source: Papers/jar-cerco-2017/cerco.tex @ 3604

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64
65\title{CerCo: Certified Complexity\thanks{The project CerCo acknowledges the
66financial support of the Future and Emerging Technologies (FET) programme within
67the Seventh Framework Programme for Research of the European Commission, under
68FET-Open grant number: 243881}}
69\subtitle{Verified lifting of concrete complexity annotations through a realistic C compiler}
70\journalname{Journal of Automated Reasoning}
71\titlerunning{Certified Complexity}
72\date{Received: date / Accepted: date}
73\author{Jaap Boender \and Brian Campbell \and Dominic P. Mulligan \and Claudio {Sacerdoti Coen}}
74\authorrunning{Boender, Campbell, Mulligan, and Sacerdoti Coen}
75\institute{Jaap Boender \at
76              Faculty of Science and Technology,\\
77                                                        Middlesex University London,\\
78                                                        United Kingdom.\\
79              \email{J.Boender@mdx.ac.uk}
80           \and
81           Brian Campbell \at
82              Department of Informatics,\\
83              University of Edinburgh,\\
84              United Kingdom.\\
85              \email{Brian.Campbell@ed.ac.uk}
86           \and
87           Dominic P. Mulligan \at
88             Computer Laboratory,\\
89             University of Cambridge, \\
90             United Kingdom.\\
91             \email{Dominic.Mulligan@cl.cam.ac.uk }
92           \and
93           Claudio Sacerdoti Coen \at
94              Dipartimento di Informatica---Scienza e Ingegneria (DISI),\\
95              University of Bologna,\\
96              Italy.\\
97              \email{Claudio.SacerdotiCoen@unibo.it}}
98
99\begin{document}
100
101\maketitle
102
103\begin{abstract}
104We provide an overview of the FET-Open Project CerCo (`Certified Complexity').
105Our main achievement is the development of a technique for analysing non-functional properties of programs (time, space) at the source level with little or no loss of accuracy and a small trusted code base.
106The core component is a C compiler, verified in the Matita theorem prover, that produces an instrumented copy of the source code in addition to generating object code.
107This instrumentation exposes, and tracks precisely, the actual (non-asymptotic) computational cost of the input program at the source level.
108Untrusted invariant generators and trusted theorem provers may then be used to compute and certify the parametric execution time of the code.
109\keywords{Verified compilation \and Complexity analysis \and CerCo (`Certified Complexity')}
110\end{abstract}
111
112\section{Introduction}
113\label{sect.introduction}
114
115% ---------------------------------------------------------------------------- %
116% SECTION                                                                      %
117% ---------------------------------------------------------------------------- %
118\section{Introduction}
119
120%\paragraph{Problem statement.}
121Programs can be specified with both
122functional constraints (what the program must do) and non-functional constraints (what time, space or other resources the program may use).  In the current
123state of the art, functional properties are verified
124by combining user annotations---preconditions, invariants, and so on---with a
125multitude of automated analyses---invariant generators, type systems, abstract
126interpretation, theorem proving, and so on---on the program's high-level source code.
127By contrast, many non-functional properties
128are verified using analyses on low-level object code,
129%\footnote{A notable
130%  exception is the explicit allocation of heap space in languages like C, which
131%  can be handled at the source level.}
132but these analyses may then need information
133about the high-level functional behaviour of the program that must then be reconstructed.
134This analysis on low-level object code has several problems:
135\begin{itemize*}
136\item
137It can be hard to deduce the high-level structure of the program after compiler optimisations.
138The object code produced by an optimising compiler may have radically different control flow to the original source code program.
139\item
140Techniques that operate on object code are not useful early in the development process of a program, yet problems with a program's design or implementation are cheaper to resolve earlier in the process, rather than later.
141\item
142Parametric cost analysis is very hard: how can we reflect a cost that depends on the execution state, for example the
143value of a register or a carry bit, to a cost that the user can understand
144looking at the source code?
145\item
146Performing functional analyses on object code makes it hard for the programmer to provide information about the program and its expected execution, leading to a loss of precision in the resulting analyses.
