Comparing Llama-2 and GPT-3 LLMs for HPC kernels generation

• URL: http://arxiv.org/abs/2309.07103v1
• Date: Tue, 12 Sep 2023 01:19:54 GMT
• Title: Comparing Llama-2 and GPT-3 LLMs for HPC kernels generation
• Authors: Pedro Valero-Lara, Alexis Huante, Mustafa Al Lail, William F. Godoy, Keita Teranishi, Prasanna Balaprakash, Jeffrey S. Vetter
• Abstract summary: We evaluate the use of the open-source Llama-2 model for generating high-performance computing kernels. Our goal is to compare the accuracy of Llama-2 and our original GPT-3 baseline by using a similar metric.
• Score: 3.070523453466106
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: We evaluate the use of the open-source Llama-2 model for generating well-known, high-performance computing kernels (e.g., AXPY, GEMV, GEMM) on different parallel programming models and languages (e.g., C++: OpenMP, OpenMP Offload, OpenACC, CUDA, HIP; Fortran: OpenMP, OpenMP Offload, OpenACC; Python: numpy, Numba, pyCUDA, cuPy; and Julia: Threads, CUDA.jl, AMDGPU.jl). We built upon our previous work that is based on the OpenAI Codex, which is a descendant of GPT-3, to generate similar kernels with simple prompts via GitHub Copilot. Our goal is to compare the accuracy of Llama-2 and our original GPT-3 baseline by using a similar metric. Llama-2 has a simplified model that shows competitive or even superior accuracy. We also report on the differences between these foundational large language models as generative AI continues to redefine human-computer interactions. Overall, Copilot generates codes that are more reliable but less optimized, whereas codes generated by Llama-2 are less reliable but more optimized when correct.

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