Efficient Satisfiability Modulo Theories via Delayed Theory Combination

2005 ◽  
pp. 335-349 ◽  
Author(s):  
Marco Bozzano ◽  
Roberto Bruttomesso ◽  
Alessandro Cimatti ◽  
Tommi Junttila ◽  
Silvio Ranise ◽  
...  
Keyword(s):  
Satisfiability Modulo Theories ◽  
Theory Combination

scholarly journals Delayed theory combination vs. Nelson-Oppen for satisfiability modulo theories: a comparative analysis

2009 ◽  
Vol 55 (1-2) ◽  
pp. 63-99 ◽  
Author(s):  
Roberto Bruttomesso ◽  
Alessandro Cimatti ◽  
Anders Franzen ◽  
Alberto Griggio ◽  
Roberto Sebastiani
Keyword(s):  
Comparative Analysis ◽  
Satisfiability Modulo Theories ◽  
Theory Combination

Engineering Theories with Z3

10.29007/x7b4 ◽  
2018 ◽  
Author(s):  
Nikolaj Bjorner
Keyword(s):  
Satisfiability Modulo Theories ◽  
Smt Solver ◽  
Testing Tools ◽  
Software And Hardware ◽  
Design And Testing

Modern Satisfiability Modulo Theories (SMT)solvers are fundamental to many programanalysis, verification, design and testing tools. They are a goodfit for the domain of software and hardware engineering becausethey support many domains that are commonly used by the tools.The meaning of domains are captured by theories that can beaxiomatized or supported by efficient <i>theory solvers</i>.Nevertheless, not all domains are handled by all solvers andmany domains and theories will never be native to any solver.We here explore different theories that extend MicrosoftResearch's SMT solver Z3's basicsupport. Some can be directly encoded or axiomatized,others make use of user theory plug-ins.Plug-ins are a powerful way for tools to supply their custom domains.

scholarly journals SMT-Based Contention-Free Task Mapping and Scheduling on 2D/3D SMART NoC with Mixed Dimension-Order Routing

2022 ◽  
Vol 19 (1) ◽  
pp. 1-21
Author(s):  
Daeyeal Lee ◽  
Bill Lin ◽  
Chung-Kuan Cheng
Keyword(s):  
System Performance ◽  
Search Space ◽  
Satisfiability Modulo Theories ◽  
Low Latency ◽  
Task Mapping ◽  
Single Cycle ◽  
Space Reduction ◽  
Reduction Techniques ◽  
2D And 3D ◽  
Mixed Dimension

SMART NoCs achieve ultra-low latency by enabling single-cycle multiple-hop transmission via bypass channels. However, contention along bypass channels can seriously degrade the performance of SMART NoCs by breaking the bypass paths. Therefore, contention-free task mapping and scheduling are essential for optimal system performance. In this article, we propose an SMT (Satisfiability Modulo Theories)-based framework to find optimal contention-free task mappings with minimum application schedule lengths on 2D/3D SMART NoCs with mixed dimension-order routing. On top of SMT’s fast reasoning capability for conditional constraints, we develop efficient search-space reduction techniques to achieve practical scalability. Experiments demonstrate that our SMT framework achieves 10× higher scalability than ILP (Integer Linear Programming) with 931.1× (ranges from 2.2× to 1532.1×) and 1237.1× (ranges from 4× to 4373.8×) faster average runtimes for finding optimum solutions on 2D and 3D SMART NoCs and our 2D and 3D extensions of the SMT framework with mixed dimension-order routing also maintain the improved scalability with the extended and diversified routing paths, resulting in reduced application schedule lengths throughout various application benchmarks.

scholarly journals Constraint Answer Set Programming: Integrational and Translational (or SMT-based) Approaches

2021 ◽  
pp. 1-31
Author(s):  
YULIYA LIERLER
Keyword(s):  
Automated Reasoning ◽  
Hybrid Approach ◽  
Answer Set Programming ◽  
Satisfiability Modulo Theories ◽  
Declarative Programming ◽  
Scheduling Problems ◽  
New Horizons ◽  
Research Areas ◽  
Constraint Processing ◽  
Answer Set

Abstract Constraint answer set programming or CASP, for short, is a hybrid approach in automated reasoning putting together the advances of distinct research areas such as answer set programming, constraint processing, and satisfiability modulo theories. CASP demonstrates promising results, including the development of a multitude of solvers: acsolver, clingcon, ezcsp, idp, inca, dingo, mingo, aspmt2smt, clingo[l,dl], and ezsmt. It opens new horizons for declarative programming applications such as solving complex train scheduling problems. Systems designed to find solutions to constraint answer set programs can be grouped according to their construction into, what we call, integrational or translational approaches. The focus of this paper is an overview of the key ingredients of the design of constraint answer set solvers drawing distinctions and parallels between integrational and translational approaches. The paper also provides a glimpse at the kind of programs its users develop by utilizing a CASP encoding of Traveling Salesman problem for illustration. In addition, we place the CASP technology on the map among its automated reasoning peers as well as discuss future possibilities for the development of CASP.

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