147\end{itemize*}
148\paragraph{Vision and approach.}
149We want to reconcile functional and
150non-functional analyses: to share information and perform both at the same time
151on high-level source code.
152%
153What has previously prevented this approach is the lack of a uniform and precise
154cost model for high-level code as each statement occurrence is compiled
155differently, optimisations may change control flow, and the cost of an object
156code instruction may depend on the runtime state of hardware components like
157pipelines and caches, all of which are not visible in the source code.
158
159We envision a new generation of compilers that track program structure through compilation and optimisation and exploit this
160information to define a precise, non-uniform cost model for source code that accounts for runtime state. With such a cost model we can
161reduce non-functional verification to the functional case and exploit the state
162of the art in automated high-level verification~\cite{survey}. The techniques
163currently used by the Worst Case Execution Time (WCET) community, who perform analyses on object code,
164are still available but can be coupled with additional source-level
165analyses. Where our approach produces overly complex cost models, safe approximations can be used to trade complexity with precision.
166Finally, source code analysis can be used early in the development process, when
167components have been specified but not implemented, as modularity means
168that it is enough to specify the non-functional behaviour of missing
169components.
170\paragraph{Contributions.}
171We have developed \emph{the labelling approach}~\cite{labelling}, a
172technique to implement compilers that induce cost models on source programs by
173very lightweight tracking of code changes through compilation. We have studied
174how to formally prove the correctness of compilers implementing this technique, and
175have implemented such a compiler from C to object binaries for the 8051
176microcontroller for predicting execution time and stack space usage,
177verifying it in an interactive theorem prover.  As we are targeting
178an embedded microcontroller we do not consider dynamic memory allocation.
179
180To demonstrate source-level verification of costs we have implemented
181a Frama-C plugin~\cite{framac} that invokes the compiler on a source
182program and uses it to generate invariants on the high-level source
183that correctly model low-level costs. The plugin certifies that the
184program respects these costs by calling automated theorem provers, a
185new and innovative technique in the field of cost analysis. Finally,
186we have conducted several case studies, including showing that the
187plugin can automatically compute and certify the exact reaction time
188of Lustre~\cite{lustre} data flow programs compiled into C.
189
190\section{Project context and approach}
191Formal methods for verifying functional properties of programs have 
192now reached a level of maturity and automation that their adoption is slowly
193increasing in production environments. For safety critical code, it is
194becoming commonplace to combine rigorous software engineering methodologies and testing
195with static analyses, taking the strengths of each and mitigating
196their weaknesses. Of particular interest are open frameworks
197for the combination of different formal methods, where the programs can be
198progressively specified and enriched with new safety
199guarantees: every method contributes knowledge (e.g. new invariants) that
200becomes an assumption for later analysis.
201
202The outlook for verifying non-functional properties of programs (time spent,
203memory used, energy consumed) is bleaker.
204% and the future seems to be getting even worse.
205Most industries verify that real time systems meet their deadlines
206by simply performing many runs of the system and timing their execution,  computing the
207maximum time and adding an empirical safety margin, claiming the result to be a
208bound for the WCET of the program. Formal methods and software to statically
209analyse the WCET of programs exist, but they often produce bounds that are too
210pessimistic to be useful. Recent advancements in hardware architecture
211have been
212focused on the improvement of the average case performance, not the
213predictability of the worst case. Execution time is becoming increasingly
214dependent on execution history and the internal state of
215hardware components like pipelines and caches. Multi-core processors and non-uniform
216memory models are drastically reducing the possibility of performing
217static analysis in isolation, because programs are less and less time
218composable. Clock-precise hardware models are necessary for static analysis, and
219obtaining them is becoming harder due to the increased sophistication
220of hardware design.
221
222Despite these problems, the need for reliable real time
223systems and programs is increasing, and there is pressure
224from the research community for the introduction of
225hardware with more predictable behaviour, which would be more suitable
226for static analysis.  One example, being investigated by the Proartis
227project~\cite{proartis}, is to decouple execution time from execution
228history by introducing randomisation.
