发表机构
Parallel Research Lab（平行研究实验室）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本文批判性综述了HPC生产力测量方法，从SLOC到AI辅助编程，指出其缺乏统一定义，并分析了新系统与自动化迁移对评估的挑战。
AI 中文摘要
生产力长期以来一直被认为是高性能计算（HPC）中的核心关注点，但它仍然缺乏一个可行的正式定义和一个被广泛接受的量化评估指标。最近将生产力纳入性能可移植性分析的尝试引入了代码分歧、收敛和语义度量，但尚未在生产力与性能可移植性之间建立令人信服的正式关系。更新的语言和编译器系统，包括JACC、Mojo、JLIR和Accl++，通过更高级别的抽象、多级中间表示、自动内核生成和运行时编译来追求高效的生产力可移植性。这些系统展示了实质性的工程进展，而生产力仍然通常通过可编程性、生态系统复用、代码规模或减少实现负担来代表，而不是直接衡量人类工作量。本文对HPC生产力测量进行了批判性综述，追溯了其从源代码行数（SLOC）、静态复杂度和经验可用性研究到开发时间模型和语义方法的发展。我们还研究了自动化迁移和人工智能（AI）辅助编程如何削弱代码转换与人类工作量之间的对应关系，将工作量转向验证、纠正和性能调优。所综述的方法在测量内容、直接性、客观性、可重复性、实用性以及它们与性能和可移植性的关系方面进行了比较。最后，我们指出了量化HPC生产力评估的主要局限性和开放挑战。
英文摘要
Productivity has long been recognized as a central concern in high-performance computing (HPC), yet it still lacks a workable formal definition and a broadly accepted metric for quantified assessment. Recent attempts to incorporate productivity into performance-portability analysis have introduced code-divergence, convergence, and semantic measures, but have not established a convincing formal relationship between productivity and performance portability. More recent language and compiler systems, including JACC, Mojo, JLIR, and Accl++, pursue productive performance portability through higher-level abstractions, multi-level intermediate representations, automatic kernel generation, and runtime compilation. These systems demonstrate substantial engineering progress, while productivity is still commonly represented through programmability, ecosystem reuse, code size, or reduced implementation burden rather than direct measurement of human effort. This paper presents a critical review of HPC productivity measurement, tracing its development from source lines of code (SLOC), static complexity, and empirical usability studies to development-time models and semantic approaches. We also examine how automated migration and artificial-intelligence(AI)-assisted programming weaken the correspondence between code transformation and human effort, shifting effort toward verification, correction, and performance tuning. The reviewed methods are compared in terms of what they measure, directness, objectivity, reproducibility, practicality, and their relationship to performance and portability. We conclude by identifying the principal limitations and open challenges for quantified HPC productivity assessment.
Comments39 pages, 2 figuers, 2 tables
