Example: C(y1) executed ( D1 + D2 )n + O(1) times. (in expectation, conditional on D) branch taken i. i. d. with prob D1 . (conditional on D) expected #BM at C(y1) in first partitioning step: E[(D1 + D2) · f(D1)] · n + O(1) Integrals even more “fun” ... but doable Sebastian Wild Branch Misses in Quicksort 2015-01-04 13 / 15
  110. 110. Branch Misses in YQS Original question: Does YQS better than CQS w. r. t. branch misses? Complication for analysis: 4 branch locations how often they are executed depends on input P ? swap Q ? skip swap g Q ? P ? skip swap swap k P P ≤ ◦ ≤ Q ≥ QP Q Example: C(y1) executed ( D1 + D2 )n + O(1) times. (in expectation, conditional on D) branch taken i. i. d. with prob D1 . (conditional on D) expected #BM at C(y1) in first partitioning step: E[(D1 + D2) · f(D1)] · n + O(1) Integrals even more “fun” ... but doable Sebastian Wild Branch Misses in Quicksort 2015-01-04 13 / 15
  111. 111. Branch Misses in YQS Original question: Does YQS better than CQS w. r. t. branch misses? Complication for analysis: 4 branch locations how often they are executed depends on input P ? swap Q ? skip swap g Q ? P ? skip swap swap k P P ≤ ◦ ≤ Q ≥ QP Q Example: C(y1) executed ( D1 + D2 )n + O(1) times. (in expectation, conditional on D) branch taken i. i. d. with prob D1 . (conditional on D) expected #BM at C(y1) in first partitioning step: E[(D1 + D2) · f(D1)] · n + O(1) Integrals even more “fun” ... but doable Sebastian Wild Branch Misses in Quicksort 2015-01-04 13 / 15
  112. 112. Branch Misses in YQS Original question: Does YQS better than CQS w. r. t. branch misses? Complication for analysis: 4 branch locations how often they are executed depends on input P ? swap Q ? skip swap g Q ? P ? skip swap swap k P P ≤ ◦ ≤ Q ≥ QP Q Example: C(y1) executed ( D1 + D2 )n + O(1) times. (in expectation, conditional on D) branch taken i. i. d. with prob D1 . (conditional on D) expected #BM at C(y1) in first partitioning step: E[(D1 + D2) · f(D1)] · n + O(1) Integrals even more “fun” ... but doable Sebastian Wild Branch Misses in Quicksort 2015-01-04 13 / 15
  113. 113. Branch Misses in YQS Original question: Does YQS better than CQS w. r. t. branch misses? Complication for analysis: 4 branch locations how often they are executed depends on input P ? swap Q ? skip swap g Q ? P ? skip swap swap k P P ≤ ◦ ≤ Q ≥ QP Q Example: C(y1) executed ( D1 + D2 )n + O(1) times. (in expectation, conditional on D) branch taken i. i. d. with prob D1 . (conditional on D) expected #BM at C(y1) in first partitioning step: E[(D1 + D2) · f(D1)] · n + O(1) Integrals even more “fun” ... but doable Sebastian Wild Branch Misses in Quicksort 2015-01-04 13 / 15
  114. 114. Results CQS vs. YQS Original question: Does YQS better than CQS w. r. t. branch misses? Expected number of branch misses without pivot sampling CQS YQS Relative OPT 0.5 0.513 +2.6% 1-bit 0.6 0.673 +1.0% 2-bit sc 0.571 0.585 +2.5% 2-bit fc 0.589 0.602 +2.2% ·n ln n + O(n) CQS median-of-3 vs. YQS tertiles-of-5 CQS YQS Relative OPT 0.536 0.538 +0.4% 1-bit 0.686 0.687 +0.1% 2-bit sc 0.611 0.613 +0.3% 2-bit fc 0.627 0.629 +0.3% ·n ln n + O(n) essentially same number of BM. Branch misses not a plausible explanation for YQS’s success. Sebastian Wild Branch Misses in Quicksort 2015-01-04 14 / 15
