Caches in WCET Analysis

Transcription

1 Caches in WCET Analysis Jan Reineke Department of Computer Science Saarland University Saarbrücken, Germany ARTIST Summer School in Europe 2009 Autrans, France September 7-11, 2009 Jan Reineke Caches in WCET Analysis ARTIST Summer School / 51

2 Outline 1 Caches 2 Cache Analysis for Least-Recently-Used 3 Beyond Least-Recently-Used Predictability Metrics Relative Competitiveness Sensitivity Caches and Measurement-Based Timing Analysis 4 Summary Jan Reineke Caches in WCET Analysis ARTIST Summer School / 51

3 Outline 1 Caches 2 Cache Analysis for Least-Recently-Used 3 Beyond Least-Recently-Used Predictability Metrics Relative Competitiveness Sensitivity Caches and Measurement-Based Timing Analysis 4 Summary Jan Reineke Caches in WCET Analysis ARTIST Summer School / 51

4 Caches How they work: dynamically managed by replacement policy hit [ab] CPU Cache Main Memory Capacity: Latency: 32 KB 3 cycles 2 MB 100 cycles Why they work: principle of locality spatial temporal Jan Reineke Caches in WCET Analysis ARTIST Summer School / 51

5 Caches How they work: dynamically managed by replacement policy hit [ab] a 1 CPU Cache Main Memory Capacity: Latency: 32 KB 3 cycles 2 MB 100 cycles Why they work: principle of locality spatial temporal Jan Reineke Caches in WCET Analysis ARTIST Summer School / 51

6 Caches How they work: dynamically managed by replacement policy a 1! hit [ab] CPU Cache Main Memory Capacity: Latency: 32 KB 3 cycles 2 MB 100 cycles Why they work: principle of locality spatial temporal Jan Reineke Caches in WCET Analysis ARTIST Summer School / 51

7 Caches How they work: dynamically managed by replacement policy c 3? miss [ab] CPU Cache Main Memory Capacity: Latency: 32 KB 3 cycles 2 MB 100 cycles Why they work: principle of locality spatial temporal Jan Reineke Caches in WCET Analysis ARTIST Summer School / 51

8 Caches How they work: dynamically managed by replacement policy miss [ab] c 3? c? CPU Cache Main Memory Capacity: Latency: 32 KB 3 cycles 2 MB 100 cycles Why they work: principle of locality spatial temporal Jan Reineke Caches in WCET Analysis ARTIST Summer School / 51

9 Caches How they work: dynamically managed by replacement policy miss [ac] c 3? c = c 1 c 2 c 3 c 4! CPU Cache Main Memory Capacity: Latency: 32 KB 3 cycles 2 MB 100 cycles Why they work: principle of locality spatial temporal Jan Reineke Caches in WCET Analysis ARTIST Summer School / 51

10 Caches How they work: dynamically managed by replacement policy c 3! miss [ac] CPU Cache Main Memory Capacity: Latency: 32 KB 3 cycles 2 MB 100 cycles Why they work: principle of locality spatial temporal Jan Reineke Caches in WCET Analysis ARTIST Summer School / 51

11 Caches How they work: dynamically managed by replacement policy c 4? hit [ac] CPU Cache Main Memory Capacity: Latency: 32 KB 3 cycles 2 MB 100 cycles Why they work: principle of locality spatial temporal Jan Reineke Caches in WCET Analysis ARTIST Summer School / 51

12 Caches How they work: dynamically managed by replacement policy c 4! hit [ac] CPU Cache Main Memory Capacity: Latency: 32 KB 3 cycles 2 MB 100 cycles Why they work: principle of locality spatial temporal Jan Reineke Caches in WCET Analysis ARTIST Summer School / 51

13 Fully-Associative Caches log2 (s) Address: log2 (8 b) Block offset Tag Tag Tag... Data Block Data Block Tag Data Block =? Yes: Hit! No: Miss! Jan Reineke Caches in WCET Analysis k = associativity MUX Data ARTIST Summer School / 51 B

14 k s Tag Index Block offset Cache Set: Tag Tag log2 (s) Special cases: log log22(s) (8 b)log2 (8 b) log2 (s) Address: s Set-Associative Caches Tag... Cache Set: Data Block Data Block Data Block log2 (8 b)... s Tag Tag... Data Block Data Block Tag Data Block =? Yes: Hit! No: Miss! k MUX Data direct-mapped cache: only one line per cache set fully-associative cache: only one cache set Jan Reineke Caches in WCET Analysis ARTIST Summer School / 51

15 Cache Replacement Policies Least-Recently-Used (LRU) used in INTEL PENTIUM I and MIPS 24K/34K First-In First-Out (FIFO or Round-Robin) used in MOTOROLA POWERPC 56X, INTEL XSCALE, ARM9, ARM11 Pseudo-LRU (PLRU) used in INTEL PENTIUM II-IV and POWERPC 75X Most Recently Used (MRU) as described in literature Each cache set is treated independently: Set-associative caches are compositions of fully-associative caches. Jan Reineke Caches in WCET Analysis ARTIST Summer School / 51

16 Outline 1 Caches 2 Cache Analysis for Least-Recently-Used 3 Beyond Least-Recently-Used Predictability Metrics Relative Competitiveness Sensitivity Caches and Measurement-Based Timing Analysis 4 Summary Jan Reineke Caches in WCET Analysis ARTIST Summer School / 51

