Abstract: In the post-Moore era, there is a lack of measurement standards for the “programming wall” faced by exascale computing. As an inherent attribute of software code, code complexity serves as the foundation for code understanding, optimization, and pricing. To address the limitations of existing code complexity measurement methods in high-performance computing (HPC) applications, this paper proposes  absolute code complexity and relative code complexity, both  based on the number of operators and lines of code (LOC). Specifically, absolute complexity refers to the total number of operators in the code, while relative complexity is defined as the ratio of absolute complexity to lines of code. Experimental verification using 43 pieces of software code s shows that this method can reasonably evaluate the complexity of different types of code, especially in the field of scientific computing. Among the tested codes, llvm and the linux kernel rank first and second in terms of absolute complexity, with 33 million and 23 million operators respectively; jellyfin-media-player, spheral, and llvm top the list in relative complexity, with values of 4.54, 3.9, and 3.12 respectively. This method provides a new perspective for the analysis, comparison, and pricing of different codebases, and also offers an objective and quantifiable standard for measuring the “programming wall” in exascale computing.

Key words: high performance computing, code complexity, absolute complexity, relative complexity, operator counting