arXiv subjects

Julian Kunkel

Publications and source records attributed to Julian Kunkel.

17 recordsLinked to original sources

arXiv2026

The Power of Meta Access in the Software of the Future --- Demonstrated on LLM Chat UIs and Chat AI

Chat remains the default surface for many large language model (LLM) tasks, even as agentic tools such as OpenCode excel at coding and automation. Yet the model sees the current dialogue but not the workspace around it. We explore meta access---giving the assistant a structured, permissioned view of the interface itself and propose a ladder of levels: feature tutoring; metadata, settings, and preference access; retrieval over summaries and messages, confirmed mutation, and full orchestration. Meta access can turn a chat client from many screens into one entry point for finding, navigating, organising, and operating it, while raising risks around grounding, privacy, prompt injection, and trust. General-purpose agentic harnesses are runtimes; because they can change their own settings, meta access becomes reflexive and needs a separate control-plane boundary. We ground this in the Meta Chat prototype of the Chat AI client. It builds bounded context from, e.g., summaries, settings, and relevant messages; links references to conversations, topics, and personas; supports confirmed, reversible changes; offers a browsable tutorial library; and recommends personas as user-started conversations. By exposing application state in structured form, meta access can extend screen readers from widget descriptions to semantic workspace operations. Meta access thus turns chat from a window onto one conversation into a control layer for the whole workspace, extending chat toward agentic capability without surrendering user control. As assistants become embedded in IDEs, dashboards, office suites, and operating systems, AI-era interfaces should expose a structured, permissioned model of their state and capabilities, ideally by opening their existing metamodels through a debugger-like interface.

cs.AI↗

arXiv2026

HPC-MQBench: Qualification-First Benchmarking on Slurm with a Single-Broker Kafka Evaluation

Messaging experiments on high-performance computing clusters must coordinate services, clients, and measurement within scheduler allocations. We present HPC-MQBench, a Slurm-orchestrated benchmark that links resumable experiment control to record checks, delivery accounting, and qualification before rate ranking. Its Kafka evaluation placed producers/controller, one broker, consumers, and monitoring on four nodes. With memory-backed logs, 120 workload configurations yielded 99 qualified observations, 14 outside the producer-delivery policy, and seven with invalid evidence. Two validation stages each repeated ten workloads in five blocks. In the final stage, the selected workload qualified in all five observations, with medians of 2,927 mebibytes per second balanced endpoint rate, 1.24 percent pending deliveries, and 2.56 seconds for the 99th-percentile latency. It qualified in only two observations in the earlier stage, where qualification improved after an allocation boundary without establishing a causal allocation effect. Selection is conditional on the stage and delivery policy. Resource measurements did not isolate a unique bottleneck. The contribution is a framework for distributed experiment control and auditable configuration selection at a fixed broker count; multi-broker support and scaling experiments remain future work.

cs.DC↗

arXiv2026

Optimization of Workload Mapping and Scheduling in Scaling Heterogeneous HPC Systems: A Systematic Literature Review

The increasing heterogeneity of High-Performance Computing (HPC) systems, incorporating Central Processing Units (CPUs), Graphics Processing Units (GPUs), Field-Programmable Gate Arrays (FPGAs), and accelerators, makes workload mapping and scheduling a critical optimization challenge. We present a systematic literature review following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) 2020 guidelines, covering 202 studies published from 2017 to 2026, and classifying optimization techniques for workload mapping and scheduling in heterogeneous HPC environments. We identify four solver classes: mathematical programming, heuristics, metaheuristics, and emerging methods (ML/AI, hybrid, and quantum). Makespan minimization is the dominant objective and multi-objective formulations are near-universal; ML/AI approaches have grown from negligible to dominating recent publications, while quantum methods are concentrated almost entirely in 2025-2026. Critical gaps constrain practical relevance: most studies address scheduling in isolation without joint mapping treatment; evaluations are predominantly small-scale and simulation-based; real-system evidence is sparse; and openly reproducible implementations are rare. These findings signal that the field's pressing challenge is not algorithmic diversity but deployment-grade evidence: solutions jointly formulated, validated at production scale, and openly reproducible.