17 Cache Analysis Two types of cache analyses: 1 Local guarantees: classification of individual accesses May-Analysis Overapproximates cache contents Must-Analysis Underapproximates cache contents 2 Global guarantees: bounds on cache hits/misses Cache analyses almost exclusively for LRU In practice: FIFO, PLRU,... Jan Reineke Caches in WCET Analysis ARTIST Summer School / 51

18 Challenges for Cache Analysis Always a cache hit/always a miss? read z read y read x write z Jan Reineke Caches in WCET Analysis ARTIST Summer School / 51

19 Challenges for Cache Analysis Always a cache hit/always a miss? read z read y read x 1. Initial cache contents unknown. 2. Different paths lead to these points. write z 3. Cannot resolve address of z. Jan Reineke Caches in WCET Analysis ARTIST Summer School / 51

20 Deriving Invariants about Cache States using Abstract Interpretation read z Collecting Semantics = set of states at each program point that any execution may encounter there read y read x Two approximations: Collecting Semantics uncomputable write z Cache Semantics computable γ(abstract Cache Sem.) efficiently computable Jan Reineke Caches in WCET Analysis ARTIST Summer School / 51

21 Deriving Invariants about Cache States using Abstract Interpretation read z Collecting Semantics = set of states at each program point that any execution may encounter there read y read x Two approximations: Collecting Semantics uncomputable write z Cache Semantics computable γ(abstract Cache Sem.) efficiently computable Jan Reineke Caches in WCET Analysis ARTIST Summer School / 51

22 Deriving Invariants about Cache States using Abstract Interpretation read z Collecting Semantics = set of states at each program point that any execution may encounter there read y read x Two approximations: Collecting Semantics uncomputable write z Cache Semantics computable γ(abstract Cache Sem.) efficiently computable Jan Reineke Caches in WCET Analysis ARTIST Summer School / 51

23 Deriving Invariants about Cache States using Abstract Interpretation read z Collecting Semantics = set of states at each program point that any execution may encounter there read y read x Two approximations: Collecting Semantics uncomputable write z Cache Semantics computable γ(abstract Cache Sem.) efficiently computable Jan Reineke Caches in WCET Analysis ARTIST Summer School / 51

24 Deriving Invariants about Cache States using Abstract Interpretation read z Collecting Semantics = set of states at each program point that any execution may encounter there read y read x Two approximations: Collecting Semantics uncomputable write z Cache Semantics computable γ(abstract Cache Sem.) efficiently computable Jan Reineke Caches in WCET Analysis ARTIST Summer School / 51

25 Least-Recently-Used (LRU): Concrete Behavior s Cache Miss : z y x t s z y x LRU has notion of age s Cache Hit : z y s t s z y t Jan Reineke Caches in WCET Analysis ARTIST Summer School / 51

26 LRU: Must-Analysis: Abstract Domain Used to predict cache hits. Maintains upper bounds on ages of memory blocks. Upper bound associativity memory block definitely cached. Example Abstract state: {x} age 0 {} {s,t} {} age 3... and its interpretation: Describes the set of all concrete cache states in which x, s, and t occur, x with an age of 0, s and t with an age not older than 2. γ([{x}, {}, {s, t}, {}]) = {[x, s, t, a], [x, t, s, a], [x, s, t, b],...} Jan Reineke Caches in WCET Analysis ARTIST Summer School / 51

27 Sound Update Local Consistency Abstract Update (must) (must ) γ Lifted Concrete Update γ concrete cache states concrete cache states Jan Reineke Caches in WCET Analysis ARTIST Summer School / 51

28 LRU: Must-Analysis: Update Potential Cache Miss : Definite Cache Hit : {x} {} {s,t} {} {x} {} {s,t} {} z s {z} {x} {} {s,t} {s} {x} {t} {} Why does t not age in the second case? Jan Reineke Caches in WCET Analysis ARTIST Summer School / 51

29 LRU: Must-Analysis: Join Need to combine information where control-flow merges. Join should be conservative: γ(a) γ(a B) γ(b) γ(a B) {a} {c} {} {} {c,f} {e} {a} {} {a,c} {d} {d} {d} Intersection + Maximal Age Jan Reineke Caches in WCET Analysis ARTIST Summer School / 51

30 LRU: Must-Analysis: Join Need to combine information where control-flow merges. Join should be conservative: γ(a) γ(a B) γ(b) γ(a B) {a} {c} {} {} {c,f} {e} {a} {} {a,c} {d} {d} {d} Intersection + Maximal Age Jan Reineke Caches in WCET Analysis ARTIST Summer School / 51

31 LRU: Must-Analysis: Join Need to combine information where control-flow merges. Join should be conservative: γ(a) γ(a B) γ(b) γ(a B) {a} {c} {} {} {c,f} {e} {a} {} {a,c} {d} {d} {d} Intersection + Maximal Age Jan Reineke Caches in WCET Analysis ARTIST Summer School / 51

32 LRU: Must-Analysis: Join Need to combine information where control-flow merges. Join should be conservative: γ(a) γ(a B) γ(b) γ(a B) {a} {c} {} {} {c,f} {e} {a} {} {a,c} {d} {d} {d} Intersection + Maximal Age Jan Reineke Caches in WCET Analysis ARTIST Summer School / 51

