arXiv subjects

Jonas Posner

Publications and source records attributed to Jonas Posner.

6 recordsLinked to original sources

arXiv2026

Performance Analysis in Parallel Programming Education: A Comparative Usability Study

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.

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arXiv2026

An Empirical Analysis of High-Performance Computing Education in Germany

The growing importance of High-Performance Computing (HPC) requires the systematic integration of parallel programming and performance-oriented competencies into computational science curricula. Effective HPC education combines theoretical foundations with practical experience on real cluster infrastructures, enabling students to understand scalability, efficiency, and architectural differences between shared and distributed memory systems. However, cross-institutional evidence on how HPC education is implemented, and how curricula relate to locally available infrastructure, remains limited. We address this gap through a systematic empirical assessment of HPC education at 102 academic institutions in Germany. Based on module handbooks and course catalogs, we identified 178 HPC-related courses and evaluated their competency coverage and curricular placement. We additionally assessed local academic HPC cluster infrastructures with respect to availability, size, and documented accessibility for teaching. The results show that 67.6% of institutions offer at least one HPC-related course, but these offerings are predominantly elective modules at the master's level, with limited integration in bachelor's programs. Although 61.8% of institutions operate HPC clusters, only 23.0% explicitly document their availability for educational use, as infrastructures are mainly reserved for research. Statistical analysis indicates a significant association between restricted teaching access and reduced curricular emphasis on practical competencies such as resource management, cluster usage, parallel debugging, and performance analysis. Overall, the findings reveal a structural imbalance between theoretical instruction and the development of practical HPC competencies in German higher education.

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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.

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arXiv2026

Work Stealing for the 2D-Mesh Topology of Satellite Constellations in Low Earth Orbit

Asynchronous Many-Task (AMT) is a parallel programming model used in High Performance Computing (HPC). An AMT runtime can distribute fine-grained tasks across processing units called workers, through work stealing: when a worker has no tasks left to process, it tries to steal tasks from other workers. Workers are not restricted to a single compute node but can also be distributed across multiple nodes of an HPC cluster. Existing AMT runtimes assume a fully connected network with low, uniform latency and perform global work stealing, selecting another worker at random from all workers in the system. Space Edge Computing (SEC) uses constellations of satellites in Low Earth Orbit (LEO) as distributed compute clusters. Unlike HPC clusters, LEO satellites communicate through inter-satellite links that form a sparse mesh topology. Reaching a distant satellite requires multiple hops, each adding latency. As a step toward adapting AMT to SEC, this paper proposes a neighbor-only work stealing strategy in which workers steal exclusively from directly connected neighbors, avoiding multi-hop communication. An analytical model shows that restricting stealing this way yields a per-attempt latency advantage that grows with constellation size. Preliminary experiments on an HPC cluster with an emulated mesh over uniform low-latency links isolate the effect of victim selection: the neighbor-only strategy performs within ~2.2% of global stealing on both balanced and irregular workloads, indicating that restricting the victim set does not harm load balancing in this setting. Taken together, the experiments suggest that neighbor-only stealing can be on a par with global stealing, and the model suggests that neighbor-only stealing becomes preferable at scale.

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arXiv2026

Evaluating Malleable Job Scheduling in HPC Clusters using Real-World Workloads

Optimizing resource utilization in high-performance computing (HPC) clusters is essential for maximizing both system efficiency and user satisfaction. However, traditional rigid job scheduling often results in underutilized resources and increased job waiting times. This work evaluates the benefits of resource elasticity, where the job scheduler dynamically adjusts the resource allocation of malleable jobs at runtime. Using real workload traces from the Cori, Eagle, and Theta supercomputers, we simulate varying proportions (0-100%) of malleable jobs with the ElastiSim software. We evaluate five job scheduling strategies, including a novel one that maintains malleable jobs at their preferred resource allocation when possible. Results show that, compared to fully rigid workloads, malleable jobs yield significant improvements across all key metrics. Considering the best-performing scheduling strategy for each supercomputer, job turnaround times decrease by 37-67%, job makespan by 16-65%, job wait times by 73-99%, and node utilization improves by 5-52%. Although improvements vary, gains remain substantial even at 20% malleable jobs. This work highlights important correlations between workload characteristics (e.g., job runtimes and node requirements), malleability proportions, and scheduling strategies. These findings confirm the potential of malleability to address inefficiencies in current HPC practices and demonstrate that even limited adoption can provide substantial advantages, encouraging its integration into HPC resource management.

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arXiv2026

Exploring Performance-Productivity Trade-offs in AMT Runtimes: A Task Bench Study of Itoyori, ItoyoriFBC, HPX, and MPI

Asynchronous Many-Task (AMT) runtimes offer a productive alternative to the Message Passing Interface (MPI). However, the diverse AMT landscape makes fair comparisons challenging. Task Bench, proposed by Slaughter et al., addresses this challenge through a parameterized framework for evaluating parallel programming systems. This work integrates two recent cluster AMTs, Itoyori and ItoyoriFBC, into Task Bench for comprehensive evaluation against MPI and HPX. Itoyori employs a Partitioned Global Address Space (PGAS) model with RDMA-based work stealing, while ItoyoriFBC extends it with futurebased synchronization. We evaluate these systems in terms of both performance and programmer productivity. Performance is assessed across various configurations, including compute-bound kernels, weak scaling, and both imbalanced and communication-intensive patterns. Performance is quantified using application efficiency, i.e., the percentage of maximum performance achieved, and the Minimum Effective Task Granularity (METG), i.e., the smallest task duration before runtime overheads dominate. Programmer productivity is quantified using Lines of Code (LOC) and the Number of Library Constructs (NLC). Our results reveal distinct trade-offs. MPI achieves the highest efficiency for regular, communication-light workloads but requires verbose, lowlevel code. HPX maintains stable efficiency under load imbalance across varying node counts, yet ranks last in productivity metrics, demonstrating that AMTs do not inherently guarantee improved productivity over MPI. Itoyori achieves the highest efficiency in communication-intensive configurations while leading in programmer productivity. ItoyoriFBC exhibits slightly lower efficiency than Itoyori, though its future-based synchronization offers potential for expressing irregular workloads.

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