cs.DC↗

arXiv2026

A Treasure Trove of Performance: Analyzing the IO500 Submission Data

The IO500 benchmark has become the community standard for evaluating HPC storage system performance, yet the detailed data contained in its submission packages remains largely unexplored beyond aggregate leaderboard rankings. We present a statistical characterization of 61 IO500 submissions from four competition lists (ISC21 through SC22), examining score distributions, inter-phase correlations, and insights derived from detailed log files that accompany each submission. Our analysis reveals that IO500 scores span four orders of magnitude. Spearman correlation analysis shows strong within-domain clustering for both bandwidth (rs = 0.78 to 0.96) and metadata (rs = 0.89 to 0.98) phases, with the composite sub-scores exhibiting rs = 0.92 at per-node level (Pearson r = 0.53). Log-level analysis uncovers file-system-specific patterns in IOR close-time overhead, straggler behavior during the stonewall wear-down phase, and parallel-find load imbalance that are invisible in aggregate scores. These findings demonstrate that IO500 submission packages constitute a valuable research resource for understanding storage system behavior. The full submission dataset is publicly available at https://github.com/IO500/submission-data, and analysis scripts at https://gitlab-ce.gwdg.de/hpc-team/io500-analysis.

cs.DC↗

arXiv2026

Repository-Scale Performance Characterization of the IO500 Benchmark

The IO500 benchmark provides a common basis for evaluating high-performance computing (HPC) storage systems, while its growing submission repository also offers an opportunity to study performance behavior across systems and time. This work presents a repository-scale characterization of 294 post-reset IO500 submissions from 116 sites spanning 2019--2025. We combine descriptive and temporal analysis with phase-level correlation, scale-sensitivity analysis, and benchmark-log examination to investigate how performance characteristics represented by individual benchmark phases relate to composite rankings. The results show strong relationships among phases measuring similar I/O behavior, but weaker and scale-sensitive relationships between bandwidth and metadata performance. Temporal and log-level analyses further reveal information that is not apparent from aggregate scores alone. These findings demonstrate the value of analyzing IO500 results beyond leader-board rankings and identify important considerations concerning system scale, benchmark execution, and repository provenance when using community benchmark data for comparative and longitudinal HPC performance analysis.

cs.DC↗

arXiv2026

TT-DAC-PS: Twin-Target Deterministic Actor-Critic with Policy Smoothing for Optimal Trade Execution

This study addresses the optimal execution of large stock sell programs by introducing TT-DAC-PS (Twin-Target Deterministic Actor-Critic with Policy Smoothing), a deterministic actor-critic architecture that combines twin exponential-moving-average critic targets with pessimistic min backup, TD3-style target policy smoothing noise, delayed actor updates, and conservative Q regularisation to curb overestimation. Exploration uses Ornstein-Uhlenbeck (OU) noise with a hybrid schedule: deterministic episode-wise decay, variance-guided adjustment based on recent reward dispersion, and a Soft Actor-Critic (SAC)-style temperature that is learned and mapped to the noise scale. The environment integrates Almgren-Chriss (AC) trade impact with Limit Order Book (LOB) prices and volumes, normalised state features, per-step volume participation caps, and a utility-based reward. The trade execution algorithm is applied to LOB data for ten U.S. stocks. Performance is assessed against reinforcement-learning baseline algorithms, including Proximal Policy Optimisation (PPO), Soft Actor-Critic (SAC), and Advantage Actor-Critic (A2C), as well as alternative trade execution algorithms, including Time-Weighted Average Price (TWAP), Volume-Weighted Average Price (VWAP), and AC. The proposed model consistently reduces mean implementation shortfall percentage with competitive variance, outperforming classical baselines and standard reinforcement-learning benchmark models.

cs.AI↗

arXiv2026

Global AI Regulations for FAIR and Ethics in High-Risk Use Cases: A Comparative Review

