activity
20192022
most citedA Collaborative Filtering Approach for the Automatic Tuning of Compiler Optimisations

14 citations · 19 across the 9 of their papers we have counts for

collaborators

12 papers

cs.DC20221 cited

GPU-optimized Approaches to Molecular Docking-based Virtual Screening in Drug Discovery: A Comparative Analysis

Emanuele Vitali, Federico Ficarelli, Mauro Bisson +4

COVID-19 has shown the importance of having a fast response against pandemics. Finding a novel drug is a very long and complex procedure, and it is possible to accelerate the preli…

cs.DC20211 cited

EXSCALATE: An extreme-scale in-silico virtual screening platform to evaluate 1 trillion compounds in 60 hours on 81 PFLOPS supercomputers

Davide Gadioli, Emanuele Vitali, Federico Ficarelli +7

The social and economic impact of the COVID-19 pandemic demands the reduction of the time required to find a therapeutic cure. In the contest of urgent computing, we re-designed th…

quant-ph20212 cited

Quantum Molecular Unfolding

Kevin Mato, Riccardo Mengoni, Daniele Ottaviani +1

Molecular Docking (MD) is an important step of the drug discovery process which aims at calculating the preferred position and shape of one molecule to a second when they are bound…

cs.CV2021

Dynamic Network selection for the Object Detection task: why it matters and what we (didn't) achieve

Emanuele Vitali, Anton Lokhmotov, Gianluca Palermo

In this paper, we want to show the potential benefit of a dynamic auto-tuning approach for the inference process in the Deep Neural Network (DNN) context, tackling the object detec…

cs.DC2021

Legio: Fault Resiliency for Embarrassingly Parallel MPI Applications

Roberto Rocco, Davide Gadioli, Gianluca Palermo

Due to the increasing size of HPC machines, the fault presence is becoming an eventuality that applications must face. Natively, MPI provides no support for the execution past the…

cs.DC2021

EVEREST: A design environment for extreme-scale big data analytics on heterogeneous platforms

Christian Pilato, Stanislav Bohm, Fabien Brocheton +15

High-Performance Big Data Analytics (HPDA) applications are characterized by huge volumes of distributed and heterogeneous data that require efficient computation for knowledge ext…