33 LRU: Must-Analysis: Join Need to combine information where control-flow merges. Join should be conservative: γ(a) γ(a B) γ(b) γ(a B) {a} {c} {} {} {c,f} {e} {a} {} {a,c} {d} {d} {d} Intersection + Maximal Age Jan Reineke Caches in WCET Analysis ARTIST Summer School / 51

34 LRU: Must-Analysis: Join Need to combine information where control-flow merges. Join should be conservative: γ(a) γ(a B) γ(b) γ(a B) {a} {c} {} {} {c,f} {e} {a} {} {a,c} {d} {d} {d} Intersection + Maximal Age Jan Reineke Caches in WCET Analysis ARTIST Summer School / 51

35 LRU: Must-Analysis: Join Need to combine information where control-flow merges. Join should be conservative: γ(a) γ(a B) γ(b) γ(a B) {a} {c} {} {} {c,f} {e} {a} {} {a,c} {d} {d} {d} Intersection + Maximal Age How many memory blocks can be in the must-cache? Jan Reineke Caches in WCET Analysis ARTIST Summer School / 51

36 Example: Must-Analysis entry [{}, {}, {}, {}] A B C D exit Jan Reineke Caches in WCET Analysis ARTIST Summer School / 51

37 Example: Must-Analysis entry [{}, {}, {}, {}] A [{}, {}, {}, {}] = [{}, {}, {}, {}] B C D exit Jan Reineke Caches in WCET Analysis ARTIST Summer School / 51

38 Example: Must-Analysis entry [{}, {}, {}, {}] A [{}, {}, {}, {}] = [{}, {}, {}, {}] [{A}, {}, {}, {}] B C [{A}, {}, {}, {}] D exit Jan Reineke Caches in WCET Analysis ARTIST Summer School / 51

39 Example: Must-Analysis entry [{}, {}, {}, {}] A [{}, {}, {}, {}] = [{}, {}, {}, {}] [{A}, {}, {}, {}] B C [{A}, {}, {}, {}] D [{B}, {A}, {}, {}] [{C}, {A}, {}, {}] = [{}, {A}, {}, {}] exit Jan Reineke Caches in WCET Analysis ARTIST Summer School / 51

40 Example: Must-Analysis entry [{}, {}, {}, {}] A [{D}, {}, {A}, {}] [{}, {}, {}, {}] = [{}, {}, {}, {}] [{A}, {}, {}, {}] B C [{A}, {}, {}, {}] D [{B}, {A}, {}, {}] [{C}, {A}, {}, {}] = [{}, {A}, {}, {}] exit [{D}, {}, {A}, {}] No cache hits can be predicted :-( Jan Reineke Caches in WCET Analysis ARTIST Summer School / 51

41 Context-Sensitive Analysis/Virtual Loop-Unrolling Problem: The first iteration of a loop will always result in cache misses. Similarly for the first execution of a function. Solution: Virtually Unroll Loops: Distinguish the first iteration from others Distinguish function calls by calling context. entry Virtually unrolling the loop once: Accesses to A and D are provably hits after the first iteration Accesses to B and C can still not be classified. Within each execution of the loop, they may only miss once. Persistence Analysis [{A}, {}, {}, {}] B exit [{A}, {D}, {}, {}] B A D A [{}, {}, {}, {}] [{A}, {}, {}, {}] C [{}, {A}, {}, {}] [{D}, {}, {A}, {}] [{A}, {D}, {}, {}] C D [{}, {A}, {D}, {}] exit Jan Reineke Caches in WCET Analysis ARTIST Summer School / 51

42 LRU: May-Analysis: Abstract Domain Used to predict cache misses. Maintains lower bounds on ages of memory blocks. Lower bound associativity memory block definitely not cached. Example Abstract state: {x,y} age 0 {} {s,t} {u} age 3... and its interpretation: Describes the set of all concrete cache states in which no memory blocks except x, y, s, t, and u occur, x and y with an age of at least 0, s and t with an age of at least 2, u with an age of at least 3. γ([{x, y}, {}, {s, t}, {u}]) = {[x, y, s, t], [y, x, s, t], [x, y, s, u],...} Jan Reineke Caches in WCET Analysis ARTIST Summer School / 51

43 LRU: May-Analysis: Update Definite Cache Miss : Potential Cache Hit : {x} {} {s,t} {y} {x} {} {s,t} {y} z s {z} {x} {} {s,t} {s} {x} {} {y,t} Why does t age in the second case? Jan Reineke Caches in WCET Analysis ARTIST Summer School / 51

44 LRU: May-Analysis: Join Need to combine information where control-flow merges. Join should be conservative: γ(a) γ(a B) γ(b) γ(a B) {a} {c} {a,c} {} {c,f} {e} {a} = {e} {f} {d} {d} {d} Union + Minimal Age Jan Reineke Caches in WCET Analysis ARTIST Summer School / 51

45 LRU: May-Analysis: Join Need to combine information where control-flow merges. Join should be conservative: γ(a) γ(a B) γ(b) γ(a B) {a} {c} {a,c} {} {c,f} {e} {a} = {e} {f} {d} {d} {d} Union + Minimal Age Jan Reineke Caches in WCET Analysis ARTIST Summer School / 51

