Despite advancements in parallelization tools, optimizing scientific applications remains a complex and time-consuming task due to the iterative nature of the optimization process and the expertise required to validate optimization results. Existing tools often fail to efficiently shorten the optimization cycle or validate the outcomes of applied optimizations, such as ensuring result correctness and performance improvements. This paper introduces iCetus, an interactive parallelizer designed to address these challenges. iCetus provides a unified environment that supports a wide range of optimization strategies, including manual, automatic, and AI-driven approaches. It enables users to target specific code sections rather than the entire codebase, aiming to accelerate the optimization process. The integrated framework automates validation, ensuring that optimizations enhance performance while maintaining correctness. Evaluation of iCetus against traditional methods demonstrates a significant reduction in development time and improvements in the reliability of optimization results, indicating its potential as an effective solution for enhancing the efficiency of scientific applications.
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