Publications

@inproceedings{EduHPCRothPosner2026,
  author = {Roth, Anna-Lena and Posner, Jonas},
  title = {From a Last-Week Add-On to First-Day Practice: Three Years of Integrating MPI Performance Analysis into HPC Education with EduMPI Suite},
  year = {2026},
  note = {Accepted for publication: SC '26 Workshops, EduHPC; to appear}
}

@inproceedings{EESPSchäferRoth2026,
  author = {Schäfer, Paul and Roth, Anna-Lena and Arzt, Peter and Krull, Felix and Krenz, Sebastian and Wolf, Felix and Posner, Jonas},
  title = {Saving Energy Costs and Time on Supercomputers: Scheduler-Integrated Recurring Node Shutdown Exploiting Malleable Jobs},
  year = {2026},
  note = {Accepted for publication: SC '26 Workshops, EESP; to appear}
}

@inproceedings{AMTEAgenticJulia2026,
  author = {Bantel, Linus and Roth, Anna-Lena and Posner, Jonas and Pfl\"uger, Dirk},
  title = {{Generated, Parallel, Scalable? A Study of Agentic AI-Generated Julia Code on Supercomputers}},
  year = {2026},
  publisher = {Springer},
  note = {Accepted for publication; preprint available},
  booktitle = {Proceedings Parallel Processing and Applied Mathematics (PPAM) Workshops (AMTE)}
}

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: 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.

@inproceedings{RothPosner2026,
  author = {\underline{Anna-Lena Roth} and Posner, Jonas},
  title = {An Empirical Analysis of High-Performance Computing Education in Germany},
  year = {2026},
  publisher = {Springer},
  pages = {449--463},
  booktitle = {Computational Science -- ICCS 2026 Workshops},
  doi = {10.1007/978-3-032-29918-5_32}
}

The increasing relevance of High-Performance Computing (HPC) necessitates the systematic integration of parallel programming and performance-oriented competencies into computational science curricula. Effective HPC education requires not only theoretical foundations but also hands-on experience with real cluster infrastructures to develop a practical understanding of scalability, efficiency, and architectural distinctions between shared and distributed memory systems. Although curricular initiatives advocate such integration, comprehensive cross-institutional evidence on how HPC education is implemented--and how curricular structures relate to locally available infrastructure--remains limited. To examine this relationship, we conducted a systematic empirical assessment of HPC education across 102 academic institutions in Germany. Based on an analysis of module handbooks and course catalogs, 178 HPC-related courses were identified and evaluated with respect to competency coverage and curricular placement. While 67.6% of institutions offer at least one HPC-related course, these offerings are predominantly elective modules situated at the master's level, with comparatively limited integration in bachelor's programs. To contextualize these findings, local academic HPC cluster infrastructures were additionally assessed regarding availability, size, and documented accessibility for teaching purposes. Although 61.8% of the institutions operate HPC clusters, only 23.0% explicitly document their availability for educational use, as infrastructures are primarily reserved for research. Statistical analysis reveals a significant association between restricted teaching access and reduced curricular emphasis on practical competencies, including resource management, cluster usage, debugging in parallel environments, and performance analysis. Overall, the findings indicate a structural imbalance between theoretical instruction and the development of practical HPC competencies within German higher education.

@inproceedings{RothPosnerKuhn2026,
  author = {\underline{Anna-Lena Roth} and Posner, Jonas and Kuhn, Michael},
  title = {Enabling Interactive Visualization for Near-Real-Time Performance Analysis of MPI Applications},
  year = {2026},
  publisher = {ACM},
  pages = {120--129},
  booktitle = {Companion of the 17th ACM/SPEC International Conference on Performance Engineering},
  doi = {10.1145/3777911.3800642}
}

Performance analysis is essential for understanding the behavior of parallel applications on High-Performance Computing (HPC) systems, identifying bottlenecks and load imbalances. Traditional analysis workflows are post-mortem: applications are instrumented, performance data is collected at runtime, and insights become available only after execution. This workflow is time-consuming - especially for large-scale Message Passing Interface (MPI) applications (i.e., running on many processes/nodes) requiring repeated analysis after code changes - and demands substantial expertise, posing a steep learning barrier for beginners. To address these challenges, in previous work, we proposed EduMPI Suite, which provides near-real-time visualization of MPI communication in an interactive GUI, making performance analysis immediately accessible in educational settings. While our previous work focused on usability and classroom integration, this paper presents the technical architecture and performance evaluation of EduMPI Suite's measurement and data-management system. We couple EduMPI, an extended Open MPI fork with integrated performance measurement, with EduStore, a time-series database that processes performance-relevant events in near-real-time for visualization in EduMPI GUI. EduMPI leverages a binary-based ingestion layer for high-throughput data insertion, avoiding costly data conversions. Combined with TimescaleDB's optimized time-series processing, this design ensures continuous data availability with negligible delay. On-the-fly aggregation and indexing enable responsive queries and smooth, interactive visualization of communication traces. Our evaluation demonstrates that EduMPI Suite introduces less than 3.8% runtime overhead while maintaining query latencies below 0.09 s, enabling interactive performance analysis of MPI applications. Usability studies confirm that EduMPI Suite significantly reduces entry barriers for students and improves their ability to identify performance issues over conventional tools.

