216 citations · 362 across the 14 of their papers we have counts for
17 papers
Duration-Informed Workload Scheduler
Daniela Loreti, Davide Leone, Andrea Borghesi
High-performance computing systems are complex machines whose behaviour is governed by the correct functioning of its many subsystems. Among these, the workload scheduler has a cru…
Online Job Failure Prediction in an HPC System
Francesco Antici, Andrea Borghesi, Zeynep Kiziltan
Modern High Performance Computing (HPC) systems are complex machines, with major impacts on economy and society. Along with their computational capability, their energy consumption…
Design of an energy aware petaflops class high performance cluster based on power architecture
W. A. Ahmad, A. Bartolini, F. Beneventi +9
In this paper we present D.A.V.I.D.E. (Development for an Added Value Infrastructure Designed in Europe), an innovative and energy efficient High Performance Computing cluster desi…
RUAD: unsupervised anomaly detection in HPC systems
Martin Molan, Andrea Borghesi, Daniele Cesarini +2
The increasing complexity of modern high-performance computing (HPC) systems necessitates the introduction of automated and data-driven methodologies to support system administrato…
Machine Learning for Combinatorial Optimisation of Partially-Specified Problems: Regret Minimisation as a Unifying Lens
Stefano Teso, Laurens Bliek, Andrea Borghesi +4
It is increasingly common to solve combinatorial optimisation problems that are partially-specified. We survey the case where the objective function or the relations between variab…
Deep Learning for Virus-Spreading Forecasting: a Brief Survey
Federico Baldo, Lorenzo Dall'Olio, Mattia Ceccarelli +5
The advent of the coronavirus pandemic has sparked the interest in predictive models capable of forecasting virus-spreading, especially for boosting and supporting decision-making…
A Machine Learning Approach to Online Fault Classification in HPC Systems
Alessio Netti, Zeynep Kiziltan, Ozalp Babaoglu +3
As High-Performance Computing (HPC) systems strive towards the exascale goal, failure rates both at the hardware and software levels will increase significantly. Thus, detecting an…
An Analysis of Regularized Approaches for Constrained Machine Learning
Michele Lombardi, Federico Baldo, Andrea Borghesi +1
Regularization-based approaches for injecting constraints in Machine Learning (ML) were introduced to improve a predictive model via expert knowledge. We tackle the issue of findin…
Improving Deep Learning Models via Constraint-Based Domain Knowledge: a Brief Survey
Andrea Borghesi, Federico Baldo, Michela Milano
Deep Learning (DL) models proved themselves to perform extremely well on a wide variety of learning tasks, as they can learn useful patterns from large data sets. However, purely d…
Combining Learning and Optimization for Transprecision Computing
Andrea Borghesi, Giuseppe Tagliavini, Michele Lombardi +2
The growing demands of the worldwide IT infrastructure stress the need for reduced power consumption, which is addressed in so-called transprecision computing by improving energy e…
Injective Domain Knowledge in Neural Networks for Transprecision Computing
Andrea Borghesi, Federico Baldo, Michele Lombardi +1
Machine Learning (ML) models are very effective in many learning tasks, due to the capability to extract meaningful information from large data sets. Nevertheless, there are learni…
COUNTDOWN Slack: a Run-time Library to Reduce Energy Footprint in Large-scale MPI Applications
Daniele Cesarini, Andrea Bartolini, Andrea Borghesi +3
The power consumption of supercomputers is a major challenge for system owners, users, and society. It limits the capacity of system installations, it requires large cooling infras…
Online Anomaly Detection in HPC Systems
Andrea Borghesi, Antonio Libri, Luca Benini +1
Reliability is a cumbersome problem in High Performance Computing Systems and Data Centers evolution. During operation, several types of fault conditions or anomalies can arise, ra…
Anomaly Detection using Autoencoders in High Performance Computing Systems
Andrea Borghesi, Andrea Bartolini, Michele Lombardi +2
Anomaly detection in supercomputers is a very difficult problem due to the big scale of the systems and the high number of components. The current state of the art for automated an…
Online Fault Classification in HPC Systems through Machine Learning
Alessio Netti, Zeynep Kiziltan, Ozalp Babaoglu +3
As High-Performance Computing (HPC) systems strive towards the exascale goal, studies suggest that they will experience excessive failure rates. For this reason, detecting and clas…
FINJ: A Fault Injection Tool for HPC Systems
Alessio Netti, Zeynep Kiziltan, Ozalp Babaoglu +3
We present FINJ, a high-level fault injection tool for High-Performance Computing (HPC) systems, with a focus on the management of complex experiments. FINJ provides support for cu…
Pricing Schemes for Energy-Efficient HPC Systems: Design and Exploration
Andrea Borghesi, Andrea Bartolini, Michela Milano +1
Energy efficiency is of paramount importance for the sustainability of HPC systems. Energy consumption limits the peak performance of supercomputers and accounts for a large share…