1. Algebra of Concurrent Programming Tony Hoare Cambridge 2011

2. With ideas from Ian Wehrman John Wickerson Stephan van Staden Peter O’Hearn Bernhard Moeller Georg Struth Rasmus Petersen …and others

3. Subject matter: designs variables (p, q, r) stand for computer programs, designs, specifications,… they all describe what happens inside/around a computer that executes a given program. The program itself is the most precise. The specification is the most abstract. Designs come in between.

4. Examples Postcondition: – execution ends with array A sorted Conditional correctness: – if execution ends, it ends with A sorted Precondition: – execution starts with x even Program: x := x+1 – the final value of x one greater than the initial

5. Examples Safety: – There are no buffer overflows Termination: – execution is finite (ie., always ends) Liveness: – no infinite internal activity (livelock) Fairness: – a response is always given to each request Probability: – the ration of a’s to b’s tends to 1 with time

6. Unification Same laws apply to programs, designs, specifications Same laws apply to many forms of correctness. Tools based on the laws serve many purposes. Distinctions can be drawn later – when the need for them is apparent

7. Refinement: p ⊑ q Everything described by p is also described by q, e.g., – spec p implies spec q – prog p satisfies spec q – prog p more determinate than prog q stepwise development of a spec is – spec ⊒ design ⊒ program stepwise analysis of a program is – program ⊑ design ⊑ spec

8. Various terminology p ⊑ q below lesser stronger lower bound more precise …deterministic included in  antecedent => above greater weaker upper bound more abstract...non-deterministic containing (sets) consequent (pred)

9. Law: ⊑ is a partial order ⊑ is transitive p ⊑ r if p ⊑ q and q ⊑ r needed for stepwise development/analysis ⊑ is antisymmetric p = r if p ⊑ r and r ⊑ p needed for abstraction ⊑ is reflexive – p ⊑ p for convenience

10. Binary operator: p ; q sequential composition of p and q each execution of p;q consists of – all events x from an execution of p – and all events y from an execution of q subject to ordering constraint, either – strong-- weak – interruptible-- inhibited

11. alternative constraints on p;q strong sequence: – all x from p must precede all y from q weak sequence: – no y from q can precede any x from p interruptible: – other threads may interfere between x and y separated: – updates to private variables are protected. all our algebraic laws will apply to each alternative

12. Hoare triple: {p} q {r} defined as p;q ⊑ r – starting in the final state of an execution of p, q ends in the final state of some execution of r – p and r may be arbitrary designs. example: {..x+1 ≤ n} x:= x + 1 {..x ≤ n} where..b (finally b) describes all executions that end in a state satisfying a single-state predicate b.

13. monotonicity Law: ( ; is monotonic wrto ⊑) : – p;q ⊑ p’;q if p ⊑ p’ – p;q ⊑ p;q’ if q ⊑ q’ – compare: addition of numbers Rule (of consequence): – p’ ⊑ p & {p} q {r} & r ⊑ r’ implies {p’} q {r’} Rule is interprovable with first law

14. associativity Law (; is associative) : – (p;q);q’ = p;(q;q’) Rule (sequential composition): – {p} q {s} & {s} q’ {r} implies {p} q;q’ {r} half the law interprovable from rule

15. Unit(skip):  a program that does nothing Law (  is the unit of ;): – p;  = p =  ;p Rule (nullity) – {p}  {p} a quarter of the law is interprovable from Rule

16. concurrent composition: p | q each execution of (p|q) consists of – all events x of an execution of p, – and all events y of an execution of q same laws apply to alternatives: – interleaving: x precedes or follows y – true concurrency: x neither precedes nor follows y. – separation: x and y independent Laws: | is associative, commutative and monotonic

17. Separation Logic Law (locality of ; wrto |): – (s|p) ; q ⊑ s |(p;q)(left locality ) – p ; (q|s) ⊑ (p;q) | s(right locality) Rule (frame) : – {p} q {r} implies {p|s} q {r|s} Rule interprovable with left locality

18. Concurrency law Law (; exchanges with *) – (p|q) ; (p’|q’) ⊑ (p;p’) | (q;q’) – like exchange law of category theory Rule (| compositional) – {p} q {r} & {p’} q’ {r’} implies {p|p’} q|q’ {r|r’} Rule interprovable with the law

