The hundreds of stream cores in the latest graphics processors (GPUs), and the possibility to execute non-graphics computations on them, open unprecedented levels of parallelism at a very low cost. In the last 6 years, GPUs had an increasing performance advantage of an order of magnitude relative to x86 CPUs. Furthermore, this performance advantage will continue to increase in the next 20 years because of the scalability of the chip manufacturing processes. The goal of this project is to efficiently exploit the GPU parallelism in order to accelerate the execution of a Boolean Satisfiability (SAT) solver. SAT has a wide range of applications, including formal verification and testing of software and hardware, scheduling and planning, cryptanalysis, and detection of security vulnerabilities and malicious intent in software. We bring a tremendous expertise in SAT solving, formal verification, and solving of Constraint Satisfaction Problems (CSPs) by efficient translation to SAT. In our previous work (done on the expenses of our company) we achieved 2 orders of magnitude speedup in solving Boolean formulas from formal verification of complex pipelined microprocessors, 4 orders of magnitude speedup in SAT-based solving of CSPs, and 8 orders of magnitude speedup in SAT-based routing of optical networks. During Phase 1 we implemented a prototype of a parallel GPU-based SAT solver that is 1 2 orders of magnitude faster than the best sequential SAT solvers. In Phase 2, we will continue to exploit the GPU parallelism to accelerate SAT solving, and expect to achieve speedup of 3 4 orders of magnitude.