Approximating matchings in parallel
Information Processing Letters
GPU Gems 2: Programming Techniques for High-Performance Graphics and General-Purpose Computation (Gpu Gems)
Approximating weighted matchings in parallel
Information Processing Letters
Distributed approximate matching
Proceedings of the twenty-sixth annual ACM symposium on Principles of distributed computing
Improved distributed approximate matching
Proceedings of the twentieth annual symposium on Parallelism in algorithms and architectures
Bipartite Graph Matching Computation on GPU
EMMCVPR '09 Proceedings of the 7th International Conference on Energy Minimization Methods in Computer Vision and Pattern Recognition
Bipartite Graph Matching Computation on GPU
EMMCVPR '09 Proceedings of the 7th International Conference on Energy Minimization Methods in Computer Vision and Pattern Recognition
Bipartite graph matching on GPU over complete or local grid neighborhoods
IWANN'11 Proceedings of the 11th international conference on Artificial neural networks conference on Advances in computational intelligence - Volume Part I
A GPU algorithm for greedy graph matching
Facing the Multicore-Challenge II
GPU accelerated maximum cardinality matching algorithms for bipartite graphs
Euro-Par'13 Proceedings of the 19th international conference on Parallel Processing
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The Bipartite Graph Matching Problem is a well studied topic in Graph Theory. Such matching relates pairs of nodes from two distinct sets by selecting a subset of the graph edges connecting them. Each edge selected has no common node as its end points to any other edge within the subset. When the considered graph has huge sets of nodes and edges the sequential approaches are impractical, specially for applications demanding fast results. In this paper we investigate how to compute such matching on Graphics Processing Units (GPUs) motivated by its increasing processing power made available with decreasing costs. We present a new data-parallel approach for computing bipartite graph matching that is efficiently computed on today's graphics hardware and apply it to solve the correspondence between 3D samples taken over a time interval.