All-Pairs Shortest-Paths for Large Graphs on the GPU

The all-pairs shortest-path problem is an intricate part in numerous practical applications. We describe a shared memory cache efficient GPU implementation to solve transitive closure and the all-pairs shortest-path problem on directed graphs for large datasets. The proposed algorithmic design utilizes the resources available on the NVIDIA G80 GPU architecture using the CUDA API. Our solution generalizes to handle graph sizes that are inherently larger then the DRAM memory available on the GPU. Experiments demonstrate that our method is able to significantly increase processing large graphs making our method applicable for bioinformatics, internet node traffic, social networking, and routing problems

On word-representability of polyomino triangulations

A graph $G=(V,E)$ is word-representable if there exists a word $w$ over the alphabet $V$ such that letters $x$ and $y$ alternate in $w$ if and only if $(x,y)$ is an edge in $E$. Some graphs are word-representable, others are not. It is known that a graph is word-representable if and only if it accepts a so-called semi-transitive orientation. The main result of this paper is showing that a triangulation of any convex polyomino is word-representable if and only if it is 3-colorable. We demonstrate that this statement is not true for an arbitrary polyomino. We also show that the graph obtained by replacing each $4$-cycle in a polyomino by the complete graph $K_4$ is word-representable. We employ semi-transitive orientations to obtain our results