The seed-and-expand scheme is appropriate for multiple view stereo, since it can build dense point clouds adaptively by avoiding unnecessary computation. However, due to the irregularity of the algorithm, it is not suitable for parallel computing on general public utilities (GPU). This paper is the first attempt to implement the irregular seed-and-expand method on GPU for multiple view stereo problems. Meanwhile, a hierarchical parallel computing architecture is also proposed to maximize the usage of both CPU and GPU. The adaptivity of the seed-and-expand scheme is pushed further by processing a pixel several rounds while, in order to maintain regularity for GPU implementation, every seed has exactly the same behavior in a single round of optimization. The high adaptivity also improves the robustness of the proposed method, thus aggressive matching score and a view selection method can be used to improve the reconstruction completeness extensively, without smearing out local details and lowering the accuracy. Compared with the state of the art, the proposed method achieves higher accuracy and completeness on standard datasets. The proposed method is also very fast. It is maximally five times faster than other methods running on a CPU and is on par with the regular depth map-based methods on GPU, which are naturally suitable for GPU acceleration.