Abstract: In the information stage, the importance of data storage lies in ensuring the reliability, consistency, security, and real-time accessibility of information. Erasure codes (EC) play a crucial role in data storage systems due to their ability to minimize storage overhead and handle multiple component failures. However, the process of encoding and decoding EC involves intensive computation, impacting storage system efficiency. This paper focuses on optimizing EC, with a special emphasis on the Galois field (GF) multiplication within multi-layer loops, a time-consuming aspect of EC. We first evaluate the pros and cons of three methods for GF multiplication calculation: the log table searching method, the complete multiplication table searching method, and the shift decomposition method. Subsequently, a 4 b splitting (SP) method is proposed to reduce memory access overhead during table searching in GF(2⁸). We delve into the SP’s analysis and leverage the 64 b modern processor architecture and vector instruction set characteristics to introduce data-level parallelism in multi-layer loops. This involves amplifying data access granularity and implementing single instruction multiple data (SIMD) vectorization. Based on the open-source Intel storage acceleration library (ISA-L), all optimization methods are implemented and tested on the Sunway processor and the x86 processor. The experimental results show the effectiveness of proposed optimization in improving EC performance across different data scalability scenarios. When compared to the original ISA-L, our optimizations exhibit an average performance speedup of 3.28x on the Sunway, 2.36x on the x86.