Raffaele Montella

RESEARCH, TEACHING, or OTHER INTERESTS

Computer Science, Computer Science Applications

Scopus Publications

A high-performance, parallel, and hierarchically distributed model for coastal run-up events simulation and forecasting
Diana Di Luccio, Ciro Giuseppe De Vita, Aniello Florio, Gennaro Mellone, Catherine Alessandra Torres Charles, Guido Benassai, and Raffaele Montella
Springer Science and Business Media LLC
AbstractThe request for quickly available forecasts of intense weather and marine events impacting coastal areas is gradually increasing. High-performance computing (HPC) and artificial intelligence techniques are crucial in this application. Risk mitigation and coastal management must design scientific workflow appropriately and maintain them continuously updated and operational. Climate change accelerating increase trend of the past decades impacted on sea-level rise, together with broader factors such as geostatic effects and subsidence, reducing the effectiveness of coastal defenses. Due to this, the support tools, such as Early Warning Systems, have become increasingly more valuable because they can process data promptly and provide valuable indications for mitigation proposals. We developed the Shoreline Alert Model (SAM), an operational Python tool that produces simulation scenarios, ‘what-if’ assumptions, and coastal flooding forecasts to fill this gap in our study area. SAM aims to provide decision-makers, scientists, and engineers with new tools to help forecast significant weather-marine events and support related management or emergency responses. SAM aims to fill the gap between the wind-driven wave models, which produce simulations and forecasts of waves of significant height, period, and direction in deep or mid-water, and the run-up local models, which exstimulate marine ingression in the event of intense weather phenomena. It employs a parallelization scheme that allows users to run it on heterogeneous parallel architectures. It produced results approximately 24 times faster than the baseline when using shared memory with distributed memory, processing roughly 20,000 coastal cross-shore profiles along the coastline of the Campania region (Italy). Increasing the performance of this model and, at the same time, honoring the need for relatively modest HPC resources will enable the local manager and policymakers to enforce fast and effective responses to intense weather phenomena.


Leveraging Large Language Models to Support Authoring Gamified Programming Exercises †
Raffaele Montella, Ciro Giuseppe De Vita, Gennaro Mellone, Tullio Ciricillo, Dario Caramiello, Diana Di Luccio, Sokol Kosta, Robertas Damaševičius, Rytis Maskeliūnas, Ricardo Queirós,et al.
MDPI AG
Skilled programmers are in high demand, and a critical obstacle to satisfying this demand is the difficulty of acquiring programming skills. This issue can be addressed with automated assessment, which gives fast feedback to students trying to code, and gamification, which motivates them to intensify their learning efforts. Although some collections of gamified programming exercises are available, producing new ones is very demanding. This paper presents GAMAI, an AI-powered exercise gamifier, enriching the Framework for Gamified Programming Education (FGPE) ecosystem. Leveraging large language models, GAMAI enables teachers to effortlessly apply storytelling to describe a gamified scenario, as GAMAI decorates natural language text with the sentences needed by OpenAI APIs to contextualize the prompt. Once a gamified scenario has been generated, GAMAI automatically produces exercise files in a FGPE-compatible format. According to the presented evaluation results, most gamified exercises generated with AI support were ready to be used, with no or minimum human effort, and were positively assessed by students. The usability of the software was also assessed as high by the users. Our research paves the way for a more efficient and interactive approach to programming education, leveraging the capabilities of advanced language models in conjunction with gamification principles.


Striking Trade-off between High Performance and Energy Efficiency in an Edge Computing Application for Detecting Floating Plastic Debris
Marco Lapegna, Giuliano Laccetti, Raffaele Montella, and Diego Romano
ACM



Special Issue on the pervasive nature of HPC (PN-HPC)
Marco Lapegna, Valeria Mele, Raffaele Montella, and Lukasz Szustak
Wiley
SummaryThis special issue on the Pervasive Nature of HPC (PN‐HPC) collects an extension of the most valuable works presented at the sixth Workshop on Models, Algorithms and Methodologies for Hybrid Parallelism in New HPC Systems (MAMHYP‐22), held in Gdansk (Poland) in September 2022, jointly with the 14th conference on Parallel Processing and Applied Mathematics (PPAM‐22). New original papers related to the workshop themes are also included. The final aim is to provide a glimpse of the current state of knowledge related to the development of efficient methodologies and algorithms for HPC systems with multiple forms of parallelism.


