Recommendation system is important for internet applications such as Netflix or Tiktok. Collaborative Filtering algorithm is a graph computing algorithm commonly used in recommendation systems. In this paper, we propose an innovative approach called PCFBCD, which stands for Parallel Collaborative Filtering using Block Coordinate Descent to accelerate Collaborative Filtering. First we introduce two new algorithms, Multiple Computation and Permutation (MCP) and Normal Parallel Processing (NPP) that both use BCD method to optimize CF algorithm for higher level parallelism. Based on it we propose a hardware architecture that fully utilize the parallelism. Then we simulate PCFBCD architecture using a general-purpose architecture simulator. Experimental results show that our new approaches achieve 3.10x to 3.58x speedup compared to traditional method.