Download presentation

Presentation is loading. Please wait.

Published byGustavo Morgan Modified over 2 years ago

1
CS294, YelickAbstract. Funs., p1 CS 294-8 Abstraction Functions http://www.cs.berkeley.edu/~yelick/294

2
CS294, YelickAbstract. Funs., p2 Agenda Administrivia Review of abstraction functions for the memory example History variables Prophecy variables General discussion

3
CS294, YelickAbstract. Funs., p3 Administrivia Dawson Engler speaking Thursday –OSDI paper online Final projects: –Send mail to schedule meeting with me –Poster session: Scheduled 12/7. Too early? With 262? –Papers due Friday 12/15 Homework 3

4
CS294, YelickAbstract. Funs., p4 History on Abstraction Functions Used since the 1970s for reasoning about datatypes, e.g., Tony Hoare’s paper –Used with “representation invariants” by Liskov and others Abadi and Lamport looked formalized their use in concurrent systems: –When do abstraction functions exist? –Formalized history variables Example from Herlihy and Wing paper demonstrated need for prophecy variables

5
CS294, YelickAbstract. Funs., p5 Execution Model Reminder A Spec Module defines a state machine An execution fragment is s 0 s 1 s 2 An execution starts in an initial state Steps are written as (s i, , s i+1 ) 0 1 2

6
CS294, YelickAbstract. Funs., p6 Some Executions of WBCache write(2,a) (read(2),a) init write(4,c) (read(3),a) B CA A AB B CA A AC B CA A AC B C A B B A AA AC CB C A AB B A A B B A A C B A A C B A A C abstract execution of specification concrete execution of implementation

7
CS294, YelickAbstract. Funs., p7 Abstraction Function for WBCache FUNC AF() -> M = RET (LAMBDA (a) -> D = IF c!a => c(a) [*] m(a) FI) Note that abstraction function maps each state in previous WBCache execution to Memory state

8
CS294, YelickAbstract. Funs., p8 Abstraction Function (Def 1) An abstraction function F: T -> S has: –If t is any initial state of T, then F(t) is an initial state of S –If t is reachable state of T and (t, , t’) is a step of T, then there is a step of S from F(t) to F(t’) having the same trace Same trace means externally visible values.

9
CS294, YelickAbstract. Funs., p9 Representation Invariants The abstraction function need not be defined on every value of the concrete state, only those reachable in the implementation Example: implementations of a set –An sorted array –Or, an unsorted array without duplicates Not every array is a legal set representation

10
CS294, YelickAbstract. Funs., p10 Modeling Failures in Spec A “crash” can happen between any two atomic actions –Volatile state reset –Stable state unaffected Add a Crash procedure to a module –Need not be atomic; invoked when there’s a crash –Does a “CRASH” command, which stops current (non-atomic) executions. Nothing else can be invoked until Crash returns. –Crash may do other things after CRASH cmd –Normal operation resumes after Crash returns

11
CS294, YelickAbstract. Funs., p11 Disk Example In the Disk example (H7, p4): –Write operations are Ordered Not atomic (although each block write is) –There is no global volatile state Crash just executes “CRASH”

12
CS294, YelickAbstract. Funs., p12 Agenda Review of abstraction functions for the memory example History variables Prophecy variables General discussion

13
CS294, YelickAbstract. Funs., p13 Example1 : Statistical DB Type Given a “statistical DB” Spec with the operations –Add(v): add a new number, v, to the DB –Size(): report the number of elements in the DB –mean(): report the mean of all elements in the DB –variance(): report the variance of all elements in the DB Notes have a parameter for the DB element type (V); for simplicity, I’ll use “real.”

14
CS294, YelickAbstract. Funs., p14 Example1 : Statistical DB Type Implementation 1: –Keep set of all values in the database –Mean and variance are computed when needed Implementation 2 (optimized): –Use only three values: integer count, initially 0 // number of elements float sum, initially 0 // sum of elements float sumSquare, initially 0 // sum of squares of // all elements

15
CS294, YelickAbstract. Funs., p15 History Variables Problem: the specification contains more info than the implementation –Specifically, one can’t recover the values in the db from the 3 state variables Idea: add some phantom variables to the state of the implementation. –Only for the proof –The operations can update this “phantom” state, but cannot change their behavior based on it.