Preface




A GIS-big data model for improving the coverage and analysis processes of territory observation, and integrating ground-based observations with retrospective meteorological data
J. Armando Barron-Lugo, Ivan Lopez-Arevalo, J.L. Gonzalez-Compean, M. Susana Alvarado-Barrientos, Jesus Carretero, Victor J. Sosa-Sosa, and Raffaele Montella
Elsevier BV



Re-assessing the Usability of FGPE Programming Learning Environment with SUS and UMUX




Developing a GIS Framework for Effective Weather Routing in the Tyrrhenian Sea
Emanuele Alcaras, Giorgio Budillon, Yuri Cotroneo, Diana Di Luccio, Raffaele Montella, and Claudio Parente
IEEE
Weather routing is a navigation technique that uses real-time meteorological and forecast data to chart the safest and most efficient course for maritime vessels, minimizing the risks posed by adverse weather and sea conditions. It involves the integration of spatial analysis techniques to optimize the route. Spatial analysis operations, like processing and visualizing geospatial data, can be carried out within a Geographic Information System (GIS). In this study, a GIS framework for weather routing is developed, utilizing datasets for the Tyrrhenian Sea provided by the Weather and Sea-State Monitoring and Observing System at Parthenope University of Naples, including wave direction and height. Methodologically, the study involves the creation of thematic maps using raster calculators to classify and visualize the aforementioned meteorological and marine variables. Within this context, a navigational framework is developed in QGIS, integrating raster data as well as vector data to provide navigators with advanced decision-making support. Preliminary findings suggest promising enhancements in navigation capabilities, indicating significant potential for improving route efficiency and safety through the application of advanced GIS-based weather routing techniques.


Foreword INSTIL 2024 workshop
IEEE



Safeguarding the Marine and Coastal Environment with Artificial Intelligence




GAMAI, an AI-Powered Programming Exercise Gamifier Tool
Raffaele Montella, Ciro Giuseppe De Vita, Gennaro Mellone, Tullio Ciricillo, Dario Caramiello, Diana Di Luccio, Sokol Kosta, Robertas Damasevicius, Rytis Maskeliunas, Ricardo Queiros,et al.
Springer Nature Switzerland



Message from the General Chair
IEEE



Improving Real-Time Data Streams Performance on Autonomous Surface Vehicles using DataX
Gennaro Mellone, Ciro Giuseppe de Vita, Giuseppe Coviello, Pietro Patrizio Ciro Aucelli, Angelo Ciaramella, and Raffaele Montella
IEEE
In the evolving Artificial Intelligence (AI) era, the need for real-time algorithm processing in marine edge en-vironments has become a crucial challenge. Data acquisition, analysis, and processing in complex marine situations require sophisticated and highly efficient platforms. This study optimizes real-time operations on a containerized distributed processing platform designed for Autonomous Surface Vehicles (ASV) to help safeguard the marine environment. The primary objective is to improve the efficiency and speed of data processing by adopting a microservice management system called DataX. DataX leverages containerization to break down operations into modular units, and resource coordination is based on Kubernetes. This combination of technologies enables more efficient resource management and real-time operations optimization, contributing significantly to the success of marine missions. The platform was developed to address the unique challenges of managing data and running advanced algorithms in a marine context, which often involves limited connectivity, high latencies, and energy restrictions. Finally, as a proof of concept to justify this platform's evolution, experiments were carried out using a cluster of single-board computers equipped with GPUs, running an AI-based marine litter detection application and demonstrating the tangible benefits of this solution and its suitability for the needs of maritime missions.


Adaptive HPC Input/Output Systems
Jesus Carretero, Javier Garcia-Blas, André Brinkmann, Marc Vef, Jean-Baptiste Besnard, Massimo Torquati, Yi Ju, and Raffaele Montella
Springer Nature Switzerland



