42 citations · 130 across the 14 of their papers we have counts for
16 papers
SweetSpot: An Analytical Model for Predicting Energy Efficiency of LLM Inference
Hiari Pizzini Cavagna, Andrea Proia, Giacomo Madella +5
Large Language Models (LLMs) inference is central to modern AI applications, dominating worldwide datacenter workloads, making it critical to predict its energy footprint. Existing…
Automatic Feature Learning for Essence: a Case Study on Car Sequencing
Alessio Pellegrino, Özgür Akgün, Nguyen Dang +2
Constraint modelling languages such as Essence offer a means to describe combinatorial problems at a high-level, i.e., without committing to detailed modelling decisions for a part…
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…
TabID: Automatic Identification and Tabulation of Subproblems in Constraint Models
Özgür Akgün, Ian P. Gent, Christopher Jefferson +5
The performance of a constraint model can often be improved by converting a subproblem into a single table constraint (referred to as tabulation). Finding subproblems to tabulate i…
A Constraint Programming-based Job Dispatcher for Modern HPC Systems and Applications
Cristian Galleguillos, Zeynep Kiziltan, Ricardo Soto
Constraint Programming (CP) is a well-established area in AI as a programming paradigm for modelling and solving discrete optimization problems, and it has been been successfully a…
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…