  115. 115. Results CQS vs. YQS Original question: Does YQS better than CQS w. r. t. branch misses? Expected number of branch misses without pivot sampling CQS YQS Relative OPT 0.5 0.513 +2.6% 1-bit 0.6 0.673 +1.0% 2-bit sc 0.571 0.585 +2.5% 2-bit fc 0.589 0.602 +2.2% ·n ln n + O(n) CQS median-of-3 vs. YQS tertiles-of-5 CQS YQS Relative OPT 0.536 0.538 +0.4% 1-bit 0.686 0.687 +0.1% 2-bit sc 0.611 0.613 +0.3% 2-bit fc 0.627 0.629 +0.3% ·n ln n + O(n) essentially same number of BM. Branch misses not a plausible explanation for YQS’s success. Sebastian Wild Branch Misses in Quicksort 2015-01-04 14 / 15
  116. 116. Results CQS vs. YQS Original question: Does YQS better than CQS w. r. t. branch misses? Expected number of branch misses without pivot sampling CQS YQS Relative OPT 0.5 0.513 +2.6% 1-bit 0.6 0.673 +1.0% 2-bit sc 0.571 0.585 +2.5% 2-bit fc 0.589 0.602 +2.2% ·n ln n + O(n) CQS median-of-3 vs. YQS tertiles-of-5 CQS YQS Relative OPT 0.536 0.538 +0.4% 1-bit 0.686 0.687 +0.1% 2-bit sc 0.611 0.613 +0.3% 2-bit fc 0.627 0.629 +0.3% ·n ln n + O(n) essentially same number of BM. Branch misses not a plausible explanation for YQS’s success. Sebastian Wild Branch Misses in Quicksort 2015-01-04 14 / 15
  117. 117. Results CQS vs. YQS Original question: Does YQS better than CQS w. r. t. branch misses? Expected number of branch misses without pivot sampling CQS YQS Relative OPT 0.5 0.513 +2.6% 1-bit 0.6 0.673 +1.0% 2-bit sc 0.571 0.585 +2.5% 2-bit fc 0.589 0.602 +2.2% ·n ln n + O(n) CQS median-of-3 vs. YQS tertiles-of-5 CQS YQS Relative OPT 0.536 0.538 +0.4% 1-bit 0.686 0.687 +0.1% 2-bit sc 0.611 0.613 +0.3% 2-bit fc 0.627 0.629 +0.3% ·n ln n + O(n) essentially same number of BM. Branch misses not a plausible explanation for YQS’s success. Sebastian Wild Branch Misses in Quicksort 2015-01-04 14 / 15
  118. 118. Results CQS vs. YQS Original question: Does YQS better than CQS w. r. t. branch misses? Expected number of branch misses without pivot sampling CQS YQS Relative OPT 0.5 0.513 +2.6% 1-bit 0.6 0.673 +1.0% 2-bit sc 0.571 0.585 +2.5% 2-bit fc 0.589 0.602 +2.2% ·n ln n + O(n) CQS median-of-3 vs. YQS tertiles-of-5 CQS YQS Relative OPT 0.536 0.538 +0.4% 1-bit 0.686 0.687 +0.1% 2-bit sc 0.611 0.613 +0.3% 2-bit fc 0.627 0.629 +0.3% ·n ln n + O(n) essentially same number of BM. Branch misses not a plausible explanation for YQS’s success. Sebastian Wild Branch Misses in Quicksort 2015-01-04 14 / 15
  119. 119. Conclusion Precise analysis of branch misses in Quicksort (CQS and YQS) including pivot sampling lower bounds on branch miss rates CQS and YQS cause very similar number of BM Strengthened evidence for the hypothesis that YQS is faster because of better usage of memory hierarchy. Sebastian Wild Branch Misses in Quicksort 2015-01-04 15 / 15
  120. 120. Conclusion Precise analysis of branch misses in Quicksort (CQS and YQS) including pivot sampling lower bounds on branch miss rates CQS and YQS cause very similar number of BM Strengthened evidence for the hypothesis that YQS is faster because of better usage of memory hierarchy. Sebastian Wild Branch Misses in Quicksort 2015-01-04 15 / 15