16
CS294, YelickAbstract. Funs., p16 Proof of 2 nd Implementation Proof idea: –add a variable db to the representation state (for the proof only) –the implementation may update db Augmented “implementation” has: VAR count := 0 sum := 0 sumSquare := 0 db : SEQ real = {} APROC Add(v) = << count := 1; sum +:= v; sumSquare += v 2 ; db += {v}; RET >>

17
CS294, YelickAbstract. Funs., p17 Proof of 2 nd Implementation Proof: The abstraction function for the augmented DB maps db field to the abstract state Need to prove the representation invariants: –count = db.size –sum = sum({x | x in db}) –sumSquare = sum({x 2 | x in db}) Invariants prove that the operations behave correctly, e.g., Size returns the right value.

18
CS294, YelickAbstract. Funs., p18 History Variables In general, we can augment an implementation with history variables such that: –Every initial state of the original machine has a corresponding state with some initial value for the history variables –No existing step is disabled by additional predicates on history variables –A value assigned to an existing component must not depend on the value of a history variable (e.g., return values). Note: the statDB example is extreme, since the entire spec state is added

19
CS294, YelickAbstract. Funs., p19 Examples of History Variables Why do history variables arise? –To simplify the specifications –To optimization the implementations More realistic examples: –Web search Spec talks about the state of the web: “search” looks at arbitrary subset Implementation cannot reproduce state of failed nodes, except by “storing” lost state (may be phantom “history” vars) –Others?

20
CS294, YelickAbstract. Funs., p20 Abstraction Relations An alternative to history variables is to use and “abstraction relation” AR AR maps each concrete state to a set of possible spec states For this example, AR maps a the 3 values to the set of all db’s having the given size, sum, and sum-of-squares. It’s a matter of proof style and taste.

21
CS294, YelickAbstract. Funs., p21 Stuttering Transitions Recall that Lamport and Abadi considered any 2 executions equivalent if one erases “no ops” I.e., (s, , s) Intuition: a single high level operation (e.g., transaction) may be implemented by several smaller steps (atomic in the impl.) A generalized abstraction function allows for 1 step of T to correspond to 0 or >1 steps of S

22
CS294, YelickAbstract. Funs., p22 Abstraction Function (Def 2) A generalized abstraction function F: T -> S has: –If t is any initial state of T, then F(t) is an initial state of S –If t is reachable state of T and (t, , t’) is a step of T, then there is an execution fragment (0 or more step) of S from F(t) to F(t’) having the same trace

23
CS294, YelickAbstract. Funs., p23 Agenda Review of abstraction functions for the memory example History variables Prophecy variables General discussion

24
CS294, YelickAbstract. Funs., p24 Example 2: NonDet (Toy) Specification VAR j := 0 APROC Out() -> Int = << IF j = 0 => BEGIN j := 2 [] j := 3 END; RET 1 [*] RET j FI >> Implementation: VAR j := 0 APROC Out() -> Int = << IF j = 0 => j := 1 [*] j = 1 => BEGIN j := 2 [] j := 3 [*] SKIP FI; RET j >> Both have traces: 1, 2, 2, 2,… and 1, 3, 3, 3, … Do we have AF’s in both directions? Notes say: The “spec” implements the “impl” using the identity abstraction function The reverse AF can’t be defined

25
CS294, YelickAbstract. Funs., p25 Prophecy Variables We can augment an implementation T with prophecy variables to produce TP such that: –Every state of T has a corresponding state with some value for the prophecy variables –No existing step is disabled in the backward direction by additional predicates on prophecy variables. For each step (t, , t’) of T and state (t’, p’) of TP, there must be a value p of the prophecy variable(s), such that ((t,p), (t’, p’)) is a step of TP. –A value assigned to an existing component must not depend on the value of a history variable (e.g., return values). –If t is an initial state of T and (t,p) is a state of TP, then (t,p) must be an initial state of TP

26
CS294, YelickAbstract. Funs., p26 Example 3: Reliable Messages MODULE ReliableMsg [M] EXPORT Put, Get, Crash = VAR q : SEQ M := {} APROC Put(m) = > APROC Get() -> M = > APROC Crash() = q = q’>> Problem: don’t know which messages will be lost at the time of a crash –ensure FIFO delivery –eliminate duplicates from retransmission