19. p|q ; p’|q’ p p’ q’ q by columns

20. p|q ; p’|q’ ⊑ p p’ q’ q p;p’ | q;q’ by rows

21. Regular language model p, q, r,… are languages – descriptions of execution of fsm. p ⊑ q is inclusion of languages p;q is (lifted) concatenation of strings – i.e., {st| s ∊ p & t ∊ q} p|q is (lifted) interleaving of strings  = { } (only the empty string) “c” = { } (only the string “c”)

22. Left locality Theorem: (s|p) ; q ⊑ s | (p;q) Proof: in lhs: s interleaves with just p, and all of q comes at the end. in rhs: s interleaves with all of p;q so lhs is a special case of rhs p s s ; q q q⊑p s q s q q

23. Exchange Theorem: (p|q) ; (p’|q’) ⊑ (p;p’) | (q;q’) – in lhs: all of p and q comes before all of p’ and q’. – in rhs: end of p may interleave with q’ or start of p’ with q the lhs is a special case of the rhs. p q p ; q’ p’ q’ ⊑p q q’ p p’ q’

24. Conclusion regular expressions satisfy all our laws for ⊑, ;, and | and for other operators introduced later

25. Part 2. More Program Control Structures Non-determinism, intersection Iteration, recursion, fixed points Subroutines, contracts, transactions Basic commands

26. Subject matter variables (p, q, r) stand for programs, designs, specifications,… they are all descriptions of what happens inside and around a computer that is executing a program. the differences between programs and specs are often defined from their syntax.

27. Specification syntax includes disjunction (or, ⊔ ) to express abstraction, or to keep options open – ‘it may be painted green or blue’ conjunction (and, ⊓ ) combines requirements – it must be cheaper than x and faster than y negation (not) for safety and security – it must not explode implication (contracts) – if the user observes the protocol, so will the system

28. Program syntax excludes disjunction – non-deterministic programs difficult to test conjunction – inefficient to find a computation satisfying both negation – incomputable implication – which side of contract?

29. programs include sequential composition (;) concurrent composition (|) interrupts iteration, recursion contracts (declarations) transactions assignments, inputs, outputs, jumps,… So include these in our specifications!

30. Bottom  An unimplementable specification – like the false predicate A program that has no execution – the compiler stops it from running Define  as least solution of: _ ⊑ _ Theorem:  ⊑ r –  satisfies every spec, – but cannot be run (Dijkstra’s miracle)

31. Algebra of  Law (  is the zero of ;) : –  ; p =  = p ;  Theorem : {p}  {q} Quarter of law provable from theorem

32. Top ⊤ a vacuous specification, – satisfied by anything, – like the predicate true a program with an error – for which the programmer is responsible – e.g., subscript error, violation of contract… define ⊤ as greatest solution of: _ ⊑ _

33. Algebra of ⊤ Law: none Theorem: none – you can’t prove a program with this error – it might admit a virus! A debugging implementation may supply useful laws for ⊤

34. Non-determinism (or): p ⊔ q describes all executions that either satisfy p or satisfy q. The choice is not (yet) determined. It may be determined later – in development of the design – or in writing the program – or by the compiler – or even at run time

35. lub (join): ⊔ Define p⊔q as least solution of p ⊑ _ & q ⊑ _ Theorem – p ⊑ r & q ⊑ r iff p⊔q ⊑ r Theorem – ⊔ is associative, commutative, monotonic, idempotent and increasing – it has unit ⊥ and zero ⊤

36. glb (meet): ⊓ Define p⊓q as greatest solution of _ ⊑ p & _ ⊑ q

37. Distribution Law ( ; distributive through ⊔ ) – p ; (q⊔q’) = p;q ⊔ p;q’ – (q⊔q’) ; p = q;p ⊔ q’;p Rule (non-determinism) – {p} q {r} & {p} q’ {r} implies {p} q⊔q’ {r} – i.e., to prove something of q⊔q’ prove the same thing of both q and q’ quarter of law interprovable with rule

