Parallelizing SMT Solving: Lazy Decomposition and Conciliation

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Artificial Intelligence

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Satisfiability Modulo Theories (SMT) is the satisfiability problem for first-order formulae with respect to background theories. SMT extends the propositional satisfiability by introducing various underlying theories. To improve the efficiency of SMT solving, many efforts have been made on low-level algorithms but they generally cannot leverage the capability of parallel hardware. We propose a high-level and flexible framework, namely lazy decomposition and conciliation (LDC), to parallelize solving for quantifier-free SMT problems. Overall, a SMT problem is firstly decomposed into subproblems, then local reasoning inside each subproblem is conciliated with the global reasoning over the shared symbols across subproblems in parallel. LDC can be built on any existing solver without tuning its internal implementation, and is flexible as it is applicable to various underlying theories. We instantiate LDC in the theory of equality with uninterpreted functions, and implement a parallel solver PZ3 based on Z3. Experiment results on the QF_UF benchmarks from SMT-LIB as well as random problems show the potential of LDC, as (1) PZ3 generally outperforms Z3 in 4 out of 8 problem subcategories under various core configurations; (2) PZ3 usually achieves super-linear speed-up over Z3 on problems with sparse structures, which makes it possible to choose an appropriate solver from Z3 and PZ3 in advance according to the structure of input problem; (3) compared to PCVC4, a state-of-the-art portfolio-based parallel SMT solver, PZ3 achieves speed-up on a larger portion of problems and has better overall speed-up ratio.



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