229
230In CerCo~\cite{cerco} we do not address this problem, optimistically
231assuming that improvements in low-level timing analysis or architecture will make
232verification feasible in the longer term. Instead, the main objective of our work is
233to bring together the static analysis of functional and non-functional
234properties, which in the current state of the art are
235independent activities with limited exchange of information: while the
236functional properties are verified on the source code, the analysis of
237non-functional properties is performed on object code to exploit
238clock-precise hardware models.
239
240\subsection{Current object-code methods}
241
242Analysis currently takes place on object code for two main reasons.
243First, there cannot be a uniform, precise cost model for source
244code instructions (or even basic blocks). During compilation, high level
245instructions are broken up and reassembled in context-specific ways so that
246identifying a fragment of object code and a single high level instruction is
247infeasible. Even the control flow of the object and source code can be very
248different as a result of optimisations, for example aggressive loop
249optimisations may completely transform source level loops. Despite the lack of a uniform, compilation- and
250program-independent cost model on the source language, the literature on the
251analysis of non-asymptotic execution time on high level languages assuming
252such a model is growing and gaining momentum. However, unless we provide a
253replacement for such cost models, this literature's future practical impact looks
254to be minimal. Some hope has been provided by the EmBounded project \cite{embounded}, which
255compositionally compiles high-level code to a byte code that is executed by an
256interpreter with guarantees on the maximal execution time spent for each byte code
257instruction. This provides a uniform model at the expense of the model's
258precision (each cost is a pessimistic upper bound) and the performance of the
259executed code (because the byte code is interpreted compositionally instead of
260performing a fully non-compositional compilation).
261
262The second reason to perform the analysis on the object code is that bounding
263the worst case execution time of small code fragments in isolation (e.g. loop
264bodies) and then adding up the bounds yields very poor estimates as no
265knowledge of the hardware state prior to executing the fragment can be assumed. By
266analysing longer runs the bound obtained becomes more precise because the lack
267of information about the initial state has a relatively small impact.
268
269To calculate the cost of an execution, value and control flow analyses
270are required to bound the number of times each basic block is
271executed.  Currently, state
272of the art WCET analysis tools, such as AbsInt's aiT toolset~\cite{absint}, perform these analyses on
273object code, where the logic of the program is harder to reconstruct
274and most information available at the source code level has been lost;
275see~\cite{stateart} for a survey. Imprecision in the analysis can lead to useless bounds. To
276augment precision, the tools ask the user to provide constraints on
277the object code control flow, usually in the form of bounds on the
278number of iterations of loops or linear inequalities on them. This
279requires the user to manually link the source and object code,
280translating his assumptions on the source code (which may be wrong) to
281object code constraints. The task is error prone and hard, especially
282in the presence of complex compiler optimisations.
283
284Traditional techniques for WCET that work on object code are also affected by
285another problem: they cannot be applied before the generation of the object
286code. Functional properties can be analysed in early development stages, while
287analysis of non-functional properties may come too late to avoid expensive
288changes to the program architecture.
289
290\subsection{CerCo's approach}
291
292In CerCo we propose a radically new approach to the problem: we reject the idea
293of a uniform cost model and we propose that the compiler, which knows how the
294code is translated, must return the cost model for basic blocks of high level
295instructions. It must do so by keeping track of the control flow modifications
296to reverse them and by interfacing with processor timing analysis.
297
298By embracing compilation, instead of avoiding it like EmBounded did, a CerCo
299compiler can both produce efficient code and return costs that are
300as precise as the processor timing analysis can be. Moreover, our costs can be
301parametric: the cost of a block can depend on actual program data, on a
302summary of the execution history, or on an approximated representation of the
303hardware state. For example, loop optimisations may assign a cost to a loop body
304that is a function of the number of iterations performed. As another example,
305the cost of a block may be a function of the vector of stalled pipeline states,
306which can be exposed in the source code and updated at each basic block exit. It
307is parametricity that allows one to analyse small code fragments without losing
308precision. In the analysis of the code fragment we do not have to ignore the
309initial hardware state, rather, we may assume that we know exactly which
310state (or mode, as the WCET literature calls it) we are in.