38. Conditional: p if b else p’ Define p ⊰b⊱ p’ as b.. ⊓ p ⊔ not(b).. ⊓ p’ – where b.. describes all executions that begin in a state satisfying b. Theorem. p ⊰b⊱ p’ is associative, idempotent, distributive, and – p ⊰b⊱ q = q ⊰not(b)⊱ p (skew symm) – (p ⊰b⊱ p’ ) ⊰c⊱ (q ⊰b⊱ q’) = (p ⊰c⊱ q) ⊰b⊱ (p’ ⊰c⊱ q’) (exchange)

39. Transaction Defined as (p ⊓..b) ⊔ (q ⊓..c) – where..b describes all executions that end satisfying single-state predicate b. Implementation: – execute p first – test the condition b afterwards – terminate if b is true – backtrack on failure of b – and try alternative q with condition c.

40. Transaction (realistic) Let r describe the non-failing executions of a transaction t. – r is known when execution of t is complete. – any successful execution of t is committed – a single failed execution of t is undone, – and q is done instead. Define: (t if r else q) = t if t ⊑ r = (t ⊓ r) ⊔ q otherwise

41. Contracts Let q be the body of a subroutine Let s be its specification Let (q.. s) assert that q meets s Programmer error (⊤) if not so Caller of subroutine may assume that s describes all its calls Implementation may just execute q

42. Least upper bound Let S be an arbitrary set of designs Define ⊔ S as least solution of ∀s∊ S. s ⊑ _ – ( ∀s∊ S. s ⊑ r ) ⇒ ⊔S ⊑ r (all r) everything is an upper bound of { }, so ⊔ { }=  – a case where ⊔S ∉ S

43. similarly ⊓ S is greatest lower bound of S ⊓ { } = ⊤

44. Subroutine with contract: q.. s Define (q..s) as glb of the set q ⊑ _ & _ ⊑ s Theorem: (q.. s) = q if q ⊑ s = ⊤ otherwise

45. Iteration (Kleene *) q* is least solution of – (ɛ ⊔ (q; _) ) ⊑ _ q* = def ⊔ {s| (ɛ ⊔ q; s) ⊑ s} – ɛ ⊔ q; q* ⊑ q* – ɛ ⊔ q; q’ ⊑ q’ implies q* ⊑ q’ – q* = ⊔ {qⁿ | n ∊ Nat}(continuity) Rule (invariance): – {p}q*{p} if{p}q{p}

46. Infinite replication !p is the greatest solution of _ ⊑ p|_ – as in the pi calculus all executions of !p are infinite – or possibly empty

47. Recursion Let F(_) be a monotonic function between programs. Theorem: all functions defined by monotonic operators are monotonic. μF is strongest solution of F(_) ⊑ _ νF is weakest solution of _ ⊑ F(_) Theorem (Knaster-Tarski): These solutions exist.

48. Basic statements/assertions skip  bottom  top⊤ assignment:x := e(x) assertion:assert b assumption:assume b finally..b initiallyb..

49. more assign thru pointer:[a] := e output:c!e input:c?x points to:a|-> e – a |-> _= def exists v. a|-> v throw, catch alloc, dispose

50. Laws(examples) assume b= def b..⊓  assert b= def b..⊓  ⊔ not(b).. x:=e(x) ; x:=f(x)=x := f(e(x)) – in a sequential language

51. more (p|-> _ ); [p] := e⊑p|-> e – in separation logic c!e | c?x=x := e – in CSP but not in CCS or Pi throw x ; (catch x; p) =p

52. Part 3 Unifying Semantic Theories Six familiar semantic definition styles. Their derivation from the algebra and vice versa.