@article{RothJamesPosner2026,
  author = {\underline{Anna-Lena Roth} and James, David and Posner, Jonas and Kuhn, Michael},
  title = {Performance Analysis in Parallel Programming Education: A Comparative Usability Study},
  year = {2026},
  journal = {PARS-Mitteilungen},
  volume = {38},
  publisher = {Gesellschaft für Informatik e.V., Fachgruppe PARS},
  url = {https://dl.gi.de/handle/20.500.12116/48841}
}

Parallel programming curricula encompass not only the development of parallel code and algorithm design but also emphasize efficiency, optimization, and performance analysis. To equip students with the skills necessary for writing efficient parallel code using message passing with MPI, practical experience on HPC environments is essential. Performance analysis tools assist in identifying issues such as load imbalances or bottlenecks. Despite their use by experienced developers, these tools' complexity and required knowledge of cluster architectures, resource management, MPI, and common parallel issues hinder their educational integration. To address these barriers, we developed EduMPI, a learning support tool designed to simplify cluster usage and performance analysis for students. EduMPI offers an intuitive GUI that automates program execution on clusters and delivers near-real-time visualizations of MPI communication. This enables students to track process communication according to their physical placement within the cluster and detect performance problems interactively. This paper presents a user study comparing EduMPI with established professional performance analysis tools, demonstrating that EduMPI lowers entry barriers and fosters an intuitive understanding of parallel program performance, thereby enhancing its educational value.

@inproceedings{RothJamesKuhnFrisch2025,
  author = {\underline{Anna-Lena Roth} and
                  David James and
                  Michael Kuhn and
                  Dustin Frisch},
  title = {Making {MPI} Collective Operations Visible: Understanding Their Utility
                  and Algorithmic Insights},
  year = {2025},
  volume = {15900},
  publisher = {Springer},
  pages = {71--84},
  booktitle = {Euro-Par 2025: Parallel Processing - 31st European Conference on Parallel
                  and Distributed Processing, Dresden, Germany, August 25-29, 2025,
                  Proceedings, Part {I}},
  doi = {10.1007/978-3-031-99854-6_5}
}

In-depth understanding of collective MPI communication is a major challenge for both beginners and experienced developers. It is challenging to grasp the operations of collective algorithms, as many performance analysis tools cannot break down collective operations into their underlying point-to-point communication. However, collective communication is a key factor in optimizing the performance of parallel programs. EduMPI is a novel tool for parallel programming education, providing near-real-time visualization of collective MPI algorithms. It displays per-process data exchange and highlights performance issues like Late Sender and Late Receiver. This paper introduces the visualization of collective communication in EduMPI, demonstrates how this representation aids in understanding the concept of collective communication and its relevance for performance optimization, and evaluates its effectiveness in an educational context. EduMPI bridges the gap in understanding complex collective MPI operations by providing a transparent view of the underlying processes, enabling students to visualize and better understand the communication flow.

@article{RothJames2025,
  author = {\underline{Anna-Lena Roth} and James, David and Kuhn, Michael},
  title = {EduMPI - Simplifying the Use of High-Performance Clusters and Focusing Performance Analysis in Parallel Programming Education},
  year = {2025},
  journal = {PARS-Mitteilungen},
  volume = {37},
  publisher = {Gesellschaft für Informatik e.V., Fachgruppe PARS},
  issn = {0177-0454},
  url = {https://dl.gi.de/handle/20.500.12116/46131}
}

The use of high-performance clusters in parallel programming education can be challenging. Learners need prior knowledge of SSH, Linux commands, and workload management to execute their MPI programs on a cluster. Additionally, performance analysis, which is crucial for improving program behavior and identifying errors, requires an understanding of instrumentation, measurement, and typical performance issues in parallel programs. This requirement impedes beginners from benefiting from performance visualization. To make these instruments available to beginners in parallel programming, we are developing EduMPI. This novel learning support tool allows learners to execute MPI programs in a real environment on high-performance clusters from an early stage of parallel programming education. EduMPI helps learners to gain visual insights into their program’s performance through analysis at runtime.

