Diana Di Luccio

WaComM: A Parallel Water Quality Community Model for Pollutant Transport and Dispersion Operational Predictions

Accurate prediction of trends in marine pollution is strategic, given the negative effects of low water quality on human marine activities. We describe here the computational and functional performance evaluation of a decision making tool that we developed in the context of an operational workflow for food quality forecast and assessment. Our Water Community Model (WaComM) uses a particle-based Lagrangian approach relying on tridimensional marine dynamics field produced by coupled Eulerian atmosphere and ocean models. WaComM has been developed matching the hierarchical parallelization design requirements and tested in Intel X86_64 and IBM P8 multi core environments and integrated in FACE-IT Galaxy workflow. The predicted pollutant concentration and the amount of pollutants accumulated in the sampled mussels are compared in search of coherent trends to prove the correct model behaviour. In the case study shown in this paper, the predicted Lagrangian tracers, acting as pollutant concentration surrogates, tend to spread rapidly and undergo rapid dilution as expected depending on dominant water column integrated currents.

Accelerating Linux and Android applications on low-power devices through remote GPGPU offloading

Low‐power devices are usually highly constrained in terms of CPU computing power, memory, and GPGPU resources for real‐time applications to run. In this paper, we describe RAPID, a complete framework suite for computation offloading to help low‐powered devices overcome these limitations. RAPID supports CPU and GPGPU computation offloading on Linux and Android devices. Moreover, the framework implements lightweight secure data transmission of the offloading operations. We present the architecture of the framework, showing the integration of the CPU and GPGPU offloading modules. We show by extensive experiments that the overhead introduced by the security layer is negligible. We present the first benchmark results showing that Java/Android GPGPU code offloading is possible. Finally, we show the adoption of the GPGPU offloading into BioSurveillance, a commercial real‐time face recognition application. The results show that, thanks to RAPID, BioSurveillance is being successfully adapted to run on low‐power devices. The proposed framework is highly modular and exposes a rich application programming interface to developers, making it highly versatile while hiding the complexity of the underlying networking layer.

Numerical and Implementation Issues in Food Quality Modeling for Human Diseases Prevention.

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 decisions 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.

Using GPGPU Accelerated Interpolation Algorithms for Marine Bathymetry Processing with On-Premises and Cloud Based Computational Resources

Data crowdsourcing is one of most remarkable results of pervasive and internet connected low-power devices making diverse and different “things” as a world wide distributed system. This paper is focused on a vertical application of GPGPU virtualization software exploitation targeted on high performance geographical data interpolation. We present an innovative implementation of the Inverse Distance Weight (IDW) interpolation algorithm leveraging on CUDA GPGPUs. We perform tests in both physical and virtualized environments in order to demonstrate the potential scalability in production. We present an use case related to high resolution bathymetry interpolation in a crowdsource data context.

Some remarks about a community open source Lagrangian pollutant transport and dispersion model

Abstract Nowadays fishes and mussels farming is very important, from an economical point of view, for the local social background of the Bay of Naples. Hence, the accurate forecast of marine pollution becomes crucial to have reliable evaluation of its adverse effects on coastal inhabitants’ health. The use of connected smart devices for monitoring the sea water pollution is getting harder because of the saline environment, the network availability and the maintain and calibration costs2. To this purpose, we designed and implemented WaComM (Water Community Model), a community open source model for sea pollutants transport and dispersion. WaComM is a model component of a scientific workflow which allows to perform, on a dedicated computational infrastructure, numerical simulations providing spatial and temporal high-resolution predictions of weather and marine conditions of the Bay of Naples leveraging on the cloud based31 FACE-IT workflow engine27. In this paper we present some remarks about the development of WaComM, using hierarchical parallelism which implies distributed memory, shared memory and GPGPUs. Some numerical details are also discussed.

High resolution remote sensing data for environmental modelling: Some case studies

Some Marine Spatial Planning (MSP) case studies have been investigated in order to evidence the importance of highresolution Synthetic Aperture Radar(SAR) wind data in semi-enclosed coastal seas. The MSP technique consisted in the optimum location of mussel aquaculture farms, on the basis of a Sustainability Index (SI) constructed with both physical and biological environmental parameters. In the Kattegat, we investigated with SI the location sites already occupied by offshore wind farms, while in the Southern Tyrrhenian Sea we verified the optimum location of the existing offshore mussel farms. The MSP results showed in both cases a significant improvement due to the increased resolution of remote sensing data and of the oceanographic models used for the simulations.

Performance, Resilience, and Security in Moving Data from the Fog to the Cloud: The DYNAMO Transfer Framework Approach

The data crowdsourcing paradigm applied in coastal and marine monitoring and management has been developed only recently due to the challenges of the marine environment. The pervasive internet of things technology is contributing to increase the number of connected instrumented devices available for data crowd-sourcing. A main issue in the fog/edge/cloud paradigm is that collected data need to be moved from tiny low power devices to cloud resources in order to be processed. This paper is about the DYNAMO data transfer framework enabling the data transfer feature in a internet of floating things scenario. The proposed framework is our solution to mitigate the effects of extreme and delay tolerant environments.

Marine Spatial Planning Using High-Resolution Synthetic Aperture Radar Measurements

In this paper, we highlight the importance of high-resolution wind data on the application of multicriteria evaluation technique to colocate offshore wind farms and open-water mussel cultivations. An index of colocation sustainability (SI), based on an environmental information, is constructed using remote sensing data and taking into account both physical constraints (i.e., water depth and wind speed) and environmental data (i.e., chlorophyll-a, sea surface temperature anomaly, and particulate organic carbon). To verify the proposed methodology, five showcases are presented, where SI is evaluated considering potential installation sites in Kattegat, Denmark, using both low-resolution (LR) wind reanalysis maps related to the Modern Era Retrospective-Analysis for Research and Application data set and fine-resolution wind maps obtained by processing synthetic aperture radar (SAR) data. Experimental results show that the availability of a reliable fine-resolution wind information is of great importance in coastal areas where the presence of the land and the isles limits the use of LR wind data.

Marine bathymetry processing through GPGPU virtualization in high performance cloud computing: Marine bathymetry processing through GPGPU virtualization

Fast technology development has influenced the widespread use of low‐power devices in different scientific, environmental, and everyday life areas, giving birth to the Internet of Things. In this paper, we focus on the context of marine studies, addressing the problem of marine bathymetry data processing and analysis via pervasive and Internet‐connected sensors and low‐power distributed devices. Pervasive and Internet‐connected low‐power devices (as the components involved in the sensing and processing actions) made diverse and different “things” as a worldwide‐distributed system. Given the high complexity of the algorithms involved in these studies, which usually involve general‐purpose graphic processing unit (GPGPU) computation, it is impossible for the limited devices to perform the required calculations. To overcome these limitations, in this paper, we propose and implement a vertical application of GVirtuS, the open‐source GPGPU virtualization and remoting service, for achieving high performance geographical data interpolation in a high performance cloud computing scenario. We present an innovative implementation by comparing, in terms of performance and accuracy, the inverse distance weighting and kriging interpolation methods in their parallel implementations leveraging on CUDA‐enabled GPGPUs. We present a real‐world use case related to high‐resolution bathymetry interpolation in a crowdsource data context in the Bay of Pozzuoli, Italy.