HOG features are a simple and efficient feature descriptor commonly used for object detection. It is widely used in pedestrian detection and other fields. However, they face severe performance challenges when dealing with massive images. One of the solutions is to speed up the pedestrian detection algorithm in the context of mass images by using the Sunway SW26010 processor nodes of the SunwayTaihuLight supercomputer. We propose two methods of parallel implementation: one method is that a processor processes 4 images simultaneously, and the other is that 256 images are processed at the same time. Through a large number of serial and parallel processing experimental tests, the results show that  the first parallel implementation method can be used to process highresolution images and the speedup can reach up to 83; the second parallel implementation method can be used to process lowresolution images and the maximum speedup is 95. The results on multinode processors show that our parallel implementation methods have good scalability.