A machine program for theorem-proving
Communications of the ACM
Chaff: engineering an efficient SAT solver
Proceedings of the 38th annual Design Automation Conference
Statistical mechanics methods and phase transitions in optimizationproblems
Theoretical Computer Science - Phase transitions in combinatorial problems
BerkMin: A Fast and Robust Sat-Solver
Proceedings of the conference on Design, automation and test in Europe
Linear algebra operators for GPU implementation of numerical algorithms
ACM SIGGRAPH 2003 Papers
GPU Cluster for High Performance Computing
Proceedings of the 2004 ACM/IEEE conference on Supercomputing
Understanding the efficiency of GPU algorithms for matrix-matrix multiplication
Proceedings of the ACM SIGGRAPH/EUROGRAPHICS conference on Graphics hardware
Survey propagation: An algorithm for satisfiability
Random Structures & Algorithms
Accelerating scientific computation in bioinformatics by using graphics processing units as parallel vector processors
VARSAT: Integrating Novel Probabilistic Inference Techniques with DPLL Search
SAT '09 Proceedings of the 12th International Conference on Theory and Applications of Satisfiability Testing
Introduction to GPU programming for EDA
Proceedings of the 2009 International Conference on Computer-Aided Design
Boolean satisfiability on a graphics processor
Proceedings of the 20th symposium on Great lakes symposium on VLSI
Simple optimizations for an applicative array language for graphics processors
Proceedings of the sixth workshop on Declarative aspects of multicore programming
Towards accelerating irregular EDA applications with GPUs
Integration, the VLSI Journal
Using cross-entropy for satisfiability
Proceedings of the 28th Annual ACM Symposium on Applied Computing
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We show how to exploit the raw power of current graphics processing units (GPUs) to obtain implementations of SAT solving algorithms that surpass the performance of CPU-based algorithms. We have developed a GPU-based version of the survey propagation algorithm, an incomplete method capable of solving hard instances of random k-CNF problems close to the critical threshold with millions of propositional variables. Our experimental results show that our GPU-based algorithm attains about a nine-fold improvement over the fastest known CPU-based algorithms running on high-end processors.