39 citations · 59 across the 3 of their papers we have counts for
6 papers
Bringing AI pipelines onto cloud-HPC: setting a baseline for accuracy of COVID-19 AI diagnosis
Iacopo Colonnelli, Barbara Cantalupo, Concetto Spampinato +2
HPC is an enabling platform for AI. The introduction of AI workloads in the HPC applications basket has non-trivial consequences both on the way of designing AI applications and on…
Pushing the boundaries of parallel Deep Learning -- A practical approach
Paolo Viviani, Maurizio Drocco, Marco Aldinucci
This work aims to assess the state of the art of data parallel deep neural network training, trying to identify potential research tracks to be exploited for performance improvemen…
OCCAM: a flexible, multi-purpose and extendable HPC cluster
Marco Aldinucci, Stefano Bagnasco, Stefano Lusso +3
The Open Computing Cluster for Advanced data Manipulation (OCCAM) is a multi-purpose flexible HPC cluster designed and operated by a collaboration between the University of Torino…
A Formal Semantics for Data Analytics Pipelines
Maurizio Drocco, Claudia Misale, Guy Tremblay +1
In this report, we present a new programming model based on Pipelines and Operators, which are the building blocks of programs written in PiCo, a DSL for Data Analytics Pipelines.…
A Comparison of Big Data Frameworks on a Layered Dataflow Model
Claudia Misale, Maurizio Drocco, Marco Aldinucci +1
In the world of Big Data analytics, there is a series of tools aiming at simplifying programming applications to be executed on clusters. Although each tool claims to provide bette…
FastFlow: Efficient Parallel Streaming Applications on Multi-core
Marco Aldinucci, Massimo Torquati, Massimiliano Meneghin
Shared memory multiprocessors come back to popularity thanks to rapid spreading of commodity multi-core architectures. As ever, shared memory programs are fairly easy to write and…