311
312The CerCo approach has the potential to dramatically improve the state of the
313art. By performing control and data flow analyses on the source code, the error
314prone translation of invariants is completely avoided. Instead, this
315work is done at the source level using tools of the user's choice.
316Any available technique for the verification of functional properties
317can be immediately reused and multiple techniques can collaborate together to
318infer and certify cost invariants for the program.  There are no
319limitations on the types of loops or data structures involved. Parametric cost analysis
320becomes the default one, with non-parametric bounds used as a last resort when the user
321decides to trade the complexity of the analysis with its precision. \emph{A priori}, no
322technique previously used in traditional WCET is lost: processor
323timing analyses can be used by the compiler on the object code, and the rest can be applied
324at the source code level.
325 Our approach can also work in the early
326stages of development by axiomatically attaching costs to unimplemented components.
327
328
329Software used to verify properties of programs must be as bug free as
330possible. The trusted code base for verification consists of the code that needs
331to be trusted to believe that the property holds. The trusted code base of
332state-of-the-art WCET tools is very large: one needs to trust the control flow
333analyser, the linear programming libraries used, and also the formal models
334of the hardware under analysis, for example. In CerCo we are moving the control flow analysis to the source
335code and we are introducing a non-standard compiler too. To reduce the trusted
336code base, we implemented a prototype and a static analyser in an interactive
337theorem prover, which was used to certify that the costs added to the source
338code are indeed those incurred by the hardware. Formal models of the
339hardware and of the high level source languages were also implemented in the
340interactive theorem prover. Control flow analysis on the source code has been
341obtained using invariant generators, tools to produce proof obligations from
342generated invariants and automatic theorem provers to verify the obligations. If
343these tools are able to generate proof traces that can be
344independently checked, the only remaining component that enters the trusted code
345base is an off-the-shelf invariant generator which, in turn, can be proved
346correct using an interactive theorem prover. Therefore we achieve the double
347objective of allowing the use of more off-the-shelf components (e.g. provers and
348invariant generators) whilst reducing the trusted code base at the same time.
349
350%\paragraph{Summary of the CerCo objectives.} To summarize, the goal of CerCo is
351% to reconcile functional with non-functional analysis by performing them together
352% on the source code, sharing common knowledge about execution invariants. We want
353% to achieve the goal implementing a new generation of compilers that induce a
354% parametric, precise cost model for basic blocks on the source code. The compiler
355% should be certified using an interactive theorem prover to minimize the trusted
356% code base of the analysis. Once the cost model is induced, off-the-shelf tools
357% and techniques can be combined together to infer and prove parametric cost
358% bounds.
359%The long term benefits of the CerCo vision are expected to be:
360%1. the possibility to perform static analysis during early development stages
361%2.  parametric bounds made easier
362%3.  the application of off-the-shelf techniques currently unused for the
363% analysis of non-functional properties, like automated proving and type systems
364%4. simpler and safer interaction with the user, that is still asked for
365% knowledge, but on the source code, with the additional possibility of actually
366% verifying the provided knowledge
367%5. a reduced trusted code base
368%6. the increased accuracy of the bounds themselves.
369%
370%The long term success of the project is hindered by the increased complexity of
371% the static prediction of the non-functional behaviour of modern hardware. In the
372% time frame of the European contribution we have focused on the general
373% methodology and on the difficulties related to the development and certification
374% of a cost model inducing compiler.