46 LRU: May-Analysis: Join Need to combine information where control-flow merges. Join should be conservative: γ(a) γ(a B) γ(b) γ(a B) {a} {c} {a,c} {} {c,f} {e} {a} = {e} {f} {d} {d} {d} Union + Minimal Age Jan Reineke Caches in WCET Analysis ARTIST Summer School / 51

47 LRU: May-Analysis: Join Need to combine information where control-flow merges. Join should be conservative: γ(a) γ(a B) γ(b) γ(a B) {a} {c} {a,c} {} {c,f} {e} {a} = {e} {f} {d} {d} {d} Union + Minimal Age Jan Reineke Caches in WCET Analysis ARTIST Summer School / 51

48 LRU: May-Analysis: Join Need to combine information where control-flow merges. Join should be conservative: γ(a) γ(a B) γ(b) γ(a B) {a} {c} {a,c} {} {c,f} {e} {a} = {e} {f} {d} {d} {d} Union + Minimal Age Jan Reineke Caches in WCET Analysis ARTIST Summer School / 51

49 LRU: May-Analysis: Join Need to combine information where control-flow merges. Join should be conservative: γ(a) γ(a B) γ(b) γ(a B) {a} {c} {a,c} {} {c,f} {e} {a} = {e} {f} {d} {d} {d} Union + Minimal Age Jan Reineke Caches in WCET Analysis ARTIST Summer School / 51

50 Outline 1 Caches 2 Cache Analysis for Least-Recently-Used 3 Beyond Least-Recently-Used Predictability Metrics Relative Competitiveness Sensitivity Caches and Measurement-Based Timing Analysis 4 Summary Jan Reineke Caches in WCET Analysis ARTIST Summer School / 51

51 Outline 1 Caches 2 Cache Analysis for Least-Recently-Used 3 Beyond Least-Recently-Used Predictability Metrics Relative Competitiveness Sensitivity Caches and Measurement-Based Timing Analysis 4 Summary Jan Reineke Caches in WCET Analysis ARTIST Summer School / 51

52 Uncertainty in WCET Analysis Amount of uncertainty determines precision of WCET analysis Uncertainty in cache analysis depends on replacement policy variation due to inputs and initial hardware state uncertainty penalty BCET ACET WCET upper bound execution time Jan Reineke Caches in WCET Analysis ARTIST Summer School / 51

53 Uncertainty in Cache Analysis read z read y read x write z Jan Reineke Caches in WCET Analysis ARTIST Summer School / 51

54 Uncertainty in Cache Analysis read z 1. Initial cache contents unknown. read y read x write z Jan Reineke Caches in WCET Analysis ARTIST Summer School / 51

55 Uncertainty in Cache Analysis read z 1. Initial cache contents unknown. read y read x 2. Need to combine information. write z Jan Reineke Caches in WCET Analysis ARTIST Summer School / 51

56 Uncertainty in Cache Analysis read z 1. Initial cache contents unknown. read y read x 2. Need to combine information. 3. Cannot resolve address of z. write z Jan Reineke Caches in WCET Analysis ARTIST Summer School / 51

57 Uncertainty in Cache Analysis read z 1. Initial cache contents unknown. read y read x 2. Need to combine information. 3. Cannot resolve address of z. write z = Amount of uncertainty determined by ability to recover information Jan Reineke Caches in WCET Analysis ARTIST Summer School / 51

58 Predictability Metrics Evict Fill [fed] [gfe] [dex] [fec] [fde] [gfd] [hgf ] Sequence: a,..., e, f, g, h Jan Reineke Caches in WCET Analysis ARTIST Summer School / 51

59 Meaning of Metrics Evict Number of accesses to obtain any may-information. I.e. when can an analysis predict any cache misses? Fill Number of accesses to complete may- and must-information. I.e. when can an analysis predict each access? Evict and Fill bound the precision of any static cache analysis. Can thus serve as a benchmark for analyses. Jan Reineke Caches in WCET Analysis ARTIST Summer School / 51

60 Evaluation of Least-Recently-Used LRU forgets about past quickly: cares about most-recent access to each block only order of previous accesses irrelevant???? a a??? b b a?? c c b a? d d c b a In the example: Evict = Fill = 4 In general: Evict(k) = Fill(k) = k, where k is the associativity of the cache Jan Reineke Caches in WCET Analysis ARTIST Summer School / 51

61 Evaluation of First-In First-Out (sketch) Like LRU in the miss-case But: Ignores hits? a b c a? a b c b? a b c c? a b c d d? a b In the worst-case k 1 hits and k misses: Evict(k) = 2k 1 Another k accesses to obtain complete knowledge: Fill(k) = 3k 1 (k = associativity) Jan Reineke Caches in WCET Analysis ARTIST Summer School / 51

62 Evaluation of Pseudo-LRU (sketch) Tree-bits point to block to be replaced a b c d c a b c d e a e c d Accesses rejuvenate neighborhood Active blocks keep their (inactive) neighborhood in the cache Analysis yields: Evict(k) = k 2 log 2 k + 1 Fill(k) = k 2 log 2 k + k 1 Jan Reineke Caches in WCET Analysis ARTIST Summer School / 51

