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5.1 Using Compressions

This section outlines the currently available methods for compressing the state machine representing a process, and gives some guidance in how and when to use them.


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5.1.1 Methods of compression

FDR currently provides six different methods of taking a transition system (or state machine) and attempting to compress it into a more efficient one. Each compression function must first be declared by the transparent operator before it can be used (see section Transparent). The functions must be spelt exactly as given below or the relevant operation will not take place — in fact, since they are transparent, FDR will ignore an unknown function (i.e., treat it as the identity function) and simply give a warning in the status window (see section Options).

explicate

Enumeration: by simply tabulating the transition system, rather than deducing the operational semantics “on the fly”. This obviously cannot reduce the number of nodes, but does allow them to be represented by small (integer) values, as opposed to a representation of their natural structure. This in turn makes subsequent manipulations substantially faster.

sbisim

Strong, node-labelled, bisimulation: the standard notion enriched (as discussed in [Roscoe94]) by the minimal acceptance and divergence labelling of the nodes. This was used in FDR1 for the final stage of normalising specifications.

tau_loop_factor
tau-loop

elimination: since a process may choose automatically to follow a tauaction, it follows that all the processes on a tau-loop(or, more properly, in a strongly connected component under tau-reachability)are equivalent.

diamond

Diamond elimination: this carries out the node-compression discussed in the last section systematically, so as to include as few nodes as possible in the output graph. This is perhaps the most novel of the techniques, and the technical details are discussed in Diamond elimination.

normalise

Normalisation: discussed extensively elsewhere,(11) this can give significant gains, but it suffers from the disadvantage that by going through powerspace nodes it can be expensive and lead to expansion.

Normalisation (as described in Checking refinement) is essential (and automatically applied, if needed) for the top level of the left-hand side of a refinement check, but in FDR is made available as a general compression technique through the transparent function normalise. (For historical reasons, this function is also available as normal and normalize.)

model_compress

Factoring by semantic equivalence: the compositional models of CSP we are using all represent much weaker congruences than bisimulation. Therefore, if we can afford to compute the semantic equivalence relation over states it will give better compression than bisimulation to factor by this equivalence relation.

It makes sense to factorise only by the model in which the check is being carried out. This is therefore made an implicit parameter to a single transparent function model_compress. The technical details of this method are discussed in Computing semantic equivalence.

Both tau-loopelimination and factoring by a semantic equivalence use the notion of factoring a GLTS by an equivalence relation; details can be found in [RosEtAl95].


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5.1.2 Compressions in context

FDR will take a complex CSP description and build it up in stages, compressing the resulting process each time. Ultimately we expect these decisions to be at least partly automated, but in current versions almost all compression directives must be included in the syntax of the process in question. At present, the only automatic applications of these techniques are:

One of the most interesting and challenging things when incorporating these ideas is preserving the debugging functionality of the system. The debugging process becomes hierarchical: at the top level we will find erroneous behaviours of compressed parts of the system; we will then have to debug the pre-compressed forms for the appropriate behaviour, and so on down. On very large systems (such as that discussed in the next section) it will not be practical to complete this process for all parts of the system. Therefore the debugging facility initially works out subsystem behaviours down no further than the highest level compressed processes, and only investigates more deeply when directed by the user (as described in The FDR Process Debugger and Debugging).

The way a system is composed together can have an enormous influence on the effectiveness of hierarchical compression. The following principles should generally be followed:

  1. Put processes which communicate with each other together early. For example, in the dining philosophers, you should build up the system out of consecutive fork/philosopher pairs rather than putting the philosophers all together, the forks all together and then putting these two processes together at the highest level.
  2. Hide all events at as low a level as is possible. The laws of CSP allow the movement of hiding inside and outside a parallel operator as long as its synchronisations are not interfered with. In general therefore, any event that is to be hidden should be hidden the first time (in building up the process) that it no longer has to be synchronised at a higher level. The reason for this is that the compression techniques all tend to work much more effectively on systems with many tau-actions.
  3. Hide all events that are irrelevant to the specification you are trying to prove.

