1 / 98

# Algebra of Concurrent Programming - PowerPoint PPT Presentation

Algebra of Concurrent Programming. Tony Hoare Redmond August 2011. With ideas from. Ian Wehrman John Wickerson Stephan van Staden Peter O’Hearn Bernhard Moeller Georg Struth Rasmus Petersen …and others. Subject matter: designs.

Related searches for Algebra of Concurrent Programming

I am the owner, or an agent authorized to act on behalf of the owner, of the copyrighted work described.

## PowerPoint Slideshow about 'Algebra of Concurrent Programming' - shilah

Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author.While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server.

- - - - - - - - - - - - - - - - - - - - - - - - - - E N D - - - - - - - - - - - - - - - - - - - - - - - - - -
Presentation Transcript

### Algebra of Concurrent Programming

Tony Hoare

Redmond August 2011

• Ian Wehrman

• John Wickerson

• Peter O’Hearn

• Bernhard Moeller

• Georg Struth

• Rasmus Petersen

• …and others

• 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.

• 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

• 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

• 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

• 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

Various terminologyp ⊑ 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)

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

• sequential composition of p and q

• eachexecution 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

• 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

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 .

• Law: ( ; is monotonic wrto ⊑) :

• p;q ⊑ p’;q’ if p ⊑ p’

• p;q⊑ p’;q’ if q ⊑ q’

• Theorem (rule of consequence):

• p’ ⊑ p & {p} q {r} & r ⊑ r’ implies {p’} q {r’}

• Theorem implies first law

• Law (; is associative) :

• (p;q);q’ = p;(q;q’)

• Theorem (sequential composition):

• {p} q {s} & {s} q’ {r} implies{p} q;q’ {r}

• half the law provable from theorem

• a program that does nothing

• Law ( is the unit of ;):

• p;= p = ;p

• Theorem (nullity)

• {p}  {p}

• a quarter of the law is provable from theorem

• 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.

• Laws: | is associative, commutative and monotonic

• Law (locality of ; wrto |):

• (s|p) ; q ⊑ s |(p;q) (left locality )

• p ; (q|s) ⊑ (p;q) | s (right locality)

• Theorem (frame rule) :

• {p} q {r} implies {p|s} q {r|s}

• Theorem implies left locality

• Law (; exchanges with *)

• (p|q) ; (p’|q’) ⊑ (p;p’) | (q;q’)

• like exchange law of category theory

• Theorem (| compositional)

• {p} q {r} & {p’} q’ {r’} implies

{p|p’} q|q’ {r|r’}

• Theorem implies the law

p|q;p’|q’

p

p’

q

q’

by columns

p|q;p’|q’

p

p’

p;p’ |q;q’

q

q’

by rows

• 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” = {<c>} (only the string “c”)

• 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 qq ⊑ p s q s q q

• 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’

• regular expressions satisfy all our laws for ⊑ , ; , and |

• and for other operators introduced later

• Non-determinism, intersection

• Iteration, recursion, fixed points

• Subroutines, contracts, transactions

• Basic commands

• 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.

• disjunction (or) to express abstraction,

or to keep options open

• ‘it may be painted green or blue’

• conjunction (and) to combine 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

• disjunction

• non-deterministic programs difficult to test

• conjunction

• inefficient to find a computation satisfying both

• negation

• incomputable

• implication

• there is no point in executing it

• sequential composition (;)

• concurrent composition (|)

• interrupts

• iteration, recursion

• contracts (declarations)

• transactions

• assignments, inputs, outputs, jumps,…

• So include these in our specifications!

• 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)

• Law ( is the zero of ;) :

•  ; p =  = p ; 

• Theorem : {p}  {q}

• Quarter of law provable from theorem

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: _ ⊑ _

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 ⊤

• 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

lub (join): ⊔

• Define p⊔q as least solution of

p ⊑ _ & q ⊑ _

• Theorem

• p ⊑ r & q ⊑ r iffp⊔q⊑ r

• Theorem

• ⊔ is associative, commutative, monotonic, idempotent and increasing

• it has unit ⊥ and zero ⊤

glb (meet): ⊓

• Define p⊓qas greatest solution of

_ ⊑ p & _ ⊑ q

• Law ( ; distributive through ⊔ )

• p ; (q⊔q’) = p;q ⊔ p;q’

• (q⊔q’) ; p = q;p⊔ q’;p

• Theorem (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 provable from theorem

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)

• 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 an alternative q with condition c.

• 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

• 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

• 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

• ⊓S is greatest lower bound of S

• ⊓ { } = ⊤

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)

• Theorem (invariance):

• {p}q*{p} if {p}q{p}

• !p is the greatest solution of _ ⊑ p|_

• as in the pi calculus

• all executions of !p are infinite

• or possibly empty

• 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.