63 Evaluation of Policies Policy Evict(k) Fill(k) Evict(8) Fill(8) LRU k k 8 8 FIFO 2k 1 3k MRU 2k 2 /3k 4 14 /20 PLRU k log 2 2 k + 1 k log 2 2 k + k LRU is optimal w.r.t. metrics. Other policies are much less predictable. Use LRU if predictability is a concern. How to obtain may- and must-information within the given limits for other policies? Jan Reineke Caches in WCET Analysis ARTIST Summer School / 51

64 Outline 1 Caches 2 Cache Analysis for Least-Recently-Used 3 Beyond Least-Recently-Used Predictability Metrics Relative Competitiveness Sensitivity Caches and Measurement-Based Timing Analysis 4 Summary Jan Reineke Caches in WCET Analysis ARTIST Summer School / 51

65 Relative Competitiveness Competitiveness (Sleator and Tarjan, 1985): worst-case performance of an online policy relative to the optimal offline policy used to evaluate online policies Relative competitiveness (Reineke and Grund, 2008): worst-case performance of an online policy relative to another online policy used to derive local and global cache analyses Jan Reineke Caches in WCET Analysis ARTIST Summer School / 51

66 Definition Relative Miss-Competitiveness Notation m P (p, s) = number of misses that policy P incurs on access sequence s M starting in state p C P Jan Reineke Caches in WCET Analysis ARTIST Summer School / 51

67 Definition Relative Miss-Competitiveness Notation m P (p, s) = number of misses that policy P incurs on access sequence s M starting in state p C P Definition (Relative miss competitiveness) Policy P is (k, c)-miss-competitive relative to policy Q if m P (p, s) k m Q (q, s) + c for all access sequences s M and cache-set states p C P, q C Q that are compatible p q. Jan Reineke Caches in WCET Analysis ARTIST Summer School / 51

68 Definition Relative Miss-Competitiveness Notation m P (p, s) = number of misses that policy P incurs on access sequence s M starting in state p C P Definition (Relative miss competitiveness) Policy P is (k, c)-miss-competitive relative to policy Q if m P (p, s) k m Q (q, s) + c for all access sequences s M and cache-set states p C P, q C Q that are compatible p q. Definition (Competitive miss ratio of P relative to Q) The smallest k, s.t. P is (k, c)-miss-competitive rel. to Q for some c. Jan Reineke Caches in WCET Analysis ARTIST Summer School / 51

69 Example Relative Miss-Competitiveness P is (3, 4)-miss-competitive relative to Q. If Q incurs x misses, then P incurs at most 3 x + 4 misses. Jan Reineke Caches in WCET Analysis ARTIST Summer School / 51

70 Example Relative Miss-Competitiveness P is (3, 4)-miss-competitive relative to Q. If Q incurs x misses, then P incurs at most 3 x + 4 misses. Best: P is (1, 0)-miss-competitive relative to Q. Jan Reineke Caches in WCET Analysis ARTIST Summer School / 51

71 Example Relative Miss-Competitiveness P is (3, 4)-miss-competitive relative to Q. If Q incurs x misses, then P incurs at most 3 x + 4 misses. Best: P is (1, 0)-miss-competitive relative to Q. Worst: P is not-miss-competitive (or -miss-competitive) relative to Q. Jan Reineke Caches in WCET Analysis ARTIST Summer School / 51

72 Example Relative Hit-Competitiveness P is ( 2 3, 3)-hit-competitive relative to Q. If Q has x hits, then P has at least 2 3 x 3 hits. Jan Reineke Caches in WCET Analysis ARTIST Summer School / 51

73 Example Relative Hit-Competitiveness P is ( 2 3, 3)-hit-competitive relative to Q. If Q has x hits, then P has at least 2 3 x 3 hits. Best: P is (1, 0)-hit-competitive relative to Q. Equivalent to (1, 0)-miss-competitiveness. Jan Reineke Caches in WCET Analysis ARTIST Summer School / 51

74 Example Relative Hit-Competitiveness P is ( 2 3, 3)-hit-competitive relative to Q. If Q has x hits, then P has at least 2 3 x 3 hits. Best: P is (1, 0)-hit-competitive relative to Q. Equivalent to (1, 0)-miss-competitiveness. Worst: P is (0, 0)-hit-competitive relative to Q. Analogue to -miss-competitiveness. Jan Reineke Caches in WCET Analysis ARTIST Summer School / 51

75 Local Guarantees: (1, 0)-Competitiveness Let P be (1, 0)-competitive relative to Q: m P (p, s) 1 m Q (q, s) + 0 m P (p, s) m Q (q, s) Jan Reineke Caches in WCET Analysis ARTIST Summer School / 51

76 Local Guarantees: (1, 0)-Competitiveness Let P be (1, 0)-competitive relative to Q: m P (p, s) 1 m Q (q, s) + 0 m P (p, s) m Q (q, s) 1 If Q hits, so does P, and 2 if P misses, so does Q. Jan Reineke Caches in WCET Analysis ARTIST Summer School / 51

77 Local Guarantees: (1, 0)-Competitiveness Let P be (1, 0)-competitive relative to Q: m P (p, s) 1 m Q (q, s) + 0 m P (p, s) m Q (q, s) 1 If Q hits, so does P, and 2 if P misses, so does Q. As a consequence, 1 a must-analysis for Q is also a must-analysis for P, and 2 a may-analysis for P is also a may-analysis for Q. Jan Reineke Caches in WCET Analysis ARTIST Summer School / 51