AI governance is shifting from voluntary ethics to enforceable, risk-based regulation, yet cross-jurisdictional divergence creates compliance uncertainty for operators of high-stakes AI. We present a comparative matrix for the EU, US, and China that maps (i) risk classification triggers, (ii) binding obligations, (iii) enforcement and accountability mechanisms, and (iv) the degree to which FAIR principles are operationalised in practice. We stress-test the matrix on three high-impact domains: Electroencephalography (EEG)-guided rehabilitation robotics, AI-enabled debt collection in prospective Central Bank Digital Currency (CBDC) ecosystems, and AI-driven allocation of scarce Graphics Processing Unit (GPU) resources in emerging AI Factory infrastructures. Using primary legal texts and implementation evidence, we identify three recurring gaps: weak interoperability mandates, difficult operationalisation of cross-regime obligations (AI + sector regulation + data protection), and under-specified governance for critical digital infrastructure use cases. To bridge the implementation gap, we outline Knowledge Blocks, a machine-checkable compliance artefact pattern based on Resource Description Framework/Web Ontology Language (RDF/OWL), Shapes Constraint Language (SHACL), and Provenance Ontology (PROV-O), enabling audit-ready compliance-by-design across multiple regimes.

cs.AI↗

arXiv2026

An Empirical Evaluation of Quantum-Inspired QUBO Methods for Heterogeneous HPC Workflow Mapping and Scheduling

Heterogeneous HPC workflow scheduling under multiple hard constraints poses a challenging combinatorial optimization problem. Classical exact solvers guarantee optimality but face scalability limits, motivating interest in quantum-inspired Quadratic Unconstrained Binary Optimization (QUBO) as an alternative optimization paradigm. This work presents a systematic empirical evaluation of QUBO-based scheduling methods against classical baselines including MILP, CP-SAT, GA, and HEFT. We evaluate three QUBO variants, single-run simulated annealing, multi-attempt annealing, and a layered QAOA-inspired schedule, with hybrid enhancement strategies on validation workflows (3-4 tasks) and synthetic scaling instances (5-20 tasks). All solvers are assessed through a unified pipeline tracking feasibility, makespan, and resource utilization under progressive constraint activation and controlled penalty sweeps. All approaches recover the expected optimal makespan on validation instances, confirming formulation correctness. However, feasibility degradation emerges for specific QUBO variants as constraint interactions intensify, particularly when communication costs are introduced. Penalty analysis reveals a sharp feasibility threshold for QUBO-SA, where insufficient penalties consistently fail and moderate-to-strong penalties restore feasibility. Scaling experiments show that classical solvers remain robust across all tested sizes, while QUBO-SA loses feasibility beyond 15 tasks and the QAOA-inspired variant beyond 10 tasks. The study provides a clear empirical characterization of the reliability boundaries of quantum-inspired QUBO formulations for HPC scheduling and identifies regimes where classical approaches remain preferable under current solver capabilities.

cs.DC↗

arXiv2026

Interactive and Urgent HPC: State of the Research

When we think of how we use smartphones, e-commerce, collaboration platforms, LLMs, etc., most of our interactions with computers are interactive and often urgent. Similar trends of interactivity and urgency are coming to HPC, with applications from simulations to data analysis and machine learning requiring more parallel computational capability and more interactivity. This chapter overviews the progress made so far along with some vectors of what the path forward will bring for greater integration of interactive and urgent HPC policies, techniques, and technologies into our HPC ecosystems.

cs.DC↗

arXiv2025

Evaluating Large Language Models for Workload Mapping and Scheduling in Heterogeneous HPC Systems

