Improving the efficiency of heterogeneous HPL needs to fully utilize the computing power of acceleration components and CPU, the acceleration components integrate more computing cores and are responsible for the main calculation. The general CPU is responsible for task scheduling and also participates in calculation. Under the premise of reasonable division of tasks and load balancing, optimizing CPU-side computing performance is particularly important to improve overall efficiency. Optimizing the basic linear algebra subprogram (BLAS) functions for specific platform architecture characteristics can often make full use of general-purpose CPU computing capabilities to improve the overall system efficiency. The BLAS-like Library Instantiation Software (BLIS) algorithm library is an open source BLAS function framework, which has the advantages of easy development, portability, and modularity. Based on the heterogeneous system platform architecture and HPL algorithm characteristics, this study uses three-level cache, vectorized instructions, and multi-threaded parallel technology to optimize the BLAS functions called by the CPU, applies auto-tuning technology to optimize the matrix block parameters, and eventually forms the HygonBLIS algorithm library. Compared with MKL, the overall performance of the HPL using HygonBLIS has been improved by 11.8% in the heterogeneous environment.