Sept COMP60611 Fundamentals of Parallel and Distributed Systems Lecture 9 Promela, jSpin and the problem of Interference John Gurd, Graham Riley Centre for Novel Computing School of Computer Science University of Manchester

Sept Introduction Review of interference. Overview of jSpin and the Promela modelling language Use of assertions in model checking –For checking safety properties Summary

Sept The Oriental Garden problem - Interference People enter an ornamental garden through either of two turnstiles. Management wish to know how many are in the garden at any time. The concurrent program consists of two concurrent threads and a shared people ‘value’ variable. From Magee & Kramer

Sept Oriental garden program int value = 0; main() { pthread_t thread1, thread2; pthread_create( &thread1, NULL, &turnstyle, (void*)0 ); //East pthread_create( &thread2, NULL, &turnstyle, (void*)1 ); //West pthread_join( thread1, NULL); pthread_join( thread2, NULL); printf("Number of people on exit = %d\n",value); exit(0); } The people count (value) and turnstile threads are created by the Garden program as follows:

Sept Turnstyle function void *turnstyle(void *arg) { long id; int arrive; id = (long) arg; for(arrive=0;arrive<GARDEN_MAX;arrive++) { value++; } printf("Turnstyle %d completed\n",id); }

Sept Graphic of possible outcome After the East and West turnstile threads have each incremented the people count 20 times, the garden people counter is not the sum of the counts displayed. Counter increments have been lost. Why? Magee & Kramer

Sept Concurrent processes! Turnstyle threads for east and west may be executing the code for the increment function ‘at the same time’. east west increment: read value write value + 1 program counter program counter PC shared code We know, that without some form of locking to ensure mutual exclusion, writes can be lost and the wrong total computed.

Sept The modelling strategy Our approach is to use a modelling language to explore the behaviour of parallel algorithms at the design stage. –We can, of course, always build models of existing systems to investigate their behaviour. Our models will aim to capture the fundamental aspects of the concurrent behaviour. We wish to use tools to help us explore the behaviour of the models to help ensure that they are correct : –do the right things, and don’t do wrong things –i.e. have the right properties.

Sept Spin and Promela We will take a fairly pragmatic approach to the use of the model checker tool, Spin, and its modelling language Promela. The main aim will be to see how we can use Spin to help us check that our parallel algorithms are ‘correct’. We will not worry too much about exactly how Spin works. –We take an engineering approach… Though we will recognise the important role that the state diagram of a parallel program plays in the process.

Sept jSpin overview A java front end to the Spin model checker –Spin is widely used in industry –See: –See the Reference Sheet for installation instructions jSpin provides a simplified interface to the full Spin model checker. –Good for teaching purposes. –Spin is a sophisticated model checker! Spin takes in models (of concurrent systems) written in the Promela modelling language and provides support for checking properties –of the underlying state transition diagram of the model.

Sept Features of Spin Syntax checking of Promela models. ‘Random’ simulation of a Promela model. –Executes a computation of the program. Promela supports non-deterministic programs so a program may have many computations. Interleaving as well as non-determinism in IF and DO statements (see later) is resolved randomly (i.e. a single computation is executed). ‘Interactive’ execution of a Promela model –The user can explore computations by selecting the next statement to be executed (i.e. the next transition to be taken). ‘Guided’ execution –If Spin finds an error, it can leave a ‘trail’ of a computation which led to the error, which can be examined in Guided mode.

Sept Getting a feel for jSpin An example of a sequential program in Promela can be found in intDivisor.pml –In ~griley/COMP60611/source/labs/extras/intDivisor –Don’t worry about details of the syntax at this point. –Load this program in jSpin. –Execute it in Random mode. –Use Interactive mode to step through the code. –Note the use of assertions to check both pre- and post-conditions on the state of the sequential program. –Verify (shows no errors). –Change dividend to be 16, change the statement: remainder >= divisor; to remainder > divisor; –Verify and note the error reported.

