Software for Efficient and Energy-Aware Supercomputers

Informazioni relative al progetto

SEANERGYS

ID dell’accordo di sovvenzione: 101177590

DOI

10.3030/101177590

Data della firma CE 18 Giugno 2025

Data di avvio 1 Giugno 2025

Data di completamento 31 Maggio 2029

Finanziato da

• Digital, Industry and Space

Costo totale

€ 32 947 132,50

Contributo UE

€ 16 473 566,30

Coordinato da

FORSCHUNGSZENTRUM JULICH GMBH
Germany

CORDIS fornisce collegamenti ai risultati finali pubblici e alle pubblicazioni dei progetti ORIZZONTE.

I link ai risultati e alle pubblicazioni dei progetti del 7° PQ, così come i link ad alcuni tipi di risultati specifici come dataset e software, sono recuperati dinamicamente da .OpenAIRE .

Risultati finali

Documents, reports (4)

Setup of common development platform including CI/CD

Report on availability of version control, issue management, basic CI/CD functionality [T5.2]

Analysis of the State-of-the-Art Workload Managers for System-level Scheduling

Feature analysis and testing of current workload management solutions [T4.1]

Design Workshop

Report from design workshop (MS1) to drive requirement gathering and initial phases of development [all of WP1],including final component assessment and ADR setup [T1.3]

Dissemination & Communication Plan

Comprehensive strategy including website, materials, exhibitions, publications, press releases, social media engagement, and collaborations to maximize project visibility and impact. [T7.1]

Pubblicazioni

Book chapters (3)

Performance Models to Support HPC Co-scheduling

Autori: Athanasios Tsoukleidis-Karydakis, Efstratios Karapanagiotis, Nikolaos Triantafyllis, Nectarios Koziris, Georgios Goumas
Pubblicato in: Lecture Notes in Computer Science, Job Scheduling Strategies for Parallel Processing, 2026
Editore: Springer Nature Switzerland
DOI: 10.1007/978-3-032-10507-3_15

ELiSE: A Tool to Support Algorithmic Design for HPC Co-scheduling

Autori: Efstratios Karapanagiotis, Nikolaos Triantafyllis, Athanasios Tsoukleidis-Karydakis, Georgios Goumas, Nectarios Koziris
Pubblicato in: Lecture Notes in Computer Science, Job Scheduling Strategies for Parallel Processing, 2026
Editore: Springer Nature Switzerland
DOI: 10.1007/978-3-032-10507-3_16

Duration-Informed Workload Scheduler

Autori: Daniela Loreti, Davide Leone, Andrea Borghesi
Pubblicato in: Lecture Notes in Computer Science, High Performance Computing, 2025
Editore: Springer Nature Switzerland
DOI: 10.1007/978-3-032-07612-0_1

Peer reviewed articles (7)

Elevating Datacenter Resilience with ThermADNet: A Thermal Anomaly Detection System

Autori: Mohsen Seyedkazemi Ardebili, Andrea Acquaviva, Luca Benini, Andrea Bartolini
Pubblicato in: Future Generation Computer Systems, Numero 179, 2026, ISSN 0167-739X
Editore: Elsevier BV
DOI: 10.1016/J.FUTURE.2025.108311

Federated LSTM autoencoders for time series anomaly detection in production-scale HPC systems

Autori: Emmen Farooq, Michela Milano, Andrea Borghesi
Pubblicato in: Knowledge-Based Systems, Numero 334, 2025, ISSN 0950-7051
Editore: Elsevier BV
DOI: 10.1016/J.KNOSYS.2025.115043

EXASAGE: The first data center operational data analysis assistant

Autori: Junaid Ahmed Khan, Martin Molan, Andrea Bartolini
Pubblicato in: Future Generation Computer Systems, Numero 176, 2025, ISSN 0167-739X
Editore: Elsevier BV
DOI: 10.1016/J.FUTURE.2025.108185

RoWD: Automated rogue workload detector for HPC security

Autori: Francesco Antici, Jens Domke, Andrea Bartolini, Zeynep Kiziltan, Satoshi Matsuoka
Pubblicato in: Future Generation Computer Systems, Numero 180, 2026, ISSN 0167-739X
Editore: Elsevier BV
DOI: 10.1016/J.FUTURE.2026.108392

F-DATA: A Fugaku Workload Dataset for Job-centric Predictive Modelling in HPC Systems

Autori: Francesco Antici, Andrea Bartolini, Jens Domke, Zeynep Kiziltan, Keiji Yamamoto
Pubblicato in: Scientific Data, Numero 12, 2025, ISSN 2052-4463
Editore: Springer Science and Business Media LLC
DOI: 10.1038/S41597-025-05633-1

Federated transfer learning for anomaly detection in HPC systems: First real-world validation on a tier-0 supercomputer

Autori: Emmen Farooq, Michela Milano, Andrea Borghesi
Pubblicato in: Expert Systems with Applications, Numero 298, 2025, ISSN 0957-4174
Editore: Elsevier BV
DOI: 10.1016/J.ESWA.2025.129754

An online algorithm for power consumption prediction of HPC workload

Autori: Francesco Antici, Andrea Borghesi, Zeynep Kiziltan, Jens Domke, Andrea Bartolini
Pubblicato in: Future Generation Computer Systems, Numero 175, 2025, ISSN 0167-739X
Editore: Elsevier BV
DOI: 10.1016/J.FUTURE.2025.108064

Other (1)

A Framework for Developing Next-Generation HPC Schedulers

Autori: Konstantinos Katsikopoulos, Nikolaos Triantafyllis, Athanasios Tsoukleidis-Karydakis, Efstratios Karapanagiotis, Nectarios Koziris, Georgios Goumas
Editore: Zenodo
DOI: 10.5281/ZENODO.21000570

Conference proceedings (4)

SweetSpot: An Analytical Model for Predicting Energy Efficiency of LLM Inference

Autori: Hiari Pizzini Cavagna, Andrea Proia, Giacomo Madella, Giovanni Battista Esposito, Francesco Antici, Daniele Cesarini, Zeynep Kiziltan, Andrea Bartolini
Pubblicato in: Proceedings of the 17th ACM/SPEC International Conference on Performance Engineering, 2026
Editore: ACM
DOI: 10.1145/3777884.3797011

SeT-Diff: Towards Semantic Foundation Models for HPC Telemetry and Time-Series

Autori: Giovanni B. Esposito, Francesco Antici, Daniele Cesarini, Andrea Bartolini
Pubblicato in: Proceedings of the 23rd ACM International Conference on Computing Frontiers, 2026
Editore: ACM
DOI: 10.1145/3801487.3806064

MT4G: A Tool for Reliable Auto-Discovery of NVIDIA and AMD GPU Compute and Memory Topologies

Autori: Stepan Vanecek, Manuel Walter Mußbacher, Dominik Größler, Urvij Saroliya, Martin Schulz
Pubblicato in: Proceedings of the SC '25 Workshops of the International Conference for High Performance Computing, Networking, Storage and Analysis, 2025
Editore: ACM
DOI: 10.1145/3731599.3767518

SEANERGYS: Integrating Monitoring, Analytics, and Resource Management for Energy-efficient HPC System Operations

Autori: Estela Suarez, Andrea Borghesi, Sonja Happ, Hans-Christian Hoppe, Michael Ott, Simon Pickartz, Martin Schulz, Hannes Troepgen
Pubblicato in: Proceedings of the 23rd ACM International Conference on Computing Frontiers: Workshops and Special Sessions, 2026
Editore: ACM
DOI: 10.1145/3801488.3806245