  This paper addresses the problem of providing a novel approach to sourcing
significant training data for LLMs focused on science and engineering. In
particular, a crucial challenge is sourcing parallel scientific codes in the
ranges of millions to billions of codes. To tackle this problem, we propose an
automated pipeline framework, called LASSI, designed to translate between
parallel programming languages by bootstrapping existing closed- or open-source
LLMs. LASSI incorporates autonomous enhancement through self-correcting loops
where errors encountered during compilation and execution of generated code are
fed back to the LLM through guided prompting for debugging and refactoring. We
highlight the bi-directional translation of existing GPU benchmarks between
OpenMP target offload and CUDA to validate LASSI.
  The results of evaluating LASSI with different application codes across four
LLMs demonstrate the effectiveness of LASSI for generating executable parallel
codes, with 80% of OpenMP to CUDA translations and 85% of CUDA to OpenMP
translations producing the expected output. We also observe approximately 78%
of OpenMP to CUDA translations and 62% of CUDA to OpenMP translations execute
within 10% of or at a faster runtime than the original benchmark code in the
same language.