Sept Example Promela code As an example of a parallel program, briefly look at cs_attempt1.pml –In ~griley/COMP60611/source/labs/extras/CriticalSection. –Execute in Random mode. –Execute in Interactive mode and see how the choices of statements are mode available and change during execution. –Verify and note no errors are reported.

Sept More Spin features Spin can be used to check (verify) both safety and liveness properties –Safety mode for safety properties. –Acceptance mode and Non Progress mode safety and liveness properties specified in Temporal Logic – more later. Safety properties can be specified as assertions in the Promela code. –We will look at the use of assertions in a future lecture. Spin supports the description of properties in linear temporal logic (LTL) –Allows the checking of sophisticated safety and liveness properties. –We will take an introductory look at LTL later.

Sept Promela modelling language See Chapter 1 of “Principles of the Spin model Checker”, M. Ben-Ari, Springer, –Available as an electronic resource from the John Rylands library. Also see Principles of Concurrent and Distributer Programming, 2 nd Edition. Ben-Ari. A small language with a C-like syntax designed specifically to build models of concurrent systems that can be ‘checked’ using tools like Spin. Best way to learn a lauguage is to read and play! –So, please read the book(s) and play with models using jSpin.

Sept Programs in Promela Programs consist of a set of Processes –Sequential units of execution –Processes can have both global and local variables Programs do not have to have global variables. Promela supports channels for modelling distributed systems. –Small number of (small) data types bool, byte, int, unsigned etc. –Control statements based on guarded commands Introduced by Edgar Dijkstra. Well suited for expressing non-determinism. You may not be very familiar with guarded commands… yet.

Sept Example: byte n=0; active proctype P() { byte temp; temp = n+1; n = temp; printf( “Process P, n=%d\n”,n); } active proctype Q() { byte temp; temp = n+1; n = temp; printf( “Process Q, n=%d\n”,n); } global variables Process definition. Active means runs on startup. local variables C-like print statement

Sept Notes In Promela, the semicolon (;) is used as a statement separator NOT as a statement terminator, as in C and Java… so they are not always necessary. –This can be confusing… it is a language designers folly! –Usually ok to use semicolons ‘naturally’, the compiler is fairly tolerent.

Sept Modelling choices Statements in Promela are executed atomically. –So n=n+1; is executed atomically in Promela –but as we have seen, in most ‘real’ languages (C, Java, Fortran…) this would be executed as an atomic read of n (to a register variable) followed by an increment, followed by an atomic write (from a register).’ Allowing the possibility of interference. –We can model this with three atomic statements: temp=n; temp=temp+1; n=temp; –We could also model this in Promela as: temp=n+1; n=temp; Two atomic statements (or temp=n; n=temp+1;) We only need to model to the level which shows the effect we are interested in! –In this case, we are modelling interference, so two atomic statements are sufficient.

Sept Alternative version byte n; proctype P() { byte temp; temp = n+1; n = temp; printf( “Process P, n=%d\n”); } init() { n=0 ; atomic { run P(); } Init() is special and is the first process to run. Used to init globals and create other processes. Atomic ensures both instances of P start together.

Sept Init() and final output init() { n=0 ; atomic { run P(); } (_nr_pr == 1) -> printf(“n=%d\n”,n); } Waits for the ‘built- in’ variable containing the number of active processes to be 1 – i.e. only the init() process is ‘alive’ - before printing. This is an example of a guarded command.

Sept Guarded commands – beware! Any alternative whose guard evaluates to true may be executed. –non-determinism The else is only executed if none of the guards is true. Not quite what you are probably used to… If :: a > 6 -> a+1; :: a a-1; :: else -> a=2*a; fi; guard command

Sept Guarded command – do loops Any alternative whose guard evaluates to true may be executed. –non-determinism break exits the loop. Again, not quite what you are probably used to… do :: a > 6 -> a-1; :: a a+1; :: a==6 -> break; od; guard command

Sept Counting loops – two ways #define N 10; active proctype P() { int sum=0; byte i=1; do :: i>N -> break; :: else -> sum=sum+1; i++; od; } #include “for.h” #define N 10; active proctype P() { int sum=0; for (i,1,N) sum=sum+i; rof(i); } A copy of file for.h needs to be available in the local directory.

