Data centers and their compute nodes require accurate and flexible digital twins capable of modeling the complex interplay of workloads, environmental parameters, and physical metrics. Current machine learning approaches for HPC and its telemetry typically rely on a static subset of anonymous, fixed-position sensor variables tailored to single tasks. Consequently, these models become obsolete when target tasks change or sensor metrics vary. We propose SeT-Diff, the first foundational model for compute node telemetry and time-series. Unlike rigid architectures, our diffusion-based approach conditions the generative process on each sensor’s semantic description, decoupling the system dynamics from the structure of the dataset. Experiments on a real-world supercomputer dataset demonstrate a Mean Absolute Error (MAE) of 0.0470 on reconstruction tasks. SeT-Diff exhibits zero-shot permutation stability, maintaining accuracy with negligible degradation even when sensors are shuffled. A single pre-trained model effectively performs data imputation, forecasting, and virtual sensing - achieving a 0.033 MAE in thermal inference - making SeT-Diff an effective data-driven digital twin for HPC systems.

Weight snapshots taken during neural network training provide a foundation for reproducibility and for understanding how models evolve during learning. They indicate whether networks progress toward higher accuracy or diverge toward poor generalization, yet their size and frequency impose severe storage and I/O burdens. As models scale, snapshots exhibit substantial cross-epoch redundancy, making them increasingly difficult to archive and analyze efficiently. We introduce a Merkle-tree deduplication pipeline that removes redundancy while exposing metadata about training dynamics. Chunking and deduplicating weights yields 70–80% storage savings across CIFAR-10/100 and protein diffraction datasets, outperforming list-based deduplication and per-snapshot compression baselines. Beyond space savings, Merkle-tree metadata categorizes chunks as fixed duplicates, shifted duplicates, or first occurrences. These signals predict validation accuracy with mean absolute error below 1% and provide an optional, metadata-driven signal to inform early stopping, enabling savings of 16–72% of the training epochs with negligible accuracy loss. Our work demonstrates that Merkle-tree deduplication provides a unified approach to reduce overhead, preserve reproducibility, and explain training dynamics within user-defined error tolerances, without disrupting the learning loop.

Large Language Models (LLMs) inference is central to modern AI applications, dominating worldwide datacenter workloads, making it critical to predict its energy footprint. Existing approaches estimate energy consumption as a simple linear function of input and output sequence. However, by analyzing the autoregressive structure of Transformers, which implies a fundamentally non-linear relationship between input and output sequence lengths and energy consumption, we demonstrate the existence of a generation energy minima. Peak efficiency occurs with short-to-moderate inputs and medium-length outputs, while efficiency drops sharply for long inputs or very short outputs. Consequently, we propose SweetSpot, an analytical model derived from the computational and memory-access complexity of the Transformer architecture, which accurately characterizes the efficiency curve as a function of input and output lengths. To assess accuracy, we measure energy consumption using TensorRT-LLM on NVIDIA H100 GPUs across a diverse set of LLMs ranging from 1B to 9B parameters, including OPT, LLaMA, Gemma, Falcon, Qwen2, and Granite. We test input and output lengths from 64 to 4096 tokens and achieve a mean MAPE of 1.79%. Our results show that aligning sequence lengths with these efficiency ''sweet spots'' reduce energy usage, up to 33.41x, enabling informed truncation, summarization, and adaptive generation strategies in production systems.

As modern High-Performance Computing (HPC) systems push the boundaries of computational capabilities, their power consumption becomes a serious threat to environmental and energy sustainability. In such a context, accurate prediction of the jobs’ power consumption is instrumental to develop efficient power management strategies acting at the system level. To this end, in this paper, we present an online prediction algorithm to predict job power consumption in a production HPC system, prior to job execution. Our solution employs machine learning tools, and it is able to predict the minimum, average and maximum power consumption of a job, aggregated per node throughout its execution. Our approach leverages only information which is available at the time of job submission, and it is validated on two datasets extracted from production supercomputers, namely F-DATA from Supercomputer Fugaku and PM100 from Marconi100. Our experimental results show that our prediction algorithm outperforms state-of-the-art techniques, and it can accurately predict job power consumption, by obtaining an error of less than 12% on F-DATA and less than 22% on PM100.

The increasing reliance on High-Performance Computing (HPC) systems to execute complex scientific and industrial workloads raises significant security concerns related to the misuse of HPC resources for unauthorized or malicious activities. Rogue job executions can threaten the integrity, confidentiality, and availability of HPC infrastructures. Given the scale and heterogeneity of HPC job submissions, manual or ad hoc monitoring is inadequate to effectively detect such misuse. Therefore, automated solutions capable of systematically analyzing job submissions are essential to detect rogue workloads. To address this challenge, we present RoWD (Rogue Workload Detector), the first framework for automated and systematic security screening of the HPC job-submission pipeline. RoWD is composed of modular plug-ins that classify different types of workloads and enable the detection of rogue jobs through the analysis of job scripts and associated metadata. We deploy RoWD on the Supercomputer Fugaku to classify AI workloads and release SCRIPT-AI, the first dataset of annotated job scripts labeled with workload characteristics. We evaluate RoWD on approximately 50K previously unseen jobs executed on Fugaku between 2021 and 2025. Our results show that RoWD accurately classifies AI jobs (achieving an F1 score of 95%), is robust against adversarial behavior, and incurs low runtime overhead, making it suitable for strengthening the security of HPC environments and for real-time deployment in production systems.

We develop novel annotation guidelines for sentence-level subjectivity detection, which are not limited to language-specific cues. We use our guidelines to collect NewsSD-ENG, a corpus of 638 objective and 411 subjective sentences extracted from English news articles on controversial topics. Our corpus paves the way for subjectivity detection in English and across other languages without relying on language-specific tools, such as lexicons or machine translation. We evaluate state-of-the-art multilingual transformer-based models on the task in mono-, multi-, and cross-language settings. For this purpose, we re-annotate an existing Italian corpus. We observe that models trained in the multilingual setting achieve the best performance on the task.

Modern High-Performance Computing (HPC) systems play a fundamental role in driving scientific research, as they execute computationally intensive jobs originating from diverse domains. However, HPC jobs are characterized by conflicting computational requirements, which may cause inefficiencies in resource usage, system throughput and energy consumption. One approach to tackling this problem is to distinguish between memory-bound and compute-bound jobs at their submission time, with the goal of making informed decisions about their execution. In this paper, we present MCBound, the first online data-driven framework to classify HPC jobs as memory/compute-bound before job execution, without user intervention. We propose a systematic characterization technique to generate a reference dataset from historical data for initial classification model training. Using the proposed characterization technique, we analyze the data of 2.2 million job runs on the Supercomputer Fugaku1, a production HPC system installed at the RIKEN Center for Computational Science, in Japan. We implement MCBound for Fugaku and classify the jobs executed during February 2024. Our approach is proven effective, as it obtains an F1-macro average score of at least 0.89 as prediction quality, while incurring a negligible overhead on the system’s operations. Our Python-based implementation of MCBound can be seamlessly configured and deployed in other HPC systems.1https://www.fujitsu.com/global/about/innovation/fugaku/

AMICA is an argument mining-based search engine, specifically designed for the analysis of scientific literature related to Covid-19. AMICA retrieves scientific papers based on matching keywords and ranks the results based on the papers' argumentative content. An experimental evaluation conducted on a case study in collaboration with the Italian National Institute of Health shows that the AMICA ranking agrees with expert opinion, as well as, importantly, with the impartial quality criteria indicated by Cochrane Systematic Reviews.