Range Allocation for Separation Logic

Talupur, Muralidhar; Sinha, Nishant; Strichman, Ofer; Pnueli, Amir

doi:10.1007/978-3-540-27813-9_12

Cited by 21 publications

(28 citation statements)

References 9 publications

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“…It has to be noted that UCLID could perhaps improve its performance by using the most recent range allocation techniques of [TSSP04], and that ICS applies a more general solver for linear arithmetic, rather than a specialized solver for difference logic as MathSAT, TSAT++ and DPLL(T ) do.…”

Section: Experimental Evaluationmentioning

confidence: 99%

“…For example, for EUF there exist the per-constraint encoding [BV02], the small domain encoding [PRSS99,BLS02], and several hybrid approaches [SLB03]. Similarly, for difference logic, sophisticated range-allocation approaches have been defined in order to improve the translations [TSSP04]. But, in spite of this, on many practical problems the translation process or the SAT solver run out of time or memory (see [dMR04]).…”

Section: Introductionmentioning

confidence: 99%

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DPLL(T) with Exhaustive Theory Propagation and Its Application to Difference Logic

Nieuwenhuis

Oliveras

2005

Computer Aided Verification

121

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Abstract. At CAV'04 we presented the DPLL(T ) approach for satisfiability modulo theories T . It is based on a general DPLL(X) engine whose X can be instantiated with different theory solvers Solver T for conjunctions of literals. Here we go one important step further: we require Solver T to be able to detect all input literals that are T -consequences of the partial model that is being explored by DPLL(X). Although at first sight this may seem too expensive, we show that for difference logic the benefits compensate by far the costs. Here we describe and discuss this new version of DPLL(T ), the DPLL(X) engine, and our Solver T for difference logic. The resulting very simple DPLL(T ) system importantly outperforms the existing techniques for this logic. Moreover, it has very good scaling properties: especially on the larger problems it gives improvements of orders of magnitude w.r.t. the existing state-of-the-art tools.

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Section: Experimental Evaluationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

DPLL(T) with Exhaustive Theory Propagation and Its Application to Difference Logic

Nieuwenhuis

Oliveras

2005

Computer Aided Verification

121

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“…Both the eager and lazy approaches have relative strengths and weaknesses. Though the small model encoding approaches [12,20] reduce the range space allocated to a finite domain, Boolean encoding of the formula often leads to large propositional logic formula, eclipsing the advantage gained from the reduced search space. Researchers [14] have also experimented with the pseudo-Boolean Solver PBS [21] to obtain a polynomial size formula, but without any significant performance gain.…”

Section: Introductionmentioning

confidence: 99%

SDSAT: Tight Integration of Small Domain Encoding and Lazy Approaches in a Separation Logic Solver

Ganai

Talupur

Gupta

2006

Tools and Algorithms for the Construction and Analysis of Systems

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Existing Separation Logic (a.k.a Difference Logic, DL) solvers can be broadly classified as eager or lazy, each with its own merits and de-merits. We propose a novel Separation Logic Solver SDSAT that combines the strengths of both these approaches and provides a robust performance over a wide set of benchmarks. The solver SDSAT works in two phases: allocation and solve. In the allocation phase, it allocates non-uniform adequate ranges for variables appearing in separation predicates. This phase is similar to previous small domain encoding approaches, but uses a novel algorithm Nu-SMOD with 1-2 orders of magnitude improvement in performance and smaller ranges for variables. Furthermore, the Separation Logic formula is not transformed into an equi-satisfiable Boolean formula in one step, but rather done lazily in the following phase. In the solve phase, SDSAT uses a lazy refinement approach to search for a satisfying model within the allocated ranges. Thus, any partially DL-theory consistent model can be discarded if it can not be satisfied within the allocated ranges. Note the crucial difference: in eager approaches, such a partially consistent model is not allowed in the first place, while in lazy approaches such a model is never discarded. Moreover, we dynamically refine the allocated ranges and search for a feasible solution within the updated ranges. This combined approach benefits from both the smaller search space (as in eager approaches) and also from the theory-specific graph-based algorithms (characteristic of lazy approaches). Experimental results show that our method is robust and always better than or comparable to state-of-the art solvers.

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“…If the constant term c is always zero, a sufficient number of bits for representing the bounded integers is log 2 |V | , so that every symbol in V can potentially be assigned a unique integer value. The small-domain encoding is studied in [4,25] for full difference logic over integers.…”

Section: Encoding Restricted Difference Logic In Satmentioning

confidence: 99%

Checking Bounded Reachability in Asynchronous Systems by Symbolic Event Tracing

Dubrovin¹

2010

Lecture Notes in Computer Science

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This report presents a new symbolic technique for checking reachability properties of asynchronous systems by reducing the problem to satisfiability in restrained difference logic. The analysis is bounded by fixing a finite set of potential events, each of which may occur at most once in any order. The events are specified using high-level Petri nets. The logic encoding describes the space of possible causal links between events rather than possible sequences of states as in Bounded Model Checking. Independence between events is exploited intrinsically without partial order reductions, and the handling of data is symbolic. On a family of benchmarks, the proposed approach is consistently faster than Bounded Model Checking. In addition, this report presents a compact encoding of the restrained subset of difference logic in propositional logic.

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Range Allocation for Separation Logic

Cited by 21 publications

References 9 publications

DPLL(T) with Exhaustive Theory Propagation and Its Application to Difference Logic

DPLL(T) with Exhaustive Theory Propagation and Its Application to Difference Logic

SDSAT: Tight Integration of Small Domain Encoding and Lazy Approaches in a Separation Logic Solver

Checking Bounded Reachability in Asynchronous Systems by Symbolic Event Tracing

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