Effective Parallelization of a High-Order Graph Matching Algorithm for GPU Execution

High-order graph matching is a feature-matching algorithm that uses geometric information among features. This algorithm is more robust to repetitive patterns or unclear areas than first-order matching that uses only feature descriptors. However, the processing speed of high-order matching is very slow because of its high computational complexity. To accelerate its speed, this paper proposes a new parallelization algorithm of high-order matching for GPU execution. The obstacle for parallelization is the write collision caused by multiple threads that must simultaneously update the data at the same memory location. In high-order matching, multiple formulations of the objective function can generate the same solution. By taking advantage of this property, the proposed algorithm replaces the operation causing write collision with another operation eliminating the collision while generating the same solution. The proposed algorithm is tested with GTX 960 and takes 31.3 ms, which is 68 times faster than the execution time with a CPU and approximately three times faster than that with a straightforward parallelization for the same GPU.