Generated, Parallel, Scalable? A Study of Agentic AI-Generated Julia Code on Supercomputers

Julia is increasingly used in High-Performance Computing
(HPC ) as a single-language alternative to combining high-level scripting
with low-level systems languages, but achieving scalable performance
still requires expertise in parallel programming. Large Language Models
( LLM s) are increasingly used for code generation and are advancing
rapidly with each new version. Yet, existing studies focus on single-shot
prompting rather than agentic settings, in which an LLM autonomously
plans, generates, and refines code through tool use.
Using an OpenCode-based agent extended with a Julia-documentation
Model Context Protocol (MCP ) server, we study agentic generation of
parallel Julia code, focusing on task-based execution with Dagger.jl.
We evaluate three LLM s, OpenAI GPT-5.5, Anthropic Claude Opus
4.7, and the open-weight Qwen3-Coder-Next, on three problems with
distinct parallel structures: π approximation, tiled general matrix multi-
plication, and tiled Cholesky decomposition. The generated Dagger.jl
implementations are compared against agent-generated Base.Threads
and MPI.jl baselines, with shared-memory experiments scaling to 192
cores and distributed-memory experiments on two nodes.
The agents reliably produce executable code for small inputs but fail
at larger scales due to deadlocks, oversubscription, or out-of-memory
errors, with the open-weight model affected most severely. The two
commercial models scale comparably on Base.Threads and MPI.jl, while
their Dagger.jl implementations expose recurring weaknesses in task
dependencies, granularity, and scheduling. Agentic AI is promising for
producing parallel Julia code, but generating robust, performance-aware
implementations for large-scale HPC systems remains an open challenge.