53. operational rules algebraic laws deduction rules

54. Hoare Triple a method for program verification {p} q {r} ≝ p;q ⊑ r – one way of achieving r is by first doing p and then doing q Theorem (sequential composition): – {p} q {s} & {s} q’ {r} implies {p} q;q’ {r} – proved by associativity

55. Plotkin reduction a method for program execution -> r = def p ; q ⊒ r – if p describes state before execution of q then r describes a possible final state, eg. – ->..(x = 37) Theorem (sequential composition): -> s & -> r implies r

56. Milner transition method of execution for processes p – q -> r≝p ⊒ q;r – one of the ways of executing p is by first executing q and then executing r. – e.g., (x := x+3) –(x:=x+1)-> (x:=x+2) Theorem (sequential composition): – p –q-> s & s –q’-> r => p –(q;q’)-> r (big-step rule for ; )

57. partial correctness describes what may happen p[q]r= def p ⊑ q;r – if p describes a state before execution of q, then execution of q may achieve r Theorem (sequential composition): p [q] s & s [q’] r implies p [q;q’] r useful if r describes error states, and q describes initial states from which a test execution of q may end in error.

58. Summary {p} q {r}= def p;q ⊑ r – Hoare triple ->r= def p;q ⊒ r – Plotkin reduction p –q->r = def p ⊒ q;r – Milner transition p [q] r = de fp ⊑ q;r – test generation

59. Sequential composition Law: ; is associative Theorem: sequence rule is valid for all four triples. the Law is provable from the conjunction of all of them

60. Skip  Law: p ;  = p =  ; p Theorems: {p}  {p} p [  ] p p −  → p –>p Law follows from conjunction of all four theorems

61. Left distribution ; through ⊔ Law: p;(q ⊔ q’)=p;q ⊔ p;q’ Theorems: – {p} (q⊔q’) {r} if {p}q{r} and {p}q’{r} – -> r if -> r or -> r – p [q⊔q’] r if p [q] r or p [q’] r – p -(q⊔q’)-> r if p –q->r and p -q’->r (not used in CCS) law provable from either and rule together with either or rule.

62. locality and frame left locality(s|p) ; q ⊑s | (p;q) Hoare frame: {p} q {r} ⇒ {s|p} q {s|r} right locality p ; (q|s) ⊑ (p;q) | s Milner frame: p -q-> r⇒(p|s) - q-> (r|s) Full locality requires both frame rules

63. Separation logic Exchange law: – (p | p’) ; (q| q’)  (p ; q) | (p’;q’) Theorems – {p} q {r} & {p’} q’ {r’} ⇒ {p|p’} q|q’ {r|r’} – p -q -> r & p’–q’-> r’ => p|p’ –q|q’-> r|r’ the law is provable from either theorem For the other two triples, the rules are equivalent to the converse exchange law.

64. usual restrictions on triples in {p} q {r}, p and r are of form..b,..c in p [q] r,p and r are of form b.., c.. in ->r,p and r are of form..b,..c in p –q->r, p and r are programs in p –q->r (small step), q is atomic (in all cases, q is a program) all laws are valid without these restrictions

65. Weakest precondition (-;) (q -; r) = def the weakest solution of ( _ ;q ⊆ r) – the same as Dijkstra’s wp(q, r) – for backward development of programs

66. Weakest precondition (-;) Law (-; adjoint to ;) – p ⊑ q -; riffp;q ⊑ r(galois) Theorem – (q -; r) ; q⊑r – p⊑q -; (p ; q) Law provable from the theorems – cf. (r div q)  q ≤ r – r≤(r  q) div q

67. Theorems q’ ⊑ q & r ⊑ r’=> q-;r ⊑ q’-;r’ (q;q’)-;r ⊑q-;(q’-;r) q-;r ⊑(q;s) -; (r;s)

68. Specification statement (;-) (p ;- r) = def the weakest solution of ( p ; _ ⊆ r) – Back/Morgan’s specification statement – for stepwise refinement of designs – same as p⇝r in RGSep – same as (requires p; ensures r) in VCC

69. Law of consequence

70. Frame laws

71. Part 4 Denotational Models A model is a mathematical structure that satisfies the axioms of an algebra, and realistically describes a useful application, for example, program execution.

72. Models denotational models algebraic laws

73. Some Standard Models: Boolean algebra ( {0,1}, ≤, , , (1 - _) ) predicate algebra (Frege, Heyting) – (ℙS,├, , , (S - _), =>, ∃, ∀) regular expressions (Kleene): – (ℙA*, ⊆, ∪, ;, ɛ, { }, | ) binary relations (Tarski): – (ℙ(S  S), ⊆, ∪, ∩, ;, Id, not(_), converse(_)) algebra of designs is a superset of these

74. Model: (EV, EX, PR) EV is an underlying set of events (x, y,..) that can occur in any execution of any program EX are executions (e, f,…), modelled as sets of events PR are designs (p, q, r,…), modelled as sets of executions.

