Authors:
A. Galletti
1
;
R. Montella
2
;
L. Marcellino
1
;
A. Riccio
1
;
D. Di Luccio
1
;
A. Brizius
3
and
I. Foster
3
Affiliations:
1
Parthenope University of Naples “Parthenope”, Italy
;
2
Parthenope University of Naples “Parthenope” and University of Chicago, Italy
;
3
University of Chicago, United States
Keyword(s):
Lagrangian Methods, Numerical Interpolation, Food Quality, Human Diseases, Cloud Computing, High Performance Computing, Scientific Workflow, Smart Devices, Internet of Things, Marine Data Crowdsourcing.
Abstract:
Monitoring nearshore sea water pollution using connected smart devices could be nowadays impracticable due
to the aggressive saline environment, the network availability and the maintain and calibration costs. Accurate
forecast of marine pollution is most needed to evaluate the adverse effects on coastal inhabitants’ health when
fishes and mussels farming economically characterizes the local social background. In an operational context,
numerical simulations are performed routinely on a dedicated computational infrastructure producing space
and temporal high-resolution predictions of weather and marine conditions of the Bay of Naples. In this paper
we present our results in developing a community open source Lagrangian pollutant transport and dispersion
model, leveraging on hierarchical parallelism implying distributed memory, shared memory and GPGPUs.
Some numerical details are also discussed. This system has been used to develop an alarm system to help local
authorities in making d
ecisions regarding the collection of mussels. The model setup and the simulation results
will be improved using FairWind, an under development system dedicated to coastal marine crowdsourced data
gathering and sharing, based on smart devices and Internet of Things afloat.
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