high performance computing on graphics processing units: hgpu.org

In recent years, a multitude of AI solutions has emerged to facilitate code generation, commonly known as Language Model-based Programming (LLM). These tools empower programmers to automate their work. Automatic programming also falls within the domain of optimizing compilers, primarily based on the polyhedral model, which processes loop nests concentrating most computations. This article focuses on harnessing LLM tools to generate OpenCL code for non-serial polyadic dynamic programming kernels. We have chosen the Nussinov RNA folding computational task, previously employed to test polyhedral compilers in optimizing kernels with non-uniform dependences. The code generated in OpenMP by polyhedral optimizers is limited to CPU computations. We automatically convert it into the OpenCL standard using ChatGPT-3.5 through its source-to-source queries to extend the number of possible platforms. The validity and efficiency of the generated code were verified on various CPUs and GPUs from different manufacturers.