Sept An unfamiliar concept(?) – blocking statements in Promela In Promela, a conditional statement will only execute if it evaluates to true Otherwise the statement will block –i.e. the process will wait until the expression becomes true: bool my_turn = false; my_turn; /* if my_turn=false, the process blocks */ a=2; /* statement executes once my_turn is set true */ The statement my_turn; is equivalent to my_turn==true; We could have used, say, !his_turn; equivalently! This makes sense in a multi-process environment! It provides a synchronisation mechanism between processes –Presumably, another process will act to make the conditional true at some point to allow the blocked process to progress.

Sept Other features of Promela In Promela, the use of global variables enables the simulation of ‘shared memory’ programs (for execution on shared memory machines). Promela also supports the message passing paradigm through channels. –Channels can be used to pass data directly between the local variables of processes without using shared memory. –Channels enable the modelling of distributed algorithms for use on distributed memory computers. We will focus on shared memory problems –Relevant to programming multi-core processors.

Sept Ornamental Garden - model #include "for.h" byte n=0; byte MAX=5 proctype T() { byte temp; for (i,1,MAX) temp=n+1; n=temp; rof (i); } init { atomic { run T(); } (_nr_pr == 1) -> printf("The value is %d\n", n); assert (n==2*MAX); }

Sept Use jSpin to investigate… Use Random mode and Interactive mode to investigate manually. Use the assertion on the result with Verify to generate a trail that can be examined. Problem: the update to the global variable ‘value’ are not atomic. The read and write statementent from both processes get interleaved and so can produce wrong results. Solution: use locks to ensure ‘mutual exclusion’ in the updates to the shared variable. –i.e. only allow one process at a time to read and update. Do this in the model, to check all computations are ok, then in the code, so any actual computation will be ok – if the implementation is faithfull to the model. –At least we will know the design is correct.

Sept Fix using locks… model #include "for.h“ #include “lock.pml” bool lock=false; byte n=0; byte MAX=5 proctype T() { byte temp; for (i,1,MAX) setLock(lck); temp=n+1; n=temp; releaseLock(lck); rof (i); } init { atomic { run T(); } (_nr_pr == 1) -> printf("The value is %d\n", n); assert (n==2*MAX); }

Sept Test using jSpin and fix program Verify the assertion. That’s it! – tested for all possible computations! Can also use Random and Interactive mode to get a feel for how the program executes. Fix in the program and test: for(arrive=0;arrive<GARDEN_MAX;arrive++) { pthread_mutex_lock(&value_mutex); value++; pthread_mutex_unlock(&value_mutex); }

Sept Hidden assumptions We have modelled the non-atomic behaviour of incrementing in ‘real’ programming languages, i.e. n=n+1; –No reason to think we have missed anthing here. We have modelled the action of pthread locks in Promela (O.k, I have...). We appear to have modelled pthread locks to a level sufficient to demonstrate solving the interference problem. But note there are complexities in the behaviour of locks that we have not modelled –To do with more than two processes and fairness issues. –Our (my!) Promela model does not necessarily generalise to more than two processes. –See this more clearly when we have looked at liveness and fairness. The lesson is to be aware of the limitations of a model. –Practice and experience.

Sept Summary Interference bugs are generally extremely difficult to locate in parallel programs. The general solution is to give processes mutually exclusive access to shared data. –Through, for example, the use of monitor constructs. Encapsulated shared data and mutually exclusice access methods. Our next task is to choose an application to model and see how jSpin can help us to check its correctness. Will choose the classic critical section problem. –Illustrates many (most?) of the common, fundamental concurrency issues. The lessons learned generalise to real, complex applications.