Large language models (LLMs) are increasingly explored for their reasoning capabilities, yet their ability to perform structured, constraint-based optimization from natural language remains insufficiently understood. This study evaluates twenty-one publicly available LLMs on a representative heterogeneous high-performance computing (HPC) workload mapping and scheduling problem. Each model received the same textual description of system nodes, task requirements, and scheduling constraints, and was required to assign tasks to nodes, compute the total makespan, and explain its reasoning. A manually derived analytical optimum of nine hours and twenty seconds served as the ground truth reference. Three models exactly reproduced the analytical optimum while satisfying all constraints, twelve achieved near-optimal results within two minutes of the reference, and six produced suboptimal schedules with arithmetic or dependency errors. All models generated feasible task-to-node mappings, though only about half maintained strict constraint adherence. Nineteen models produced partially executable verification code, and eighteen provided coherent step-by-step reasoning, demonstrating strong interpretability even when logical errors occurred. Overall, the results define the current capability boundary of LLM reasoning in combinatorial optimization: leading models can reconstruct optimal schedules directly from natural language, but most still struggle with precise timing, data transfer arithmetic, and dependency enforcement. These findings highlight the potential of LLMs as explainable co-pilots for optimization and decision-support tasks rather than autonomous solvers.

cs.DC↗

arXiv2025

Ethical AI: Towards Defining a Collective Evaluation Framework

Artificial Intelligence (AI) is transforming sectors such as healthcare, finance, and autonomous systems, offering powerful tools for innovation. Yet its rapid integration raises urgent ethical concerns related to data ownership, privacy, and systemic bias. Issues like opaque decision-making, misleading outputs, and unfair treatment in high-stakes domains underscore the need for transparent and accountable AI systems. This article addresses these challenges by proposing a modular ethical assessment framework built on ontological blocks of meaning-discrete, interpretable units that encode ethical principles such as fairness, accountability, and ownership. By integrating these blocks with FAIR (Findable, Accessible, Interoperable, Reusable) principles, the framework supports scalable, transparent, and legally aligned ethical evaluations, including compliance with the EU AI Act. Using a real-world use case in AI-powered investor profiling, the paper demonstrates how the framework enables dynamic, behavior-informed risk classification. The findings suggest that ontological blocks offer a promising path toward explainable and auditable AI ethics, though challenges remain in automation and probabilistic reasoning.

cs.AI↗

arXiv2025

GrapheonRL: A Graph Neural Network and Reinforcement Learning Framework for Constraint and Data-Aware Workflow Mapping and Scheduling in Heterogeneous HPC Systems

Effective resource utilization and decreased makespan in heterogeneous High Performance Computing (HPC) environments are key benefits of workload mapping and scheduling. Tools such as Snakemake, a workflow management solution, employ Integer Linear Programming (ILP) and heuristic techniques to deploy workflows in various HPC environments like SLURM (Simple Linux Utility for Resource Management) or Kubernetes. Its scheduler factors in workflow task dependencies, resource requirements, and individual task data sizes before system deployment. ILP offers optimal solutions respecting constraints, but only for smaller workflows. Meanwhile, meta-heuristics and heuristics offer faster, though suboptimal, makespan. As problem sizes, system constraints, and complexities evolve, maintaining these schedulers becomes challenging. In this study, we propose a novel solution that integrates Graph Neural Network (GNN) and Reinforcement Learning (RL) to flexibly handle workflows, dynamic constraints, and heterogeneous resources while providing quick responses. GNN manages dependencies and resource requirements, and RL optimizes scheduling decision-making via a learned policy, overcoming the need for a comprehensive global search. Experimental results with different datasets demonstrate that this method effectively adapts to different workflows, adheres to HPC constraints, and offers optimal solutions akin to ILP but with drastically reduced execution times (76 percent faster), comparable to heuristic methods (only 3.85 times slower than OLB). Our contribution is to provide a robust yet scalable mapping and scheduling solution that can handle changing constraints, as well as workload sizes and complexities in a heterogeneous HPC Compute Continuum system landscape.

cs.DC↗

arXiv2025

Workflow-Driven Modeling for the Compute Continuum: An Optimization Approach to Automated System and Workload Scheduling

