title = {Sustainable-HPC: toward Digital Twin for active management of self-cooled data centers with Renewable Energy Sources and waste heat recovery},
abstract = {High-performance computing (HPC) data centers are key for advancing research and industry, but their high energy consumption and environmental impact pose critical sustainability challenges. Few HPC centers currently use renewable energy sources, particularly hydrogen-based storage, or integrate waste heat reuse. Recently, Digital Twins (DTs) are proving crucial for advancing sustainable HPC centers, enabling the integration of advanced monitoring and energy optimization with renewable energy sources. They simulate operations, predict maintenance needs, balance workloads, and support renewable integration for enhanced efficiency.}
booktitle = {33rd Euromicro International Conference on Parallel, Distributed,
and Network-Based Processing, {PDP} 2025, Turin, Italy, March 12-14,
abstract = {The HPC4AI datacenter hosted at the Computer Science Department of the University of Turin was born to address the exponentially increasing computing needs of cross-disciplinary research on AI. To address the needs of modern AI, HPC4AI rethinks the traditional usage of Cloud and HPC systems where the Cloud provides a modern interface for HPC, and HPC serves as an accelerator for the cloud. To date, it has supported 40+ research projects across a broad range of domains, from astronomy and medicine to human sciences. Furthermore, it acts as an R\&D platform to study, develop, and test new datacenter technologies. As such, it hosts a zoo of exotic computing platforms and the first prototype of two-phase evaporative server cooling. This work describes operating and managing HPC4AI with its challenges and lessons learned, with an analysis of key opportunities for digital twins.},
author = {Vaccaro, Viviana and Birke, Robert and Tagliabue, Lavinia Chiara and Rabellino, Sergio and Aldinucci, Marco},
booktitle = {2025 33rd Euromicro International Conference on Parallel, Distributed, and Network-Based Processing (PDP)},
...
...
@@ -134,11 +154,13 @@
keywords = {icsc, dyman},
pages = {499-505},
title = {HPC4AI@UNITO: A Use Case For Datacenter Digital Twin},
abstract = {Synthetic data offers alternatives for data augmentation and sharing. Till date, it remains unknown how to use watermarking techniques to trace and audit synthetic tables generated by tabular diffusion models to mitigate potential misuses. In this paper, we design TabWak, the first watermarking method to embed invisible signatures that control the sampling of Gaussian latent codes used to synthesize table rows via the diffusion backbone. TabWak has two key features. Different from existing image watermarking techniques, TabWak uses self-cloning and shuffling to embed the secret key in positional information of random seeds that control the Gaussian latents, allowing to use different seeds at each row for high inter-row diversity and enabling row-wise detectability. To further boost the robustness of watermark detection against post-editing attacks, TabWak uses a valid-bit mechanism that focuses on the tail of the latent code distribution for superior noise resilience. We provide theoretical guarantees on the row diversity and effectiveness of detectability. We evaluate TabWak on five datasets against baselines to show that the quality of watermarked tables remains nearly indistinguishable from non-watermarked tables while achieving high detectability in the presence of strong post-editing attacks, with a 100\% true positive rate at a 0.1\% false positive rate on synthetic tables with fewer than 300 rows. Our code is available at the following repository https://github.com/chaoyitud/TabWak.},
author = {Zhu, Chaoyi and Tang, Jiayi and Galjaard, Jeroen M. and Chen, Pin-Yu and Birke, Robert Birke and Bos, Cornelis and Chen, Lydia Y.},