PuzzleMesh: A Puzzle Model to Build Mesh of Agnostic Services for Edge-Fog-Cloud
Dante D. Sanchez-Gallegos, J. L. Gonzalez-Compean, Jesus Carretero, Heidy M. Marin-Castro, Andrei Tchernykh, and Raffaele Montella
Institute of Electrical and Electronics Engineers (IEEE)
This paper presents the design, development, and evaluation of PuzzleMesh, an agnostic service mesh composition model to process large volumes of data in edge-fog-cloud environments. This model is based on a puzzle metaphor where pieces, puzzles, and metapuzzles represent self-contained autonomous and reusable software artifacts encapsulated into containers and published as microservices. A piece represents the integration of apps with I/O interfaces (loops/sockets), parallel processing, and management software. A puzzle represents a processing structure (e.g., workflows) built coupling pieces through loops and sockets. Puzzles integrate structures with a microservice architecture, implicit continuous dataflows, and transparent data exchange management software. A metapuzzle represents a recursive assemble of puzzles. A mesh represents a pool of pieces, puzzles, and metapuzzles available for designers to choose artifacts to build services. A prototype developed using PuzzleMesh model was evaluated through case studies about the automatic construction of processing services for the acquisition, pre-processing, manufacturing, preserving, and visualizing of satellite imagery. A qualitative comparison revealed that PuzzleMesh provides a flexible way to build reusable and portable services and to improve the usability of the services. The case study also revealed that PuzzleMesh yielded better performance results than other state-of-the-art tools.


AI-based Monitoring of Coastal and Marine Environments




Message from the Organizing Committee Chairs: PDP 2023
Raffaele Montella, Angelo Ciaramella, Marco Lapegna, Marco Danelutto, and Dora Blanco Heras
IEEE



Parallel and hierarchically-distributed Shoreline Alert Model (SAM)
Ciro Giuseppe de Vita, Gennaro Mellone, Aniello Florio, Catherine Alessandra Torres Charles, Diana Di Luccio, Marco Lapegna, Guido Benassai, Giorgio Budillon, and Raffaele Montella
IEEE
In this paper, the Shoreline Alert Model (SAM) is presented as a component of a computation platform based on workflows dedicated to extreme weather/marine event simulation. The model aims to mitigate the effects of global change by providing decision-makers, scientists, and engineers with a novel, next-generation tool set for facing extreme weather events and implementing related management or emergency responses. SAM uses a parallelization schema, allowing users to run it on heterogeneous parallel architectures. As a result, SAM produces approximately 24 times faster results than the baseline when using shared memory with distributed memory and dealing with about 20,000 transects along the Campania coastline. The system is based on the algorithms of the open-source numerical models WRF (Weather Research and Forecasting) and WW3 (Wave-watch III) implemented with refraction and shoaling routines together with run-up equations to form the modeling chain used for coastal flooding assessment.


Message from the General Chairs: PDP 2023
Raffaele Montella, Angelo Ciaramella, Marco Lapegna, Marco Danelutto, and Dora Blanco Heras
IEEE



A highly scalable high-performance Lagrangian transport and diffusion model for marine pollutants assessment
Raffaele Montella, Diana Di Luccio, Ciro Giuseppe de Vita, Gennaro Mellone, Marco Lapegna, Gloria Ortega, Livia Marcellino, Enrico Zambianchi, and Giulio Giunta
IEEE
While using High-Performance Computing (HPC) for precise and accurate air quality forecasts is a common issue, similar services devoted to marine pollution in coastal areas remain challenging. This paper presents Water quality Community Model Plus Plus (WaComM++) leveraging a parallelization schema enabling the users to run it on heterogeneous parallel architectures. We evaluated the proposed model under several execution approaches using a real-world application for pollutants forecast in the Gulf of Napoli (Campania, Italy). As a result, WaComM++ has produced results 657K times faster than the sequential run (taking into account the Particles' Outer Cycle and not considering the particle domain distribution) when using distributed and shared memory with multi-GPUs dealing with about 25 million particles.


A containerized distributed processing platform for autonomous surface vehicles: preliminary results for marine litter detection
Gennaro Mellone, Ciro Giuseppe De Vita, Dante Domizzi Sánchez-Gallegos, Diana Di Luccio, Gaia Mattei, Francesco Peluso, Pietro Patrizio Ciro Aucelli, Angelo Ciaramella, and Raffaele Montella
IEEE
Autonomous Surface Vehicles and their management represent one of the significant challenges in coastal and offshore surveying. Although the development of this kind of data acquisition device has skyrocketed in the last few years, line guides and technological solutions still need to come. On the other hand, this kind of robotic vessel's true potential has yet to be explored. This paper presents ArgonautAI, a containerized distributed processing platform for autonomous surface vehicles. The proposed ArgonautAI architecture leverage a cluster of single-board computers with diverse and different characteristics (computing power, CUDA GPUs, FPGAs, GPIOs, PWMs, specialized I/O) orchestrated using Kubernetes and a customized programming interface. Furthermore, the proposed solution introduces two different types of containers: 1) the platform containers hosting the software life support for the platform and 2) the mission containers defined to support the survey mission-specific scopes. The firsts manage the vehicle's instruments (e.g. position, attitude, environment, depth), the data storage, the vessel-to-shore communication, and so on; the latter host mission-specific software components. Finally, as proof of concept of the proposed platform, we present an AI-based marine litter detection application using a hierarchical computer vision approach on heterogenic onboard computing resources.


