HPCG benchmark is a new standard for supercomputer ranking. This benchmark is used mainly for evaluating how fast a supercomputer is able to solve a large scale sparse linear system, which is closer to real applications, and has attracted extensive attention recently. Research of parallel HPCG on domestic heterogeneous many-core supercomputers is very important, not only to improve the HPCG ranking of Chinese supercomputers, but also to provide reference of parallel algorithm and optimization techniques for many applications. This work studies parallelization and optimization of HPCG on a domestically produced complex heterogeneous supercomputer, leveraging blocked graph coloring algorithm for parallelism exploration for the first time on this system, and proposes a graph coloring algorithm for structured grids. The parallelism produced by this algorithm is higher than the traditional JPL and CC algorithm, with better coloring quality. With this algorithm, successfully reduced the iteration number of HPCG by 3 times, and the total performance is improved by 6%. This study also analyzes the data transfer cost of each component in the complex heterogeneous system, providing a task partitioning method, which is more suitable for HPCG, and the neighbor communication cost in SpMV and SymGS is hidden by inner-outer region partitioning. In the whole-system test, an HPCG performance of 1.67% of the peek GFLOPS of the system is achieved, compared to single-node performance, the weak-scaling efficiency on the whole system has reached 92%.