Concerns have arisen regarding the unregulated utilization of artificial intelligence (AI) outputs, potentially leading to various societal issues. While humans routinely validate information, manually inspecting the vast volumes of AI-generated results is impractical. Therefore, automation and visualization are imperative. In this context, Explainable AI (XAI) technology is gaining prominence, aiming to streamline AI model development and alleviate the burden of explaining AI outputs to users. Simultaneously, software auto-tuning (AT) technology has emerged, aiming to reduce the man-hours required for performance tuning in numerical calculations. AT is a potent tool for cost reduction during parameter optimization and high-performance programming for numerical computing. The synergy between AT mechanisms and AI technology is noteworthy, with AI finding extensive applications in AT. However, applying AI to AT mechanisms introduces challenges in AI model explainability. This research focuses on XAI for AI models when integrated into two different processes for practical numerical computations: performance parameter tuning of accuracy-guaranteed numerical calculations and sparse iterative algorithm.

翻译：近年来，人工智能（AI）输出的无监管利用引发担忧，这可能衍生出各种社会问题。尽管人类具备常规的信息验证能力，但人工审查海量AI生成结果并不现实。因此，自动化与可视化势在必行。在此背景下，可解释人工智能（XAI）技术日益受到重视，旨在简化AI模型开发流程，减轻向用户解释AI输出结果的负担。与此同时，软件自动调优（AT）技术应运而生，旨在缩短数值计算性能调优所需的人工工时。AT是降低参数优化及数值计算高性能编程成本的有力工具。AT机制与AI技术的协同效应尤为显著，AI在AT领域已获得广泛应用。然而，将AI应用于AT机制会引发AI模型可解释性方面的挑战。本研究聚焦于将AI模型集成至面向实际数值计算的两类不同流程时的XAI问题：一类是精度保证数值计算的性能参数调优，另一类是稀疏迭代算法。