78 Global Guarantees: (k, c)-competitiveness Given: Global guarantees for policy Q. Wanted: Global guarantees for policy P. Jan Reineke Caches in WCET Analysis ARTIST Summer School / 51

79 Global Guarantees: (k, c)-competitiveness Given: Global guarantees for policy Q. Wanted: Global guarantees for policy P. 1 Determine competitiveness of policy P relative to policy Q. m P k m Q + c m Q (T) = m P (T) Jan Reineke Caches in WCET Analysis ARTIST Summer School / 51

80 Global Guarantees: (k, c)-competitiveness Given: Global guarantees for policy Q. Wanted: Global guarantees for policy P. 1 Determine competitiveness of policy P relative to policy Q. m P k m Q + c m Q (T) = m P (T) 2 Compute global guarantee for task T under policy Q. m P k m Q + c m Q (T) = m P (T) Jan Reineke Caches in WCET Analysis ARTIST Summer School / 51

81 Global Guarantees: (k, c)-competitiveness Given: Global guarantees for policy Q. Wanted: Global guarantees for policy P. 1 Determine competitiveness of policy P relative to policy Q. m P k m Q + c m Q (T) = m P (T) 2 Compute global guarantee for task T under policy Q. m P k m Q + c m Q (T) = m P (T) 3 Calculate global guarantee on the number of misses for P using the global guarantee for Q and the competitiveness results of P relative to Q. m P k m Q + c m Q (T) = m P (T) Jan Reineke Caches in WCET Analysis ARTIST Summer School / 51

82 Relative Competitiveness: Automatic Computation P and Q (here: FIFO and LRU) induce transition system: (h, h) e first-in last-in MRU LRU [eabc] FIFO, [eabc] LRU e (m, m) [abcd] FIFO, [abcd] LRU (h, h) a Legend c (h, h) [eabc] FIFO, [ceab] LRU d (h, h) [abcd] FIFO, [dabc] LRU [abcd] FIFO d (h, m),... Cache-set state Memory access Misses in pairs of cache-set states e (m, m) [eabc] FIFO, [ceda] LRU c (h, m) [eabc] FIFO, [edab] LRU d (m, h) [deab] FIFO, [deab] LRU Competitive miss ratio = maximum ratio of misses in policy P to misses in policy Q in transition system Jan Reineke Caches in WCET Analysis ARTIST Summer School / 51

83 Transition System is Large Problem: The induced transition system is large. Observation: Only the relative positions of elements matter: [abc] LRU, [bde] FIFO [fgl] LRU, [ghm] FIFO c (h, m) l (h, m) [cab] LRU, [cbd] FIFO [lfg] LRU, [lgh] FIFO Solution: Construct finite quotient transition system. Jan Reineke Caches in WCET Analysis ARTIST Summer School / 51

84 -Equivalent States in Running Example (h, h) e [eabc] FIFO, [eabc] LRU e (m, m) [abcd] FIFO, [abcd] LRU (h, h) a c (h, h) d (h, h) [eabc] FIFO, [ceab] LRU [abcd] FIFO, [dabc] LRU e (m, m) [eabc] FIFO, [ceda] LRU c (h, m) [eabc] FIFO, [edab] LRU d (m, h) [deab] FIFO, [deab] LRU Jan Reineke Caches in WCET Analysis ARTIST Summer School / 51

85 Finite Quotient Transition System Merging -equivalent states yields a finite quotient transition system: (h, h) (m, m) [abcd] FIFO, [abcd] LRU (h, h) [abcd] FIFO, [dabc] LRU (m, h) (m, m) [eabc] FIFO, [ceda] LRU (h, m) [eabc] FIFO, [edab] LRU Jan Reineke Caches in WCET Analysis ARTIST Summer School / 51

86 Competitive Ratio = Maximum Cycle Ratio Competitive miss ratio = maximum ratio of misses in policy P to misses in policy Q (0, 0) (1, 1) (0, 0) (1, 0) (1, 1) (0, 1) Jan Reineke Caches in WCET Analysis ARTIST Summer School / 51

87 Competitive Ratio = Maximum Cycle Ratio Competitive miss ratio = maximum ratio of misses in policy P to misses in policy Q (0, 0) (1, 1) (0, 0) (1, 0) (1, 1) (0, 1) Maximum cycle ratio = = 2 Jan Reineke Caches in WCET Analysis ARTIST Summer School / 51

88 Tool Implementation Implemented in Java, called Relacs Interface for replacement policies Fully automatic Provides example sequences for competitive ratio and constant Analysis usually practically feasible up to associativity 8 limited by memory consumption depends on similarity of replacement policies Online version: Jan Reineke Caches in WCET Analysis ARTIST Summer School / 51

89 Generalizations Identified patterns and proved generalizations by hand. Aided by example sequences generated by tool. Jan Reineke Caches in WCET Analysis ARTIST Summer School / 51

90 Generalizations Identified patterns and proved generalizations by hand. Aided by example sequences generated by tool. Previously unknown facts: PLRU(k) is (1, 0) comp. rel. to LRU(1 + log 2 k), LRU-must-analysis can be used for PLRU Jan Reineke Caches in WCET Analysis ARTIST Summer School / 51

