activity
20042021
most citedDiscrete solution of the electrokinetic equations

109 citations · 282 across the 12 of their papers we have counts for

collaborators

12 papers

physics.ed-ph2021

Remote teaching data-driven physical modeling through a COVID-19 open ended data challenge

Marco Cosentino Lagomarsino, Guglielmo Pacifico, Valerio Firmano +7

Physics can be seen as a conceptual approach to scientific problems, a method for discovery, but teaching this aspect of our discipline can be a challenge. We report on a first-tim…

cs.DC2018★ 3 cited

Real-time cortical simulations: energy and interconnect scaling on distributed systems

Francesco Simula, Elena Pastorelli, Pier Stanislao Paolucci +11

We profile the impact of computation and inter-processor communication on the energy consumption and on the scaling of cortical simulations approaching the real-time regime on dist…

cs.DC2018★ 1 cited

Large Scale Low Power Computing System - Status of Network Design in ExaNeSt and EuroExa Projects

Roberto Ammendola, Andrea Biagioni, Fabrizio Capuani +10

The deployment of the next generation computing platform at ExaFlops scale requires to solve new technological challenges mainly related to the impressive number (up to 10^6) of co…

cs.DC2018

The Brain on Low Power Architectures - Efficient Simulation of Cortical Slow Waves and Asynchronous States

Roberto Ammendola, Andrea Biagioni, Fabrizio Capuani +10

Efficient brain simulation is a scientific grand challenge, a parallel/distributed coding challenge and a source of requirements and suggestions for future computing architectures.…

cs.DC2018★ 6 cited

Gaussian and exponential lateral connectivity on distributed spiking neural network simulation

Elena Pastorelli, Pier Stanislao Paolucci, Francesco Simula +10

We measured the impact of long-range exponentially decaying intra-areal lateral connectivity on the scaling and memory occupation of a distributed spiking neural network simulator…

q-bio.MN2017★ 22 cited

Quantifying the entropic cost of cellular growth control

Daniele De Martino, Fabrizio Capuani, Andrea De Martino

We quantify the amount of regulation required to control growth in living cells by a Maximum Entropy approach to the space of underlying metabolic states described by genome-scale…