arXiv:2606.08710v1 Announce Type: cross Abstract: Modernization of legacy scientific codes is often necessary to keep up with the ever-evolving changes in the compute resource ecosystem. Parallelization and migration from poorly supported software ecosystems are two of the most time-consuming activities in the research software engineering field. This paper presents our experience in the successful, two-phase AI-assisted modernization of NMAP-RKPM, a roughly 60,000-line, 3D explicit solid mechanics physics engine based on the Reproducing Kernel Particle Method (RKPM). We converted this single-
The increasing sophistication of AI agents and a growing need to modernize legacy scientific codebases for high-performance computing makes this development timely.
This demonstrates a practical, successful application of agentic AI in a complex, high-value domain like scientific code modernization for HPC, indicating broader applicability.
The successful modernization of a 60,000-line scientific code using AI signals a viable path for automating highly skilled and time-consuming software engineering tasks for critical infrastructure.
- · AI agent developers
- · High-Performance Computing (HPC) research institutions
- · Scientific software engineers
- · Cloud providers
- · Traditional software modernization consultancies
- · Manual code refactoring efforts
- · Organizations with rigid legacy systems
Increased efficiency and reduced cost in modernizing legacy scientific and engineering software.
Acceleration of research and development in fields reliant on complex simulations due to updated tools.
The emergence of 'AI-native' scientific computing paradigms where agents continuously optimize code for new hardware architectures.
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Read at arXiv cs.AI