用AI自动优化科研代码，让调度模拟提速95%以上
Accelerated Genetic Programming Hyper-Heuristics for Simulation-Based Scheduling via Agentic AI
- 用AI代理分析代码瓶颈并自动重构优化
- 模拟耗时从1298秒降至200秒内，输出不变
- 适合科研人员快速提升计算效率
Python在科研中广泛应用，但复杂仿真代码随规模扩大变得极慢。尤其在离散事件项目调度模拟中，顺序状态更新、嵌套循环和条件判断限制了编译库与GPU加速的效益。传统优化需反复性能分析、重构与验证，耗时且需专业技能。本文提出基于Claude智能体的系统性重构方法，在高性能计算环境下处理真实项目调度任务。智能体在基准测试和正确性验证指导下，定位瓶颈、实施优化并评估效果，研究人员保持最终决策权。测试显示运行时间由1298秒降至200秒以下，年节省四百万核心小时（约32万新西兰元）。
原文摘要 · Abstract (English)
Python is widely used in scientific research because it enables rapid development and provides rich ecosystems for data analysis, artificial intelligence (AI), and machine learning. However, customized research code can become prohibitively slow as experiments scale. This challenge is particularly acute in discrete-event project-scheduling simulations, where sequential state updates, nested loops, conditional evaluations, and object-oriented structures limit the benefits of compiled numerical and GPU-accelerated libraries. Addressing these bottlenecks typically requires iterative profiling, refactoring, testing, and validation, yet researchers may lack the time or specialized software-engineering expertise for low-level optimization. This paper presents a systematic refactoring approach using Claude agentic AI on real-world project-scheduling workloads in a high-performance computing (HPC) environment. Guided by representative benchmarks and correctness checks, the agent identifies bottlenecks, implements targeted optimizations, and evaluates their effects, while the researcher retains final control. Testing runtime reduced from 1,298 seconds to under 200 seconds without changing outputs, saving four million core-hours (NZ\$320,000) annually.
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