Imagine effortlessly harnessing the full power of your multi-core processor without getting bogged down in complex code optimization. That’s the promise of OMPar, an innovative tool that uses AI to automatically parallelize C/C++ code. In the world of high-performance computing, making the most of multi-core processors is crucial. However, manually rewriting code for parallel processing is a time-consuming and error-prone process. Traditional parallelization tools, while helpful, often miss opportunities or introduce errors. This is where OMPar comes in, transforming the process by using the power of large language models (LLMs). OMPar works by intelligently inserting OpenMP pragmas, special directives that tell the compiler how to run code in parallel. It uses two key components: OMPify, which identifies sections of code that can be parallelized, and MonoCoder-OMP, which generates the precise OpenMP directives needed. This modular approach allows for greater flexibility and accuracy than general-purpose LLMs. Tested against standard benchmarks like HeCBench and ParEval, OMPar shines. It significantly outperforms traditional methods, identifying more parallelization opportunities and generating more efficient code. The results are impressive: OMPar-optimized code runs faster, scales better across multiple cores, and maintains code accuracy. What sets OMPar apart is its ability to work with partial or incomplete codebases and continuously learn from new code patterns. This means it gets smarter over time, leading to even better parallelization. OMPar isn't just a performance booster; it represents a paradigm shift in how we approach code optimization. By automating a complex and crucial task, it frees developers to focus on higher-level design and innovation, ultimately paving the way for more efficient and scalable software for everyone.