75. Set concepts ⊑ is  (set inclusion) ⊔ is  (set union) ⊓ is  (intersection of sets)  is { } (the empty set) ⊤is EV (the universal set)

76. With (|) p | q = {e ∪ f | e ε p & f ε q & e∩f = { } } – each execution of p|q is the disjoint union of an execution of p and an execution of q – p|q contains all such disjoint unions | generalises many binary operators

77. Introducing time TIM is a set of times for events – partially ordered by ≤ Let when : EV -> TIM – map each event to its time of occurrence.

78. Definition of < x < y = def not(when(y) ≤ when(x)) – x < y & y < x means that x and y occur ‘in true concurrency’. e x < y – no event of f occurs before an event of e – hence e<f implies e  f = { } If ≤ is a total order, – there is no concurrency, – executions are time-ordered strings

79. Sequential composition (then) p ; q = {e  f | e∊p & f∊q & e<f} special case: if ≤ is a total order, – e < f means that e  f is concatenation (e⋅f) of strings – ; is the composition of regular expressions

80. Theorems These definitions of ; and | satisfy the locality and exchange laws. (s|p) ; q ⊑ s |(p;q) (p|q) ; (p’|q’) ⊑ (p;p’) | (q;q’) – Proof: the lhs describes fewer interleavings than the rhs. special case: regular expressions satisfy all our laws for ⊑, ⊔, ;, and |

81. Disjoint concurrency (||) p||q = def (p ; q)  (q ; p) – all events of p concurrent with all of q. – no interaction is possible between them. Theorems: (p||q) ; r  p || (q ; r) (p||q) ; (p’||q’)  (p;p’) || (q;q’) – Proof: the rhs has more disjointness constraints than the lhs. – the wrong way round! So make the programmer responsible for disjointness, using interfaces!

82. Interfaces Let q be the body of a subroutine Let s be its specification Let (q.. s) assert that q is correct Caller may assume s Implementer may execute q

83. Solution p*q = def (p|q.. p||q) = p|qif p|q ⊑ p||q ⊤ otherwise – programmer is responsible for absence of interaction between p and q. Theorem: ; and * satisfy locality and exchange. – Proof: in cases where lhs ≠ rhs, rhs = ⊤

84. Problem ; is almost useless in the presence of arbitrary interleaving (interference). It is hard to prove disjointness of p||q We need a more complex model – which constrains the places at which a program may make changes.

85. Separation PL is the set of places at which an event can occur each place is ‘owned’ by one thread, – no other thread can act there. Let where:EV -> PL map each event to its place of occurrence. where(e) = def {where(x) | x ∊ e }

86. Separation principle events at different places are concurrent events at the same place are totally ordered in time ∀x,y ∊ EV. where(x) = where(y) iff x≤y or y≤x

87. Picture time space

88. Theorem p || q = {e  f | e ∊ p & f ∊ q & where(e)  where(f) = { } } proved from separation principle

89. Convexity Principle Each execution contains every event that occurs between any of its events. ∀e ∊ EX, y ∊ EV. ∀x, z ∊ e. when(x) ≤ when(y) ≤ when(z) => y ∊ e – no event from elsewhere can interfere between any two events of an execution

90. A convex execution of p;q time space pq

91. A non-convex ‘execution’ of p;q time space pq

92. Conclusion: in Praise of Algebra Reusable Modular Incremental Unifying Discriminative Computational Comprehensible Abstract Beautiful!

93. Algebra likes pairs Algebra chooses as primitives – operators with two operands +,  – predicates with two places =,  – laws with two operators & v, +  – algebras with two componentsrings

94. Tuples Tuples are defined in terms of pairs. – Hoare triples – Plotkin triples – Jones quintuples – seventeentuples …

95. Semantic Links deductions transitions denotations algebra

96. Increments algebra

97. Filling the gaps algebra