The convergence of IoT, Edge, Cloud, and HPC technologies creates a compute continuum that merges cloud scalability and flexibility with HPC's computational power and specialized optimizations. However, integrating cloud and HPC resources often introduces latency and communication overhead, which can hinder the performance of tightly coupled parallel applications. Additionally, achieving seamless interoperability between cloud and on-premises HPC systems requires advanced scheduling, resource management, and data transfer protocols. Consequently, users must manually allocate complex workloads across heterogeneous resources, leading to suboptimal task placement and reduced efficiency due to the absence of an automated scheduling mechanism. To overcome these challenges, we introduce a comprehensive framework based on rigorous system and workload modeling for the compute continuum. Our method employs established tools and techniques to optimize workload mapping and scheduling, enabling the automatic orchestration of tasks across both cloud and HPC infrastructures. Experimental evaluations reveal that our approach could optimally improve scheduling efficiency, reducing execution times, and enhancing resource utilization. Specifically, our MILP-based solution achieves optimal scheduling and makespan for small-scale workflows, while heuristic methods offer up to 99% faster estimations for large-scale workflows, albeit with a 5-10% deviation from optimal results. Our primary contribution is a robust system and workload modeling framework that addresses critical gaps in existing tools, paving the way for fully automated orchestration in HPC-compute continuum environments.

cs.DC↗

arXiv2024

AdamZ: An Enhanced Optimisation Method for Neural Network Training

AdamZ is an advanced variant of the Adam optimiser, developed to enhance convergence efficiency in neural network training. This optimiser dynamically adjusts the learning rate by incorporating mechanisms to address overshooting and stagnation, that are common challenges in optimisation. Specifically, AdamZ reduces the learning rate when overshooting is detected and increases it during periods of stagnation, utilising hyperparameters such as overshoot and stagnation factors, thresholds, and patience levels to guide these adjustments. While AdamZ may lead to slightly longer training times compared to some other optimisers, it consistently excels in minimising the loss function, making it particularly advantageous for applications where precision is critical. Benchmarking results demonstrate the effectiveness of AdamZ in maintaining optimal learning rates, leading to improved model performance across diverse tasks.

cs.LG↗

arXiv2024

Interactive and Urgent HPC: Challenges and Opportunities

As a broader set of applications from simulations to data analysis and machine learning require more parallel computational capability, the demand for interactive and urgent high performance computing (HPC) continues to increase. This paper overviews the progress made so far and elucidates the challenges and opportunities for greater integration of interactive and urgent HPC policies, techniques, and technologies into HPC ecosystems.

cs.DC↗

arXiv2023

DECICE: Device-Edge-Cloud Intelligent Collaboration Framework

DECICE is a Horizon Europe project that is developing an AI-enabled open and portable management framework for automatic and adaptive optimization and deployment of applications in computing continuum encompassing from IoT sensors on the Edge to large-scale Cloud / HPC computing infrastructures. In this paper, we describe the DECICE framework and architecture. Furthermore, we highlight use-cases for framework evaluation: intelligent traffic intersection, magnetic resonance imaging, and emergency response.

cs.DC↗

arXiv2019

Analysis of parallel I/O use on the UK national supercomputing service, ARCHER using Cray LASSi and EPCC SAFE

In this paper, we describe how we have used a combination of the LASSi tool (developed by Cray) and the SAFE software (developed by EPCC) to collect and analyse Lustre I/O performance data for all jobs running on the UK national supercomputing service, ARCHER; and to provide reports on I/O usage for users in our standard reporting framework. We also present results from analysis of parallel I/O use on ARCHER and analysis on the potential impact of different applications on file system performance using metrics we have derived from the LASSi data. We show that the performance data from LASSi reveals how the same application can stress different components of the file system depending on how it is run, and how the LASSi risk metrics allow us to identify use cases that could potentially cause issues for global I/O performance and work with users to improve their I/O use. We use the IO-500 benchmark to help us understand how LASSi risk metrics correspond to observed performance on the ARCHER file systems. We also use LASSi data imported into SAFE to identify I/O use patterns associated with different research areas, understand how the research workflow gives rise to the observed patterns and project how this will affect I/O requirements in the future. Finally, we provide an overview of likely future directions for the continuation of this work.

cs.DC↗