一句话：所给摘要提出面向 HPC 的 Python 工作流，以语言扩展和数据中心 IR 自动优化实现跨 CPU/GPU/FPGA 可移植高性能（与标题主题不一致）。
注意：所给英文摘要与标题“Explanation-guided fairness testing through genetic algorithm”主题不一致，以下按摘要内容概述。摘要针对 Python 在高性能计算中生产力高但性能不足的问题，提出一种兼顾 Python 生产力与跨架构可移植性能的工作流。其关键包括面向 HPC 的语言扩展，以及由以数据为中心的中间表示驱动的自动优化集合。评估在 CPU、GPU、FPGA 和 Piz Daint 超级计算机上进行，规模最高达 23,328 核；相较此前最优方案取得 2.47 倍和 3.75 倍加速，首次给出带注解 Python 的 Xilinx 与 Intel FPGA 结果，并在 512 节点上达到最高 93.16% 的扩展效率。
Python has become the de facto language for scientific computing. Programming in Python is highly productive, mainly due to its rich science-oriented software ecosystem built around the NumPy module. As a result, the demand for Python support in High Performance Computing (HPC) has skyrocketed. However, the Python language itself does not necessarily offer high performance. In this work, we present a workflow that retains Python's high productivity while achieving portable performance across different architectures. The workflow's key features are HPC-oriented language extensions and a set of automatic optimizations powered by a data-centric intermediate representation. We show performance results and scaling across CPU, GPU, FPGA, and the Piz Daint supercomputer (up to 23,328 cores), with 2.47x and 3.75x speedups over previous-best solutions, first-ever Xilinx and Intel FPGA results of annotated Python, and up to 93.16% scaling efficiency on 512 nodes.