Hiding can introduce divergence, and therefore invalidate many failures/divergences model specifications. However, in the traces model it does not alter the sequence of unhidden events, and in the stable failures model does not alter refusals which contain every hidden event. Therefore if you are only trying to prove a property in one of these models — or if it has already been established by whatever method that the system is divergence free — the improved compression we get by hiding extra events makes it worthwhile doing so.

Two examples of this, one based on the COPY chain example we saw above and one on the dining philosophers are discussed in more detail in [RosEtAl95]. The first is probably typical of the gains we can make with compression and hiding; the second is atypically good.


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5.1.3 Hiding and safety properties

If the underlying datatype T of the COPY processes is large, then chaining N of them together will lead to unmanageably large state-spaces whatever sort of compression is applied to the entire system. Suppose x is one member of the type T; an obviously desirable (and true) property of the COPY chain is that the number of x’s input on channel left is always greater than or equal to the number output on right, but no greater than the latter plus N. Since the truth or falsity of this property is unaffected by the system’s communications in the rest of its alphabet, {left.y,right.y | y<-Sigma \ {x}},we can hide this set and build the network up a process at a time from left to right. At the intermediate stages you have to leave the right-hand communications unhidden (because these still have to be synchronised with processes yet to be built in) but nevertheless, in the traces model, the state space of the intermediate stages grows more slowly with n than without the hiding. In fact, with n COPY processes the hidden version compresses to exactly 2 n [2 to the power n]states whatever the size of T (assuming that this is at least 2).

If the (albeit slower) exponential growth of states even after hiding and compressing the actual system is unacceptable, there is one further option: find a network with either fewer states, or better compression behaviour, that the actual one refines, but which can still be shown to satisfy the specification. In the example above this is easy: simply replace COPY with

Cx=(mu P . left.x -> right.x -> P) ||| CHAOS(Sigma \ {left.x,right.x})

the process which acts like a reliable one-place buffer for the value x, but can input and output as it chooses on other members of T (for the definition of CHAOS, see Processes). It is easy to show that COPY refines this, and a chain of n Cx'scompresses to n+1 states (even without hiding irrelevant external communications).

The methods discussed in this section can be used to prove properties about the reliability of communications between a given pair of nodes in a complex environment, and similar cases where the full complexity of the operation of a system is irrelevant to why a particular property is true.


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5.1.4 Hiding and deadlock

In the stable failures model, a system P can deadlock if and only if P \ Sigmacan. In other words, we can hide absolutely all events — and move this hiding as far into the process as possible using the principles already discussed.

Consider the case of the N dining philosophers (in a version, for simplicity, without a Butler process)(12). A way of building this system up hierarchically is as progressively longer chains of the form

PHIL0|| FORK0|| PHIL1|| ... FORKi-1|| PHILi

In analysing the whole system for deadlock, we can hide all those events of a subsystem that do not synchronise with any process outside the subsystem. Thus, in this case we can hide all events other than the interactions between PHIL0 and FORKN-1, and between PHILm and FORKm.The failures normal form of the subsystem will have very few states (exactly 4). Thus, we can compute the failures normal form of the whole hidden system, adding a small fixed number of philosopher/fork combinations at a time, in time proportional to N, even though an explicit model-checker would find exponentially many states.

We can, in fact, do even better than this. Imagine doing the following:

By this method we can produce a model of 10 N [10 to the power N]philosophers and forks in a row in time proportional to N. To make them into a ring, all you would have to do would be to add another row of one or more philosophers and forks in parallel, synchronising the two at both ends. Depending on how it was built (such as whether all the philosophers are allowed to act with a single-handedness) you would either find deadlock or prove it absent from a system with doubly exponential number of states.

This example is, of course, extraordinarily well-suited to our methods. What makes it work are firstly the fact that the networks we build up have a constant-sized external interface (which could only happen in networks that were, like this one, chains or nearly so) and have a behaviour that compresses to a bounded size as the network grows.

On the whole we do not have to prove deadlock freedom of quite such absurdly large systems. We expect that our methods will also bring great improvements to the deadlock checking of more usual size ones that are not necessarily as perfectly suited to them as the example above.


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