Malleability Techniques for HPC Systems
Jesus Carretero, David Exposito, Alberto Cascajo, and Raffaele Montella
Springer International Publishing



Towards a GPU parallel software for environmental data fitting
Pasquale De Luca, Diana Di Luccio, Ardelio Galletti, Giulio Giunta, Livia Marcellino, and Raffaele Montella
ACM
In this paper we are interested in fitting data arising from environmental problems. To this aim, several procedures and methods are available in literature, and all of them involve high computational complexity when real dataset are considered. In this work, we propose a novel GPU parallel algorithm, specifically designed for fitting environmental and bathymetric data, which is based on the Kriging method. The implementation exploits the capabilities of advanced parallel computing architectures for efficiently solving large size problems. We obtain remarkable gain in terms of execution times and memory usage, as confirmed by experimental tests, by combining suitable parallel numerical libraries and ad hoc parallel kernels in CUDA environment.


AIQUAM: Artificial Intelligence-based water QUAlity Model
Ciro Giuseppe De Vita, Gennaro Mellone, Diana Di Luccio, Sokol Kosta, Angelo Ciaramella, and Raffaele Montella
IEEE
Monitoring the impact of the pollutants on the sea is a crucial issue for coastal human activities, such as aquaculture. However, leveraging a continuous microbiological laboratory analysis is unfeasible for costs and practical reasons. Here we present a novel methodology finalized to predict water quality as categorized indexes leveraging an integrated approach between computational components and artificial intelligence techniques. As a paradigm demonstrator, we couple WaComM++ with AIQUAM. The use case presented is an application of AIQUAM in the Bay of Naples (Campania Region, Italy) for predicting bacteria contaminants in mussel farms. The results are encouraging as the model reached a correct prediction rate of 93%.


Artificial Intelligence for mussels farm quality assessment and prediction
Ciro Giuseppe de Vita, Gennaro Mellone, Francesca Barchiesi, Diana Di Luccio, Angelo Ciaramella, and Raffaele Montella
IEEE
Mussel farming is one of the production sectors influenced by the pollutants in seawater, both of chemical and biological origin. Monitoring the impact of the pollutants on mussel farms is a crucial issue in coastal management. A computational approach to mitigate the coast connected to the in-situ monitoring and give the possibility to predict the water quality evolution concerning the coastal hydrodynamics and the known pollution source activities could be a convenient solution. However, although a coupled atmosphere-ocean numerical models workflow is a solution already made operational in diverse and different contexts, the prediction of bacteria contamination in farmed mussels, given the forecast of contaminant concentration, is still an open issue. In this paper, we introduce a novel methodology devoted to predicting the level of contamination given the pollutant concentration from Lagrangian models for transport and diffusion. We present the Artificial Intelligencebased water QUAlity Model (AIQUAM). AIQUAM adopts a computational approach based on High-Performance computer facilities and artificial intelligence to define the dynamics of pollutants in the proximity of mussel farms. We motivate the design and implementation of decision-making tools to support the local authorities in the management activities. Within the framework presented here, the mussel is modeled by the mussel-pollutant interaction time and the bio-accumulation phenomena in filtering organisms (mussels), which can result in hygienic-sanitary emergence deriving from the sale and consumption of potentially polluted products.

RECENT SCHOLAR PUBLICATIONS

Developing a GIS Framework for Effective Weather Routing in the Tyrrhenian Sea
E Alcaras, G Budillon, Y Cotroneo, D Di Luccio, R Montella, C Parente
2024 IEEE International Workshop on Metrology for the Sea; Learning to 2024



A high-performance, parallel, and hierarchically distributed model for coastal run-up events simulation and forecasting
D Di Luccio, CG De Vita, A Florio, G Mellone, CA Torres Charles, ...
The Journal of Supercomputing 80 (15), 22748-22769 2024



Weather and sea state observations and modeling in the Gulf of Naples in support of sustainable maritime mobility
Y Cotroneo, P de Ruggiero, L Gifuni, R Montella, D Di Luccio, CG De Vita, ...
EMS2024 2024