375
376\section{The typical CerCo workflow}
377\label{sec:workflow}
378\begin{figure}[!t]
379\begin{tabular}{l@{\hspace{0.2cm}}|@{\hspace{0.2cm}}l}
380\begin{lstlisting}
381char a[] = {3, 2, 7, 14};
382char threshold = 4;
383
384int count(char *p, int len) {
385  char j;
386  int found = 0;
387  for (j=0; j < len; j++) {
388    if (*p <= threshold)
389      found++;
390    p++;
391    }
392  return found;
393}
394
395int main() {
396  return count(a,4);
397}
398\end{lstlisting}
399&
400%  $\vcenter{\includegraphics[width=7.5cm]{interaction_diagram.pdf}}$
401\begin{tikzpicture}[
402    baseline={([yshift=-.5ex]current bounding box)},
403    element/.style={draw, text width=1.6cm, on chain, text badly centered},
404    >=stealth
405    ]
406{[start chain=going below, node distance=.5cm]
407\node [element] (cerco) {CerCo\\compiler};
408\node [element] (cost)  {CerCo\\cost plugin};
409{[densely dashed]
410\node [element] (ded)   {Deductive\\platform};
411\node [element] (check) {Proof\\checker};
412}
413}
414\coordinate [left=3.5cm of cerco] (left);
415{[every node/.style={above, text width=3.5cm, text badly centered,
416                     font=\scriptsize}]
417\draw [<-] ([yshift=-1ex]cerco.north west) coordinate (t) --
418    node {C source}
419    (t-|left);
420\draw [->] (cerco) -- (cost);
421\draw [->] ([yshift=1ex]cerco.south west) coordinate (t) --
422    node {C source+\color{red}{cost annotations}}
423    (t-|left) coordinate (cerco out);
424\draw [->] ([yshift=1ex]cost.south west) coordinate (t) --
425    node {C source+\color{red}{cost annotations}\\+\color{blue}{synthesized assertions}}
426    (t-|left) coordinate (out);
427{[densely dashed]
428\draw [<-] ([yshift=-1ex]ded.north west) coordinate (t) --
429    node {C source+\color{red}{cost annotations}\\+\color{blue}{complexity assertions}}
430    (t-|left) coordinate (ded in) .. controls +(-.5, 0) and +(-.5, 0) .. (out);
431\draw [->] ([yshift=1ex]ded.south west) coordinate (t) --
432    node {complexity obligations}
433    (t-|left) coordinate (out);
434\draw [<-] ([yshift=-1ex]check.north west) coordinate (t) --
435    node {complexity proof}
436    (t-|left) .. controls +(-.5, 0) and +(-.5, 0) .. (out);
437\draw [dash phase=2.5pt] (cerco out) .. controls +(-1, 0) and +(-1, 0) ..
438    (ded in);
439}}
440%% user:
441% head
442\draw (current bounding box.west) ++(-.2,.5)
443    circle (.2) ++(0,-.2) -- ++(0,-.1) coordinate (t);
444% arms
445\draw (t) -- +(-.3,-.2);
446\draw (t) -- +(.3,-.2);
447% body
448\draw (t) -- ++(0,-.4) coordinate (t);
449% legs
450\draw (t) -- +(-.2,-.6);
451\draw (t) -- +(.2,-.6);
452\end{tikzpicture}
453\end{tabular}
454\caption{On the left: C code to count the number of elements in an array
455 that are less than or equal to a given threshold. On the right: CerCo's interaction
456 diagram. Components provided by CerCo are drawn with a solid border.}
457\label{test1}
458\end{figure}
459We illustrate the workflow we envisage (on the right
460of~\autoref{test1}) on an example program (on the left
461of~\autoref{test1}).  The user writes the program and feeds it to the
462CerCo compiler, which outputs an instrumented version of the same
463program that updates global variables that record the elapsed
464execution time and the stack space usage.  The red lines in
465\autoref{itest1} introducing variables, functions and function calls
466starting with \lstinline'__cost' and \lstinline'__stack' are the instrumentation introduced by the
467compiler.  For example, the two calls at the start of
468\lstinline'count' say that 4 bytes of stack are required, and that it
469takes 111 cycles to reach the next cost annotation (in the loop body).
470The compiler measures these on the labelled object code that it generates.