91 Generalizations Identified patterns and proved generalizations by hand. Aided by example sequences generated by tool. Previously unknown facts: PLRU(k) is (1, 0) comp. rel. to LRU(1 + log 2 k), LRU-must-analysis can be used for PLRU FIFO(k) is ( 1 2, k 1 2 ) hit-comp. rel. to LRU(k), whereas LRU(k) is (0, 0) hit-comp. rel. to FIFO(k), but Jan Reineke Caches in WCET Analysis ARTIST Summer School / 51

92 Generalizations Identified patterns and proved generalizations by hand. Aided by example sequences generated by tool. Previously unknown facts: PLRU(k) is (1, 0) comp. rel. to LRU(1 + log 2 k), LRU-must-analysis can be used for PLRU FIFO(k) is ( 1 2, k 1 2 ) hit-comp. rel. to LRU(k), whereas LRU(k) is (0, 0) hit-comp. rel. to FIFO(k), but LRU(2k 1) is (1, 0) comp. rel. to FIFO(k), and LRU(2k 2) is (1, 0) comp. rel. to MRU(k). LRU-may-analysis can be used for FIFO and MRU optimal with respect to predictability metric Evict Jan Reineke Caches in WCET Analysis ARTIST Summer School / 51

93 Generalizations Identified patterns and proved generalizations by hand. Aided by example sequences generated by tool. Previously unknown facts: PLRU(k) is (1, 0) comp. rel. to LRU(1 + log 2 k), LRU-must-analysis can be used for PLRU FIFO(k) is ( 1 2, k 1 2 ) hit-comp. rel. to LRU(k), whereas LRU(k) is (0, 0) hit-comp. rel. to FIFO(k), but LRU(2k 1) is (1, 0) comp. rel. to FIFO(k), and LRU(2k 2) is (1, 0) comp. rel. to MRU(k). LRU-may-analysis can be used for FIFO and MRU optimal with respect to predictability metric Evict FIFO-may-analysis used in the analysis of the branch target buffer of the MOTOROLA POWERPC 56X. Jan Reineke Caches in WCET Analysis ARTIST Summer School / 51

94 Outline 1 Caches 2 Cache Analysis for Least-Recently-Used 3 Beyond Least-Recently-Used Predictability Metrics Relative Competitiveness Sensitivity Caches and Measurement-Based Timing Analysis 4 Summary Jan Reineke Caches in WCET Analysis ARTIST Summer School / 51

95 Measurement-Based Timing Analysis Run program on a number of inputs and initial states. Combine measurements for basic blocks to obtain WCET estimation. Sensitivity Analysis demonstrates this approach may be dramatically wrong. Jan Reineke Caches in WCET Analysis ARTIST Summer School / 51

96 Measurement-Based Timing Analysis Run program on a number of inputs and initial states. Combine measurements for basic blocks to obtain WCET estimation. Sensitivity Analysis demonstrates this approach may be dramatically wrong. Jan Reineke Caches in WCET Analysis ARTIST Summer School / 51

97 Influence of Initial Cache State variation due to initial cache state BCET WCET upper bound execution time Definition (Miss sensitivity) Policy P is (k, c)-miss-sensitive if m P (p, s) k m P (p, s) + c for all access sequences s M and cache-set states p, p C P. Jan Reineke Caches in WCET Analysis ARTIST Summer School / 51

98 Sensitivity Results Policy LRU 1, 2 1, 3 1, 4 1, 5 1, 6 1, 7 1, 8 FIFO 2, 2 3, 3 4, 4 5, 5 6, 6 7, 7 8, 8 PLRU 1, 2 MRU 1, 2 3, 4 5, 6 7, 8 MEM MEM MEM LRU is optimal. Performance varies in the least possible way. For FIFO, PLRU, and MRU the number of misses may vary strongly. Case study based on simple model of execution time by Hennessy and Patterson (2003): WCET may be 3 times higher than a measured execution time for 4-way FIFO. Jan Reineke Caches in WCET Analysis ARTIST Summer School / 51

99 Outline 1 Caches 2 Cache Analysis for Least-Recently-Used 3 Beyond Least-Recently-Used Predictability Metrics Relative Competitiveness Sensitivity Caches and Measurement-Based Timing Analysis 4 Summary Jan Reineke Caches in WCET Analysis ARTIST Summer School / 51

100 Summary Cache Analysis for Least-Recently-Used... efficiently represents sets of cache states by bounding the age of memory blocks from above and below.... requires context-sensitivity for precision. Jan Reineke Caches in WCET Analysis ARTIST Summer School / 51

101 Summary Cache Analysis for Least-Recently-Used... efficiently represents sets of cache states by bounding the age of memory blocks from above and below.... requires context-sensitivity for precision. Predictability Metrics... quantify the predictability of replacement policies. LRU is the most predictable policy. Jan Reineke Caches in WCET Analysis ARTIST Summer School / 51

102 Summary Cache Analysis for Least-Recently-Used... efficiently represents sets of cache states by bounding the age of memory blocks from above and below.... requires context-sensitivity for precision. Predictability Metrics... quantify the predictability of replacement policies. LRU is the most predictable policy. Relative Competitiveness... allows to derive guarantees on cache performance,... yields first may-analyses for FIFO and MRU. Jan Reineke Caches in WCET Analysis ARTIST Summer School / 51

