LLM-HPC++: Evaluating LLM-Generated Modern C++ and MPI+OpenMP Codes for Scalable Mandelbrot Set Computation

TL;DR

This paper evaluates the ability of leading LLMs to generate correct and scalable C++ code for parallel Mandelbrot set computation using modern HPC paradigms, highlighting their strengths and limitations.

Contribution

It systematically assesses LLMs like ChatGPT and LLaMA in generating HPC code, providing insights into their effectiveness in parallel programming tasks.

Findings

ChatGPT-4 and ChatGPT-5 produce syntactically correct code

Generated code demonstrates scalable performance

LLMs show promise but have limitations in complex HPC code generation

Abstract

Parallel programming remains one of the most challenging aspects of High-Performance Computing (HPC), requiring deep knowledge of synchronization, communication, and memory models. While modern C++ standards and frameworks like OpenMP and MPI have simplified parallelism, mastering these paradigms is still complex. Recently, Large Language Models (LLMs) have shown promise in automating code generation, but their effectiveness in producing correct and efficient HPC code is not well understood. In this work, we systematically evaluate leading LLMs including ChatGPT 4 and 5, Claude, and LLaMA on the task of generating C++ implementations of the Mandelbrot set using shared-memory, directive-based, and distributed-memory paradigms. Each generated program is compiled and executed with GCC 11.5.0 to assess its correctness, robustness, and scalability. Results show that ChatGPT-4 and ChatGPT-5 achieve strong syntactic precision and scalable performance.