471
472 The annotated program can then be enriched with complexity
473assertions in the style of Hoare logic, that are passed to a deductive
474platform (in our case Frama-C). We provide as a Frama-C cost plugin a
475simple automatic synthesiser for complexity assertions which can
476be overridden by the user to increase or decrease accuracy.  These are the blue
477comments starting with \lstinline'/*@' in \autoref{itest1}, written in
478Frama-C's specification language, ACSL.  From the
479assertions, a general purpose deductive platform produces proof
480obligations which in turn can be closed by automatic or interactive
481provers, ending in a proof certificate.
482
483% NB: if you try this example with the live CD you should increase the timeout
484
485Twelve proof obligations are generated from~\autoref{itest1} (to prove
486that the loop invariant holds after one execution if it holds before,
487to prove that the whole program execution takes at most 1358 cycles, and so on).  Note that the synthesised time bound for \lstinline'count',
488$178+214*(1+\text{\lstinline'len'})$ cycles, is parametric in the length of
489the array. The CVC3 prover
490closes all obligations within half a minute on routine commodity
491hardware.  A simpler non-parametric version can be solved in a few
492seconds.
493%
494\fvset{commandchars=\\\{\}}
495\lstset{morecomment=[s][\color{blue}]{/*@}{*/},
496        moredelim=[is][\color{blue}]{$}{$},
497        moredelim=[is][\color{red}]{|}{|},
498        lineskip=-2pt}
499\begin{figure}[!p]
500\begin{lstlisting}
501|int __cost = 33, __stack = 5, __stack_max = 5;|
502|void __cost_incr(int incr) { __cost += incr; }|
503|void __stack_incr(int incr) {
504  __stack += incr;
505  __stack_max = __stack_max < __stack ? __stack : __stack_max;
506}|
507
508char a[4] = {3, 2, 7, 14};  char threshold = 4;
509
510/*@ behavior stack_cost:
511      ensures __stack_max <= __max(\old(__stack_max), 4+\old(__stack));
512      ensures __stack == \old(__stack);
513    behavior time_cost:
514      ensures __cost <= \old(__cost)+(178+214*__max(1+\at(len,Pre), 0));
515*/
516int count(char *p, int len) {
517  char j;  int found = 0;
518  |__stack_incr(4);  __cost_incr(111);|
519  $__l: /* internal */$
520  /*@ for time_cost: loop invariant
521        __cost <= \at(__cost,__l)+
522                  214*(__max(\at((len-j)+1,__l), 0)-__max(1+(len-j), 0));
523      for stack_cost: loop invariant
524        __stack_max == \at(__stack_max,__l);
525      for stack_cost: loop invariant
526        __stack == \at(__stack,__l);
527      loop variant len-j;
528  */
529  for (j = 0; j < len; j++) {
530    |__cost_incr(78);|
531    if (*p <= threshold) { |__cost_incr(136);| found ++; }
532    else { |__cost_incr(114);| }
533    p ++;
534  }
535  |__cost_incr(67);  __stack_incr(-4);|
536  return found;
537}
538
539/*@ behavior stack_cost:
540      ensures __stack_max <= __max(\old(__stack_max), 6+\old(__stack));
541      ensures __stack == \old(__stack);
542    behavior time_cost:
543      ensures __cost <= \old(__cost)+1358;
544*/
545int main(void) {
546  int t;
547  |__stack_incr(2);  __cost_incr(110);|
548  t = count(a,4);
549  |__stack_incr(-2);|
550  return t;
551}
552\end{lstlisting}
553\caption{The instrumented version of the program in \autoref{test1},
554 with instrumentation added by the CerCo compiler in red and cost invariants
555 added by the CerCo Frama-C plugin in blue. The \lstinline'__cost',
556 \lstinline'__stack' and \lstinline'__stack_max' variables hold the elapsed time
557in clock cycles and the current and maximum stack usage. Their initial values
558hold the clock cycles spent in initialising the global data before calling
559\lstinline'main' and the space required by global data (and thus unavailable for
560the stack).
561}
562\label{itest1}
563\end{figure}
564
565\begin{acknowledgements}
566\end{acknowledgements}
567
568%\bibliographystyle{spbasic}      % basic style, author-year citations
569\bibliography{cerco}   % name your BibTeX data base
570
571\end{document}
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