  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.
