arXiv subjects

Linus Bantel

Publications and source records attributed to Linus Bantel.

3 recordsLinked to original sources

arXiv2026

An Open-Source Power Measurement Platform for System-Level Semiconductor Testing

Accurate power measurement is not only essential for evaluating the energy efficiency of modern embedded and semiconductor systems, but power draw is an important proxy during stress testing. Industrial semiconductor test equipment, however, is often expensive and difficult to integrate into flexible experimental workflows. This paper presents a compact and extensible hardware platform for automated system-level power measurement of embedded devices. The proposed setup integrates a Raspberry Pi controller, a precision current measurement device, and a microcontroller-based Device-under-Test (DUT). The system supports automated firmware deployment, synchronized device execution, and high-resolution current acquisition. A lightweight HTTP-based interface allows remote clients to upload firmware binaries and retrieve measurement results. This design enables automated benchmarking and reproducible experiments across multiple firmware variants. The platform is implemented using accessible hardware components and open-source software tools, making it suitable for research, prototyping, and educational environments. We describe the architecture of the platform, the automated testing workflow, and potential application scenarios, including firmware energy profiling and regression testing.

cs.AR↗

arXiv2026

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

Julia is increasingly used in 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. LLMs 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 MCP server, we study agentic generation of parallel Julia code, focusing on task-based execution with Dagger$.$jl. We evaluate three LLMS, OpenAI GPT-5.5, Anthropic Claude Opus 4.7, and the open-weight Qwen3-Coder-Next, on three problems with distinct parallel structures: Pi approximation, tiled general matrix multiplication, 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.

cs.DC↗

arXiv2026

From Prompts to Performance: Evaluating LLMs for Task-based Parallel Code Generation

Large Language Models (LLM) show strong abilities in code generation, but their skill in creating efficient parallel programs is less studied. This paper explores how LLMs generate task-based parallel code from three kinds of input prompts: natural language problem descriptions, sequential reference implementations, and parallel pseudo code. We focus on three programming frameworks: OpenMP Tasking, C++ standard parallelism, and the asynchronous many-task runtime HPX. Each framework offers different levels of abstraction and control for task execution. We evaluate LLM-generated solutions for correctness and scalability. Our results reveal both strengths and weaknesses of LLMs with regard to problem complexity and framework. Finally, we discuss what these findings mean for future LLM-assisted development in high-performance and scientific computing.

cs.PL↗