GAMAI, an AI-Powered Programming Exercise Gamifier Tool
R Montella, C Giuseppe De Vita, G Mellone, T Ciricillo, D Caramiello, ...
International Conference on Artificial Intelligence in Education, 485-493 2024



Striking Trade-off Between High Performance and Energy Efficiency in an Edge Computing Application for Detecting Floating Plastic Debris
M Lapegna, G Laccetti, R Montella, D Romano
Proceedings of the 4th Workshop on Flexible Resource and Application 2024



Special Issue on the pervasive nature of HPC (PN‐HPC)
M Lapegna, V Mele, R Montella, L Szustak
Concurrency and Computation: Practice and Experience 36 (10), e8003 2024



A GIS-big data model for improving the coverage and analysis processes of territory observation, and integrating ground-based observations with retrospective meteorological data
JA Barron-Lugo, I Lopez-Arevalo, JL Gonzalez-Compean, ...
International Journal of Applied Earth Observation and Geoinformation 128 2024



Improving real-time data streams performance on autonomous surface vehicles using datax
G Mellone, CG De Vita, G Coviello, PPC Aucelli, A Ciaramella, R Montella
2024 32nd Euromicro International Conference on Parallel, Distributed and 2024



Safeguarding the Marine and Coastal Environment with Artificial Intelligence
P Barra, F Camastra, A Ciaramella, CG De Vita, E Di Nardo, R Montella, ...
CEUR WORKSHOP PROCEEDINGS 3762, 510-515 2024



Leveraging large language models to support authoring gamified programming exercises
R Montella, CG De Vita, G Mellone, T Ciricillo, D Caramiello, D Di Luccio, ...
Applied sciences. 14 (18), 1-15 2024



Adaptive HPC Input/Output Systems
J Carretero, J Garcia-Blas, A Brinkmann, M Vef, JB Besnard, M Torquati, ...
European Conference on Parallel Processing, 199-202 2023



A shoreline alert model for coastal early warning system in the gulf of naples (italy)
A Florio, D Di Luccio, CG De Vita, G Mellone, G Benassai, G Budillon, ...
EGU General Assembly Conference Abstracts, EGU-6673 2023



Message from the Organizing Committee Chairs: PDP 2023
R Montella, A Ciaramella, M Lapegna, M Danelutto, DB Heras
2023 31st Euromicro International Conference on Parallel, Distributed and 2023



A highly scalable high-performance Lagrangian transport and diffusion model for marine pollutants assessment
R Montella, D Di Luccio, CG De Vita, G Mellone, M Lapegna, G Ortega, ...
2023 31st Euromicro International Conference on Parallel, Distributed and 2023



Message from the General Chairs: PDP 2023
R Montella, A Ciaramella, M Lapegna, M Danelutto, DB Heras
2023 31st Euromicro International Conference on Parallel, Distributed and 2023



A containerized distributed processing platform for autonomous surface vehicles: preliminary results for marine litter detection
G Mellone, CG De Vita, DD Snchez-Gallegos, D Di Luccio, G Mattei, ...
2023 31st Euromicro International Conference on Parallel, Distributed and 2023



Parallel and hierarchically-distributed shoreline alert model (sam)
CG De Vita, G Mellone, A Florio, CAT Charles, D Di Luccio, M Lapegna, ...
2023 31st Euromicro International Conference on Parallel, Distributed and 2023



AI-based Monitoring of Coastal and Marine Environments.
F Camastra, A Ciaramella, E Di Nardo, A Ferone, A Maratea, R Montella, ...
Ital-IA, 575-579 2023



Parallel and hierarchically-distributed Shoreline Alert Model (SAM)
G Mellone, CG DE VITA, A Florio, D DI LUCCIO, L Marco, G Benassai, ...
2023



Environmental investigations in the Gulf of Pozzuoli (Naples) in relation to PAH contamination
M Esposito, M Della Rotonda, C Sbarra, M Stefanelli, MG Aquila, ...
9th International Symposium MONITORING OF MEDITERRANEAN COASTAL AREAS 2023

MOST CITED SCHOLAR PUBLICATIONS

A GPGPU transparent virtualization component for high performance computing clouds
G Giunta, R Montella, G Agrillo, G Coviello
Euro-Par 2010-Parallel Processing, 379-391 2010
Citations: 327



Wave run-up prediction and observation in a micro-tidal beach
D Di Luccio, G Benassai, G Budillon, L Mucerino, R Montella, ...
Natural Hazards and Earth System Sciences 18 (11), 2841-2857 2018
Citations: 57



