3 research outputs found
GPU Parallelism for SAT Solving Heuristics
Modern SAT solvers employ a number of smart techniques and strategies to achieve maximum efficiency in solving the Boolean Satisfiability problem. Among all components of a solver, the branching heuristics plays a crucial role in affecting the performance of the entire solver. Traditionally, the main branching heuristics that have appeared in the literature have been classified as look-back heuristics or look-ahead heuristics. As SAT technology has evolved, the former have become more and more preferable, for their demand for less computational effort. Graphics Processor Units (GPUs) are massively parallel devices that have spread enormously over the past few decades and offer great computing power at a relatively low cost. We describe how to exploit such computational power to efficiently implement look-ahead heuristics. Our aim is to “rehabilitate” these heuristics, by showing their effectiveness in the contest of a parallel SAT solver
Constraint Propagation on GPU: A Case Study for the Bin Packing Constraint
The Bin Packing Problem is one of the most important problems in discrete
optimization, as it captures the requirements of many real-world problems.
Because of its importance, it has been approached with the main theoretical and
practical tools. Resolution approaches based on Linear Programming are the most
effective, while Constraint Programming proves valuable when the Bin Packing
Problem is a component of a larger problem. This work focuses on the Bin
Packing constraint and explores how GPUs can be used to enhance its propagation
algorithm. Two approaches are motivated and discussed, one based on knapsack
reasoning and one using alternative lower bounds. The implementations are
evaluated in comparison with state-of-the-art approaches on different
benchmarks from the literature. The results indicate that the GPU-accelerated
lower bounds offers a desirable alternative to tackle large instances
GPU-Based Parallelism for ASP-Solving
Answer Set Programming (ASP) has become the paradigm of choice in the field of logic programming and non-monotonic reasoning. With the design of new and efficient solvers, ASP has been successfully adopted in a wide range of application domains. Recently, with the advent of GPU Computing, which allowed the use of modern parallel Graphical Processing Units (GPUs) for general-purpose computing, new opportunities for accelerating ASP computation has arisen. In this paper, we describe a new approach for solving ASP that exploits the parallelism provided by GPUs. The design of a GPU-based solver poses various challenges due to the peculiarities of GPU in terms of both programmability and architecture capabilities with respect to the intrinsic nature of the satisfiability problems, which exposes poor parallelism