How to Speed Up CUDA-WSat-PcL by 5x

Abstract

The Propositional Satisfiability Problem (SAT) is one of the most fundamental NP-complete problems, and is central to many domains of computer science. Utilizing a massively parallel architecture on a Graphics Processing Unit (GPU) together with a conventional CPU on NVIDIA's Compute Unified Device Architecture (CUDA) platform, this work proposes an efficient scheme to implement one parallel Stochastic Local Search (SLS) algorithms for SAT: CUDA-WSat-PcL. The implementation leads up to 5x speedup over the latest implementation of CUDA-WSat-PcL on CUDA. Additionally, our profiling results show that the CUDA portion of the new implementation is now at least 6x faster.