【摘要】 后摩尔时代的E级计算面临的编程墙缺乏衡量标准。代码复杂度作为软件代码的内在属性，是代码理解、优化和计价的基础。针对现有的代码复杂度衡量方法在HPC应用中存在局限性的问题，提出了基于操作符数目和代码行数的代码绝对复杂度和相对复杂度，其中绝对复杂度为代码操作符总数，相对复杂度为绝对复杂度与代码行数的比值。通过43个软件代码的实验验证，该方法能够合理评估不同类型代码的复杂度，特别是在科学计算领域。其中llvm和linux内核分别以3 300万和2 300万的绝对复杂度位列前两位；jellyfin-media-player, spheral和llvm则以4.54,3.9和3.12的相对复杂度排名前3位。该方法为不同代码的分析、比较和计价提供了新视角，也为E级计算编程墙的衡量提供了一种客观、可量化的标准。更多还原

【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.更多还原