  121. 121. Conclusion Precise analysis of branch misses in Quicksort (CQS and YQS) including pivot sampling lower bounds on branch miss rates CQS and YQS cause very similar number of BM Strengthened evidence for the hypothesis that YQS is faster because of better usage of memory hierarchy. Sebastian Wild Branch Misses in Quicksort 2015-01-04 15 / 15
  122. 122. Miss Rate for Branches in Quicksort without sampling: P D = Uniform(0, 1) E[fOPT(P)] = ˆ 1 0 min{p, 1 − p} dp E[f1-bit(P)] = ˆ 1 0 2p(1 − p) dp E[f2-bit-sc(P)] = ˆ 1 0 p(1 − p) 1 − 2p(1 − p) dp = π 4 − 1 2 ≈ 0.285 E[f2-bit-fc(P)] = ˆ 1 0 2p2 (1 − p)2 + p(1 − p) 1 − 2p(1 − p) dp = 2π √ 3 − 10 3 ≈ 0.294 Sebastian Wild Branch Misses in Quicksort 2015-01-04 16 / 15
  123. 123. Miss Rate for Branches in Quicksort without sampling: P D = Uniform(0, 1) E[fOPT(P)] = ˆ 1 0 min{p, 1 − p} dp E[f1-bit(P)] = ˆ 1 0 2p(1 − p) dp E[f2-bit-sc(P)] = ˆ 1 0 p(1 − p) 1 − 2p(1 − p) dp = π 4 − 1 2 ≈ 0.285 E[f2-bit-fc(P)] = ˆ 1 0 2p2 (1 − p)2 + p(1 − p) 1 − 2p(1 − p) dp = 2π √ 3 − 10 3 ≈ 0.294 Sebastian Wild Branch Misses in Quicksort 2015-01-04 16 / 15
  124. 124. Miss Rate for Branches in Quicksort without sampling: P D = Uniform(0, 1) E[fOPT(P)] = ˆ 1 0 min{p, 1 − p} dp = 0.25 E[f1-bit(P)] = ˆ 1 0 2p(1 − p) dp E[f2-bit-sc(P)] = ˆ 1 0 p(1 − p) 1 − 2p(1 − p) dp = π 4 − 1 2 ≈ 0.285 E[f2-bit-fc(P)] = ˆ 1 0 2p2 (1 − p)2 + p(1 − p) 1 − 2p(1 − p) dp = 2π √ 3 − 10 3 ≈ 0.294 Sebastian Wild Branch Misses in Quicksort 2015-01-04 16 / 15
  125. 125. Miss Rate for Branches in Quicksort without sampling: P D = Uniform(0, 1) E[fOPT(P)] = ˆ 1 0 min{p, 1 − p} dp = 0.25 E[f1-bit(P)] = ˆ 1 0 2p(1 − p) dp E[f2-bit-sc(P)] = ˆ 1 0 p(1 − p) 1 − 2p(1 − p) dp = π 4 − 1 2 ≈ 0.285 E[f2-bit-fc(P)] = ˆ 1 0 2p2 (1 − p)2 + p(1 − p) 1 − 2p(1 − p) dp = 2π √ 3 − 10 3 ≈ 0.294 Sebastian Wild Branch Misses in Quicksort 2015-01-04 16 / 15
  126. 126. Miss Rate for Branches in Quicksort without sampling: P D = Uniform(0, 1) E[fOPT(P)] = ˆ 1 0 min{p, 1 − p} dp = 0.25 E[f1-bit(P)] = ˆ 1 0 2p(1 − p) dp = 0.3 E[f2-bit-sc(P)] = ˆ 1 0 p(1 − p) 1 − 2p(1 − p) dp = π 4 − 1 2 ≈ 0.285 E[f2-bit-fc(P)] = ˆ 1 0 2p2 (1 − p)2 + p(1 − p) 1 − 2p(1 − p) dp = 2π √ 3 − 10 3 ≈ 0.294 Sebastian Wild Branch Misses in Quicksort 2015-01-04 16 / 15
  127. 127. Miss Rate for Branches in Quicksort without sampling: P D = Uniform(0, 1) E[fOPT(P)] = ˆ 1 0 min{p, 1 − p} dp = 0.25 E[f1-bit(P)] = ˆ 1 0 2p(1 − p) dp = 0.3 E[f2-bit-sc(P)] = ˆ 1 0 p(1 − p) 1 − 2p(1 − p) dp = π 4 − 1 2 ≈ 0.285 E[f2-bit-fc(P)] = ˆ 1 0 2p2 (1 − p)2 + p(1 − p) 1 − 2p(1 − p) dp = 2π √ 3 − 10 3 ≈ 0.294 Sebastian Wild Branch Misses in Quicksort 2015-01-04 16 / 15
  128. 128. Miss Rate for Branches in Quicksort without sampling: P D = Uniform(0, 1) E[fOPT(P)] = ˆ 1 0 min{p, 1 − p} dp = 0.25 E[f1-bit(P)] = ˆ 1 0 2p(1 − p) dp = 0.3 E[f2-bit-sc(P)] = ˆ 1 0 p(1 − p) 1 − 2p(1 − p) dp = π 4 − 1 2 ≈ 0.285 E[f2-bit-fc(P)] = ˆ 1 0 2p2 (1 − p)2 + p(1 − p) 1 − 2p(1 − p) dp = 2π √ 3 − 10 3 ≈ 0.294 Sebastian Wild Branch Misses in Quicksort 2015-01-04 16 / 15

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