activity
20192022
most citedOn-line Application Autotuning Exploiting Ensemble Models

1 citations · 3 across the 6 of their papers we have counts for

collaborators

8 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…

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.DC2019

Tunable Approximations to Control Time-to-Solution in an HPC Molecular Docking Mini-App

Davide Gadioli, Gianluca Palermo, Stefano Cherubin +7

The drug discovery process involves several tasks to be performed in vivo, in vitro and in silico. Molecular docking is a task typically performed in silico. It aims at finding the…

cs.DC2019

Exploiting OpenMP & OpenACC to Accelerate a Molecular Docking Mini-App in Heterogeneous HPC Nodes

Emanuele Vitali, Davide Gadioli, Gianluca Palermo +3

In drug discovery, molecular docking is the task in charge of estimating the position of a molecule when interacting with the docking site. This task is usually used to perform scr…

cs.DC20191 cited

On-line Application Autotuning Exploiting Ensemble Models

Tomas Martinovic, Davide Gadioli, Gianluca Palermo +1

Application autotuning is a promising path investigated in literature to improve computation efficiency. In this context, the end-users define high-level requirements and an autono…