Coastal marine data crowdsourcing using the Internet of Floating Things: Improving the results of a water quality model
D Di Luccio, A Riccio, A Galletti, G Laccetti, M Lapegna, L Marcellino, ...
IEEE Access 8, 101209-101223 2020
Citations: 50



Rip current evidence by hydrodynamic simulations, bathymetric surveys and UAV observation
G Benassai, P Aucelli, G Budillon, M De Stefano, D Di Luccio, G Di Paola, ...
Natural Hazards and Earth System Sciences 17 (9), 1493-1503 2017
Citations: 48



On the virtualization of CUDA based GPU remoting on ARM and X86 machines in the GVirtuS framework
R Montella, G Giunta, G Laccetti, M Lapegna, C Palmieri, C Ferraro, ...
International Journal of Parallel Programming 45, 1142-1163 2017
Citations: 41



Monitoring and modelling coastal vulnerability and mitigation proposal for an archaeological site (Kaulonia, Southern Italy)
D Di Luccio, G Benassai, G Di Paola, CM Rosskopf, L Mucerino, ...
Sustainability 10 (6), 2017 2018
Citations: 40



FACE‐IT: A science gateway for food security research
R Montella, D Kelly, W Xiong, A Brizius, J Elliott, R Madduri, ...
Concurrency and Computation: Practice and Experience 27 (16), 4423-4436 2015
Citations: 40



Accelerating Linux and Android applications on low‐power devices through remote GPGPU offloading
R Montella, S Kosta, D Oro, J Vera, C Fernndez, C Palmieri, D Di Luccio, ...
Concurrency and Computation: Practice and Experience 29 (24), e4286 2017
Citations: 39



DagOn*: Executing direct acyclic graphs as parallel jobs on anything
R Montella, D Di Luccio, S Kosta
2018 IEEE/ACM Workflows in Support of Large-Scale Science (WORKS), 64-73 2018
Citations: 38



A grid computing based virtual laboratory for environmental simulations
I Ascione, G Giunta, P Mariani, R Montella, A Riccio
Euro-Par 2006 Parallel Processing, 1085-1094 2006
Citations: 38



A fast, secure, reliable, and resilient data transfer framework for pervasive IoT applications
R Montella, M Ruggieri, S Kosta
IEEE INFOCOM 2018-IEEE conference on computer communications workshops 2018
Citations: 37



WaComM: A parallel Water quality Community Model for pollutant transport and dispersion operational predictions
R Montella, D Di Luccio, P Troiano, A Riccio, A Brizius, I Foster
2016 12th International Conference on Signal-Image Technology & Internet 2016
Citations: 37



SOLE: linking research papers with science objects
Q Pham, T Malik, I Foster, R Di Lauro, R Montella
Provenance and Annotation of Data and Processes: 4th International 2012
Citations: 34



pPOM: A nested, scalable, parallel and Fortran 90 implementation of the Princeton Ocean Model
G Giunta, P Mariani, R Montella, A Riccio
Environmental Modelling & Software 22 (1), 117-122 2007
Citations: 34



Shoreline rotation analysis of embayed beaches by means of in situ and remote surveys
D Di Luccio, G Benassai, G Di Paola, L Mucerino, A Buono, CM Rosskopf, ...
Sustainability 11 (3), 725 2019
Citations: 33



Using grid computing based components in on demand environmental data delivery
R Montella, G Giunta, A Riccio
Proceedings of the second workshop on Use of P2P, GRID and agents for the 2007
Citations: 33



SIaaS-sensing instrument as a service using cloud computing to turn physical instrument into ubiquitous service
R Di Lauro, F Lucarelli, R Montella
2012 IEEE 10th international symposium on parallel and distributed 2012
Citations: 32



Virtualizing general purpose GPUs for high performance cloud computing: an application to a fluid simulator
R Di Lauro, F Giannone, L Ambrosio, R Montella
2012 IEEE 10th International Symposium on Parallel and Distributed 2012
Citations: 32



DYNAMO: Distributed leisure yacht-carried sensor-network for atmosphere and marine data crowdsourcing applications
R Montella, S Kosta, I Foster
2018 IEEE International Conference on Cloud Engineering (IC2E), 333-339 2018
Citations: 31



Virtualizing high-end GPGPUs on ARM clusters for the next generation of high performance cloud computing
R Montella, G Giunta, G Laccetti
Cluster computing 17, 139-152 2014
Citations: 31