Minisymposium Presentation

As high performance computing (HPC) applications continue to push the boundaries of complexity and performance, leveraging AI-driven code generation has emerged as a powerful approach to streamline scientific software development processes.This talk explores the role of AI code generation models as a collaborative pair programmer in developing portable and scalable code with the SPH-EXA simulation framework.Specifically, we use AI-based code generation to assist development of SPH-EXA using CUDA, HIP, and SYCL to automate the code implementation and unit test generation and significantly reduce development effort while maintaining high standards of correctness and performance. Our work evaluates the dynamic interplay between human expertise and generative AI capabilities. We will assess productivity gains and discuss the practical challenges of ensuring performance portability and scalability across diverse hardware platforms. Through detailed performance analyses we will highlight both the benefits and the limitations of current AI-assisted approaches for scientific software development.This talk will provide insights into how AI-driven tools can serve as effective collaborative partners in HPC code development.