@inproceedings{RothJames2024,
  author = {\underline{Anna-Lena Roth} and James, David and Kuhn, Michael and Konert, Johannes},
  title = {Enhancing Parallel Programming Education with High-Performance Clusters Utilizing Performance Analysis},
  year = {2024},
  volume = {{P-356}},
  booktitle = {Fachtagung Bildungstechnologien der Gesellschaft
                f{\"{u}}r Informatik e.V., {DELFI}, Fulda, Germany},
  doi = {10.18420/delfi2024_42}
}

The use of high-performance clusters in parallel programming education can be challenging. Learners need prior knowledge of SSH, Linux commands, and workload management to execute their MPI programs on a cluster. Additionally, performance analysis, which is crucial for improving program behavior and identifying errors, requires an understanding of instrumentation, measurement, and typical performance issues in parallel programs. This requirement impedes beginners from benefiting from performance visualization. To make these instruments available to beginners in parallel programming, we are developing EduMPI. This novel learning support tool allows learners to execute MPI programs in a real environment on high-performance clusters from an early stage of parallel programming education. EduMPI helps learners to gain visual insights into their program’s performance through analysis at runtime.

@inproceedings{RothSuess2023,
  author = {\underline{Anna-Lena Roth} and S\"{u}\ss{}, Tim},
  title = {Performance Analysis Tools for MPI Applications and their Use in Programming Education},
  year = {2023},
  publisher = {ACM},
  pages = {361--368},
  booktitle = {Companion of the 2023 ACM/SPEC International Conference on Performance Engineering},
  doi = {10.1145/3578245.3584358}
}

Performance analysis tools are frequently used to support the development of parallel MPI applications. They facilitate the detection of errors, bottlenecks, or inefficiencies but differ substantially in their instrumentation, measurement, and type of feedback. Especially, tools that provide visual feedback are helpful for educational purposes. They provide a visual abstraction of program behavior, supporting learners to identify and understand performance issues and write more efficient code. However, existing professional tools for performance analysis are very complex, and their use in beginner courses can be very demanding. Foremost, their instrumentation and measurement require deep knowledge and take a long time. Immediate, as well as straightforward feedback, is essential to motivate learners. This paper provides an extensive overview of performance analysis tools for parallel MPI applications, which experienced developers broadly use today. It also gives an overview of existing educational tools for parallel programming with MPI and shows their shortcomings compared to professional tools. Using tools for performance analysis of MPI programs in educational scenarios can promote the understanding of program behavior in large HPC systems and support learning parallel programming. At the same time, the complexity of the programs and the lack of infrastructure in educational institutions are barriers. These aspects will be considered and discussed in detail.

@inproceedings{PosterRothPosner2026,
  author = {Roth, Anna-Lena and Posner, Jonas},
  title = {Near-Real-Time Performance Visualization of MPI Applications: Integrating Performance Analysis into HPC Education},
  year = {2026},
  note = {Accepted for publication: SC '26: Doctoral Showcase; to appear}
}

@phdthesis{Roth2026,
  author = {Anna-Lena Roth},
  title = {Integrating Performance Analysis into HPC Education: Concept, Practice, and Evaluation},
  year = {2026},
  school = {Fulda University of Applied Sciences},
  urn = {urn:nbn:de:hebis:66-opus4-12322},
  url = {https://nbn-resolving.org/urn:nbn:de:hebis:66-opus4-12322}
}

High Performance Computing (HPC) enables solving complex scientific and engineering problems that cannot be addressed with conventional systems due to limited computational power or insufficient resources, and is therefore gaining increasing importance in research and industry. Accordingly, HPC has become an integral part of higher education. Efficient use of HPC cluster resources relies on programming models such as the Message Passing Interface (MPI) to develop scalable parallel programs across multiple compute nodes. However, MPI-based development is challenging, as it requires not only solid knowledge of parallel programming, algorithms, and communication mechanisms, but also a deep understanding of performance-critical aspects. Program performance depends on various factors, including hardware and network architectures of HPC clusters. Experienced developers use professional performance analysis tools to identify performance issues. However, using these tools is complex and requires detailed knowledge of cluster architectures, performance metrics, and analysis methods, posing barriers for students. This thesis examines the integration of performance analysis into HPC education and the challenges students face. In this context, EduMPI Suite is introduced as an educational tool for parallel programming with MPI. Through a graphical user interface (EduMPI GUI), learners execute MPI programs on an HPC cluster and receive automated visual feedback on performance-relevant metrics in near-real-time. To support this functionality, EduMPI Suite incorporates a measurement and data-management system comprising an Open MPI fork extended with measurement capabilities and a centralized time-series database. EduMPI Suite is characterized by low measurement overhead (<3.8%), robust and persistent data storage, and low query latency (<0.09 s). EduMPI GUI focuses on visualizing MPI communication and internal algorithmic communication patterns of collective operations. In extensive usability studies and evaluations conducted over multiple semesters, the effectiveness, efficiency, usability, and user satisfaction of EduMPI Suite in identifying performance issues were assessed and compared with established professional tools. The results demonstrate that EduMPI Suite significantly lowers barriers to executing and analyzing MPI programs and is consistently perceived by students as easier and more intuitive than established tools. Students working with EduMPI Suite required substantially less assistance when completing tasks. In a moderated usability study, 96% of tasks related to performance issue identification were successfully completed using EduMPI Suite, compared to only 60% with established tools. EduMPI Suite has proven effective for integrating performance analysis into HPC education.