103 Summary Cache Analysis for Least-Recently-Used... efficiently represents sets of cache states by bounding the age of memory blocks from above and below.... requires context-sensitivity for precision. Predictability Metrics... quantify the predictability of replacement policies. LRU is the most predictable policy. Relative Competitiveness... allows to derive guarantees on cache performance,... yields first may-analyses for FIFO and MRU. Sensitivity Analysis... determines the influence of initial state on cache performance. Jan Reineke Caches in WCET Analysis ARTIST Summer School / 51

104 Summary Cache Analysis for Least-Recently-Used... efficiently represents sets of cache states by bounding the age of memory blocks from above and below.... requires context-sensitivity for precision. Predictability Metrics... quantify the predictability of replacement policies. LRU is the most predictable policy. Relative Competitiveness... allows to derive guarantees on cache performance,... yields first may-analyses for FIFO and MRU. Sensitivity Analysis... determines the influence of initial state on cache performance. Thank you for your attention! Jan Reineke Caches in WCET Analysis ARTIST Summer School / 51

105 Summary Cache Analysis for Least-Recently-Used... efficiently represents sets of cache states by bounding the age of memory blocks from above and below.... requires context-sensitivity for precision. Predictability Metrics... quantify the predictability of replacement policies. LRU is the most predictable policy. Relative Competitiveness... allows to derive guarantees on cache performance,... yields first may-analyses for FIFO and MRU. Sensitivity Analysis... determines the influence of initial state on cache performance. Thank you for your attention! Jan Reineke Caches in WCET Analysis ARTIST Summer School / 51

106 Most-Recently-Used MRU MRU-bits record whether line was recently used e c [abcd] 0101 [ebcd] 1101 [ebcd] 0010 b,d e,b,d c Never converges Jan Reineke Caches in WCET Analysis ARTIST Summer School / 51

107 Pseudo-LRU PLRU a b c d a b e d a b e d a b e f Initial cacheset state [a, b, c, d] 110. After a miss on e. State: [a, b, e, d] 011. After a hit on a. State: [a, b, e, d] 111. After a miss on f. State: [a, b, e, f ] 010. Hit on a rejuvenates neighborhood; saves b from eviction. Jan Reineke Caches in WCET Analysis ARTIST Summer School / 51

108 May- and Must-Information May P (s) := p C P CC P (update P (p, s)) Must P (s) := p C P CC P (update P (p, s)) may P (n) := must P (n) := May P (s), where s S M, s = n Must P (s), where s S M, s = n S : set of finite access sequences with pairwise different accesses Jan Reineke Caches in WCET Analysis ARTIST Summer School / 51

109 Definitions of Metrics { } Evict P := min n may P (n) n, { } Fill P := min n must P (n) = k, where k is P s associativity. Jan Reineke Caches in WCET Analysis ARTIST Summer School / 51

110 Relation: Pred. Metrics Rel. Competitiveness Let P(k) be (1, 0)-miss-competitive relative to policy Q(l), then (i) Evict P (k) Evict Q (l), (ii) mls P (k) mls Q (l). Jan Reineke Caches in WCET Analysis ARTIST Summer School / 51

111 Alternative Pred. Metrics Rel. Competitiveness Let l be the smallest associativity, such that LRU(l) is (1, 0)-miss-competitive relative to P(k). Then Alt-Evict P (k) = l. Let l be the greatest associativity, such that P(k) is (1, 0)-miss-competitive relative to LRU(l). Then Alt-mls P (k) = l. Jan Reineke Caches in WCET Analysis ARTIST Summer School / 51

112 Size of Transition System k ( ) k k ( ) k min{i,i } ( )( ) i i 2}{{} l+l i i j! j j status bits i=0 i of P and Q }{{} =0 j=0 }{{}}{{} non-empty lines in P non-empty lines in Q number of overlappings in non-empty lines min{k,k } j=0 ( k j )( k This can be bounded by j ) j! k! k! k! k! min{k,k } j=0 j=0 1 (k j)!j!(k j)! 1 j! = e k! k! 2 l+l +k+k (C l k Cl k )/ 2l+l +k+k e k! k! }{{} bound on number of overlappings Jan Reineke Caches in WCET Analysis ARTIST Summer School / 51

113 Compatible States i P = [ ] P i Q = [ ] Q update P (i P, s) update Q (i Q, s) p q Jan Reineke Caches in WCET Analysis ARTIST Summer School / 51

114 (1, 0)-Competitiveness and May/Must-Analyses Let P be (1, 0)-competitive relative to Q, then p q m P (p, x ) = 1 = m Q (q, x ) = 1 p q Jan Reineke Caches in WCET Analysis ARTIST Summer School / 51

115 (1, 0)-Competitiveness and May/Must-Analyses C P C Q S S p P : m P (p, x ) = 1 P P = Q Q q Q : m Q (q, x ) = 1 Jan Reineke Caches in WCET Analysis ARTIST Summer School / 51

116 Case Study: Impact of Sensitivity Simple model of execution time from Hennessy & Patterson (2003) CPI hit = Cycles per instruction assuming cache hits only Memory accesses Instruction including instruction and data fetches T wc = CPI hit + T meas CPI hit + Memory accesses Miss rate Instruction wc Miss penalty Memory accesses Miss rate Instruction meas Miss penalty = = = 3 Jan Reineke Caches in WCET Analysis ARTIST Summer School / 51