27
CS294, YelickAbstract. Funs., p27 Example 4: Queue Given a queue with operations –Enq: add an element to the back of the queue –Deq: remove the element at the front of the queue and return it (Abbreviated E and D on the next slide) (Two processes, A and B)

28
CS294, YelickAbstract. Funs., p28 Some Queue Histories History 1, acceptable History 2, not acceptable History 3, not acceptable q.E(x) A q.D(y) A q.E(z) A q.E(y) B q.D(x) B q.E(x) A q.D(y) A q.E(y) B q.E(x) A q.D(y) A q.E(y) B q.D(y) B

29
CS294, YelickAbstract. Funs., p29 Example 4A: Queue with Locks Given a queue implementation containing –integers, back, front –an array of values, items –a lock, l Enq = proc (q: queue, x: item) // ignoring buffer overflow lock(l) i: int = q.back++ // allocate new slot q.items[i] = x // fill it unlock(l) Deq = proc (q: queue) returns (item) signals empty lock(l) if (back==front) signal empty else front++ ret: item = items[front] unlock (l) return(ret)

30
CS294, YelickAbstract. Funs., p30 Some Queue Histories History 1, acceptable Process A got the lock first during first Enq’s Why didn’t A return immediately after releasing the lock? q.E(x) A q.D(y) A q.E(z) A q.E(y) B q.D(x) B

31
CS294, YelickAbstract. Funs., p31 Simple Abstraction Function The abstraction function maps the elements in items items[front…back] to the abstract queue value Proof is straightward The lock prevents the “interesting” cases

32
CS294, YelickAbstract. Funs., p32 Example 4B: Queue with Atomic Ops Given a queue implementation containing –an integer, back –an array of values, items Enq = proc (q: queue, x: item) i: int = INC(q.back) // allocate new slot, atomic STORE(q.items[i], x) // fill it Deq = proc (q: queue) returns (item) while true do range: int = READ(q.back) - 1 for i: int in 1.. range do x: item = SWAP (q.items[i], null) if x!= null then return x

33
CS294, YelickAbstract. Funs., p33 Queue Example Notes Several atomic operations are defined –STORE, SWAP, INC –These may or may not be supported on given hardware, which would change the proof The deq operation starts at the front end of the queue –slots already dequeued will show up as nulls –slots not yet filled will also be nulls –picks the first non-empty slot –will repeat scan until it finds an element, waiting for an enqueue to happen if necessary Many inefficiencies, such as the lack of a head pointer. Example is to illustrate proof technique.

34
CS294, YelickAbstract. Funs., p34 Need for Prophecy Variables Abstraction function –for this example, a prophecy variable is needed –Two processes, A and B (1 implicit queue) Enq(x) A Enq(y) B INC(q.back) A for this execution, there is no way of defining an abstraction function without INC(q.back) B predicting the future, I.e., whether x or y will be dequeued first STORE(q.items[2], y) B Enq(y) returns on B

35
CS294, YelickAbstract. Funs., p35 Existence of Abstraction Functions There are three cases that arise in trying to prove that an abstraction function exist in a concurrent system The function can be defined directly on the implementation state A history variable needs to be added to the implementation to record a past event A prophecy variable needs to be added to the implementation to record a future event Alternatively, you may use an abstraction relation A(t’)A(t) t t’

36
CS294, YelickAbstract. Funs., p36 General Discussion Examples of distributed programs that are challenges to specify –Bayou consistency model –Oceanstore meta-consistency model –Others Criteria: when is a spec good enough? Examples of algorithms hard to verify Examples of programs hard to verify

Similar presentations

OK

Automated Theorem Proving Lecture 1. Program verification is undecidable! Given program P and specification S, does P satisfy S?

Automated Theorem Proving Lecture 1. Program verification is undecidable! Given program P and specification S, does P satisfy S?

© 2017 SlidePlayer.com Inc.

All rights reserved.

Ads by Google

Ppt on online banking management system Ppt on interest rate derivatives Marketing mix ppt on nokia pc Ppt on world diabetes day Ppt on energy giving food meaning Ppt on waxes lipids Ppt on basic etiquettes Ppt on railway track cross Ppt on job evaluation comments Ppt on components of railway track