Andrea Bartolini

EAAI Journal 2026 Journal Article

A transformer-based approach for source code classification for heterogeneous device mapping

• Marco Siino
• Emanuele Parisi
• Francesco Barchi
• Andrea Acquaviva
• Andrea Bartolini

AAAI Conference 2026 Short Paper

Context-Aware Diffusion for Telemetry Time Series with Permutation-Stable Feature Modeling (Student Abstract)

• Giovanni B. Esposito
• Daniele Cesarini
• Andrea Bartolini

We present a context-aware diffusion model for multivariate time series generation in dynamic and partially observed environments, with applications to data-center computing node's telemetry and beyond. The model integrates pretrained textual embeddings to represent feature semantics, enabling flexible, context-guided generation and improved adaptability to unseen or re-ordered input features. Built on a transformer architecture, it employs both time-wise and feature-wise masking to support missing data during training and inference. We show that the model is robust to permutations with respect to the feature dimension, mantaining stable performance in settings where input configurations vary. Empirical evaluations on HPC sensor data illustrate the model’s versatility across generation and imputation tasks. This work introduces a modular and generalizable framework for time series modeling in complex, high-dimensional systems which can serve as a digital-twin for data-center's compute node telemetry.

TAAS Journal 2026 Journal Article

Modeling and Controlling Many-Core HPC Processors: An Alternative to PID and Moving Average Algorithms

• Giovanni Bambini
• Alessandro Ottaviano
• Christian Conficoni
• Andrea Tilli
• Luca Benini
• Andrea Bartolini

The race toward performance increase and computing power has led to chips with heterogeneous and complex designs, integrating an ever-growing number of cores on the same monolithic chip or chiplet silicon die. Higher integration density, compounded with the slowdown of technology-driven power reduction, implies that power and thermal management become increasingly relevant. Unfortunately, existing research lacks a detailed analysis and modeling of thermal, power, and electrical coupling effects and how they have to be jointly considered to perform dynamic control of complex and heterogeneous Multi-Processor System on Chips (MPSoCs). To close the gap, in this work, we first provide a detailed thermal and power model targeting a modern High Performance Computing (HPC) MPSoC. We consider real-world coupling effects such as actuators’ non-idealities and the exponential relation between the dissipated power, the temperature state, and the voltage level in a single processing element. We analyze how these factors affect the control algorithm behavior and the type of challenges that they pose. Based on the analysis, we propose a thermal capping strategy inspired by Fuzzy control theory to replace the state-of-the-art PID controller, as well as a root-finding iterative method to optimally choose the shared voltage value among cores grouped in the same voltage domain. We evaluate the proposed controller with model-in-the-loop and hardware-in-the-loop co-simulations. We show an improvement over state-of-the-art methods of up to \(5\times\) the maximum exceeded temperature while providing an average of \(3.56\%\) faster application execution runtime across all the evaluation scenarios.

EAAI Journal 2019 Journal Article

A semisupervised autoencoder-based approach for anomaly detection in high performance computing systems

• Andrea Borghesi
• Andrea Bartolini
• Michele Lombardi
• Michela Milano
• Luca Benini

AIJ Journal 2017 Journal Article

Empirical decision model learning

• Michele Lombardi
• Michela Milano
• Andrea Bartolini

ECAI Conference 2016 Conference Paper

DARDIS: Distributed And Randomized DIspatching and Scheduling

• Thomas Bridi
• Michele Lombardi 0001
• Andrea Bartolini
• Luca Benini
• Michela Milano

Scheduling and dispatching are critical enabling technologies in supercomputing and grid computing. In these contexts, scalability is an issue: we have to allocate and schedule up to tens of thousands of tasks on tens of thousands of resources. This problem scale is out of reach for complete and centralized scheduling approaches.

AAAI Conference 2012 Conference Paper

Optimization and Controlled Systems: A Case Study on Thermal Aware Workload Dispatching

• Andrea Bartolini
• Michele Lombardi
• Michela Milano
• Luca Benini

Although successfully employed on many industrial problems, Combinatorial Optimization still has limited applicability on several real-world domains, often due to modeling difficulties. This is typically the case for systems under the control of an on-line policy: even when the policy itself is well known, capturing its effect on the system in a declarative model is often impossible by conventional means. Such a difficulty is at the root of the classical, sharp separation between off-line and on-line approaches. In this paper, we investigate a general method to model controlled systems, based on the integration of Machine Learning and Constraint Programming (CP). Specifically, we use an Artificial Neural Network (ANN) to learn the behavior of a controlled system (a multicore CPU with thermal controllers) and plug it into a CP model by means of Neuron Constraints. The method obtains significantly better results compared to an approach with no ANN guidance. Neuron Constraints were first introduced in (Bartolini et al. 2011b) as a mean to model complex systems: providing evidence of their applicability to controlled systems is a significant step forward, broadening the application field of combinatorial methods and disclosing opportunities for hybrid off-line/on-line optimization.