• Define (q..s) as glb of the set

q ⊑ _ & _ ⊑ s

• Theorem: (q.. s) = q if q ⊑ s

= ⊤ otherwise

• skip 

• bottom 

• top ⊤

• assignment: x := e(x)

• assertion: assert b

• assumption: assume b

• finally ..b

• initially b..

• 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

• assume b =def b..⊓

• assert b =defb..⊓ ⊔ not(b)..

• x:=e(x) ; x:=f(x) = x := f(e(x))

• in a sequential language

• 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

Part 3Unifying Semantic Theories

• Six familiar semantic definition styles.

• Their derivation from the algebra

• and vice versa.

deduction rules

operational rules

• disregards unending executions

• ..b is re-interpreted as conditional on termination:

• ‘if the execution terminates, it will end in a state satisfying b‘.

• definition of triple stays the same

• all laws apply also to conditional correctness logic

• 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

Plotkin reduction

• a method for program execution

• <p , q> -> r =def p ; q ⊒ r

• if p describes state before execution of q then r describes a possible final state, eg.

• <..(x2 = 18) , x := x+1> -> ..(x = 37)

• Theorem (sequential composition):

• <p, q> -> s & <s, q’> -> r

implies <p, q;q’> r

• 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 ; )

• 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 impliesp [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.

• {p} q {r} =defp;q ⊑ r

• Hoare triple

• <p,q>->r =defp;q ⊒ r

• Plotkin reduction

• p –q->r =def p ⊒ q;r

• Milner transition

• p [q] r =def p ⊑ q;r

• test generation

• Law: ; is associative

• Theorem: sequence rule is valid for all four triples.

• the Law is provable from the conjunction of all of them

Skip

• Law: p ;  = p =  ; p

• Theorems:

{p}  {p} p [] p

p −→ p <p,  > –>p

• Law follows from conjunction of all four theorems

• Left distribution ; through ⊔

• Law: p;(q ⊔q’) =p;q⊔p;q’

• Theorems:

• {p} (q⊔q’) {r} if {p}q{r} and {p}q’{r}

• <p,q⊔q’>-> r if <p,q>-> r or <p, q’>-> 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.

• 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

• 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.

• 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 <p,q>->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

• (q -; r) =def

the weakest solution of ( _ ;q ⊆ r)

• the same as Dijkstra’swp(q, r)

• for backward development of programs

• Law (-; adjoint to ;)

• p ⊑ q -; r iffp;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

• q’ ⊑ q & r ⊑ r’ => q-;r ⊑ q’-;r’

• (q;q’)-;r ⊑ q-;(q’-;r)

• q-;r ⊑ (q;s) -; (r;s)

• (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

Part 4DenotationalModels

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

denotational

models

algebraic

laws

• Boolean algebra

( {0,1}, ≤, , , (1 - _) )

• predicate algebra (Frege, Heyting)

• (ℙS,├, , , (S - _), => , ∃, ∀)

• regular expressions (Kleene):

• (ℙA*, ⊆,∪, ; , ɛ , {<a>} , | )

• binary relations (Tarski):

• (ℙ(SS), ⊆, ∪, ∩, ; , Id , not(_), converse(_))

• algebra of designs is a superset of these

• 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.

• ⊑ is  (set inclusion)

• ⊔ is  (set union)

• ⊓ is  (intersection of sets)

•  is { } (the empty set)

• ⊤ is EV (the universal set)

• 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

• TIM is a set of times for events

• partially ordered by ≤

• Let when : EV -> TIM

• map each event to its time of occurrence.

• x < y =def not(when(y) ≤ when(x))

• x < y & y < x means that x and y occur ‘in true concurrency’.

• e < f =def ∀x,y . x∊e & y∊f => 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

• 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

• 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 |

• 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!

• 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

• p*q =def (p|q .. p||q)

= p|q if 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 = ⊤

• ; 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.

• 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 }

• events at different places are concurrent

• events at the same place are totally ordered in time

• ∀x,y ∊ EV .

where(x) = where(y) iffx≤y or y≤x

space

time

• p || q = {ef | e ∊ p & f ∊ q

& where(e)  where(f) = { } }

• proved from separation 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

p

q

space

time

p

q

space

time

Conclusion:in Praise of Algebra

• Reusable

• Modular

• Incremental

• Unifying

• Discriminative

• Computational

• Comprehensible

• Abstract

• Beautiful!

• Algebra chooses as primitives

• operators with two operands + , 

• predicates with two places = , 

• laws with two operators & v , + 

• algebras with two components rings

• Tuples are defined in terms of pairs.

• Hoare triples

• Plotkin triples

• Jones quintuples

• seventeentuples …

denotations

algebra

deductions

transitions

algebra

algebra