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D'Anca, Alessandro
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D'Anca, Alessandro
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- PublicationOpen AccessEnabling Dynamic and Intelligent Workflows for HPC, Data Analytics, and AI Convergence(2022-04-20)
; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ;; ; ; ;; ; ; ; ; ; ;; ; ; ;; ;; ;The evolution of High-Performance Computing (HPC) platforms enables the design and execution of progressively larger and more complex workflow applications in these systems. The complexity comes not only from the number of elements that compose the workflows but also from the type of computations they perform. While traditional HPC workflows target simulations and modelling of physical phenomena, current needs require in addition data analytics (DA) and artificial intelligence (AI) tasks. However, the development of these workflows is hampered by the lack of proper programming models and environments that support the integration of HPC, DA, and AI, as well as the lack of tools to easily deploy and execute the workflows in HPC systems. To progress in this direction, this paper presents use cases where complex workflows are required and investigates the main issues to be addressed for the HPC/DA/AI convergence. Based on this study, the paper identifies the challenges of a new workflow platform to manage complex workflows. Finally, it proposes a development approach for such a workflow platform addressing these challenges in two directions: first, by defining a software stack that provides the functionalities to manage these complex workflows; and second, by proposing the HPC Workflow as a Service (HPCWaaS) paradigm, which leverages the software stack to facilitate the reusability of complex workflows in federated HPC infrastructures. Proposals presented in this work are subject to study and development as part of the EuroHPC eFlows4HPC project.152 96 - PublicationOpen AccessA procedure to import seismological data into the Ophidia big data analytics framework(2019-09-18)
; ; ; ; ; ; ; The term Big Data identifies large data sets that cannot be managed with traditional processing software. Volume, Variety and Velocity are properties used to classify Big Data. Velocity, the most relevant property, refers to high speed of data acquisition and processing. Management of Big Data, and particularly Velocity, has become an open issue for scientific communities. The EC project INDIGO-Datacloud was conceived to tackle some of the Big Data challenges that scientific communities are facing. In the context of INDIGO-Datacloud we defined a case study in which one of the goals was the improvement of processing performance on seismological data. Starting with a practical case study, we identified, as possible software solution, Ophidia, a big data analytics framework. The first step to exploit the framework functionalities required the import of the seismological data in Ophidia. For this purpose, we designed and implemented a software module that enables the import of seismological data in SAC format. As a second step, we tested the capabilities of Ophidia through a performance test to evaluate the framework potential.204 61 - PublicationOpen AccessBig Data Analytics on Large-Scale Scientific Datasets in the INDIGO-DataCloud Project(2017-05-15)
; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ;; ; ; ; ;In the context of the EU H2020 INDIGO-DataCloud project several use case on large scale scientfic data analysis regarding different research communities have been implemented. All of them require the availability of large amount of data related to either output of imulations or observed data from sensors and need scientic (big) data solutions to run data analysis experiments. More specically,the paper presents the case studies related to the following research communities: (i) the European Multidisciplinary Seaoor and water column Observatory (INGV-EMSO), (ii) the Large Binocular Tele-scope, (iii) LifeWatch, and (iv) the European Network for Earth System Modelling (ENES).119 172 - PublicationOpen AccessAn EMSO data case study within the INDIGO-DC project(2017-04-25)
; ; ; ; ; ; ; ; ; ; ; ; ; ;; We present our experience based on a case study within the INDIGO-DataCloud (INtegrating Distributed data In-frastructures for Global ExplOitation) project (www.indigo-datacloud.eu). The aim of INDIGO-DC is to develop a data and computing platform targeting scientific communities. Our case study is an example of activities performed by INGV using data from seafloor observatories that are nodes of the infrastructure EMSO (European Multidisciplinary Seafloor and water column Observatory)-ERIC (www.emso-eu.org). EMSO is composed of several deep-seafloor and water column observatories, deployed at key sites in the European waters, thus forming a widely distributed pan-European infrastructure. In our case study we consider data collected by the NEMO-SN1 observatory, one of the EMSO nodes used for geohazard monitoring, located in the Western Ionian Sea in proximity of Etna volcano. Starting from the case study, through an agile approach, we defined some requirements for INDIGO developers, and tested some of the proposed INDIGO solutions that are of interest for our research community. Given that EMSO is a distributed infrastructure, we are interested in INDIGO solutions that allow access to distributed data storage. Access should be both user-oriented and machine-oriented, and with the use of a common identity and access system. For this purpose, we have been testing: - ONEDATA (https://onedata.org), as global data management system. - INDIGO-IAM as Identity and Access Management system. Another aspect we are interested in is the efficient data processing, and we have focused on two types of INDIGO products: - Ophidia (http://ophidia.cmcc.it), a big data analytics framework for eScience for the analysis of multidimensional data. - A collection of INDIGO Services to run processes for scientific computing through the INDIGO Orchestra175 325 - PublicationOpen AccessSeaConditions: a web and mobile service for safer professional and recreational activities in the Mediterranean Sea(2017-04-13)
; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ;; ; ; ; ; ;Reliable and timely information on the environmental conditions at sea is key to the safety of professional and recreational users as well as to the optimal execution of their activities. The possibility of users obtaining environmental information in due time and with adequate accuracy in the marine and coastal environment is defined as sea situational awareness (SSA). Without adequate information on the environmental meteorological and oceanographic conditions, users have a limited capacity to respond, which has led to loss of lives and to large environmental disasters with enormous consequent damage to the economy, society and ecosystems. Within the framework of the TESSA project, new SSA services for the Mediterranean Sea have been developed. In this paper we present SeaConditions, which is a web and mobile application for the provision of meteorological and oceanographic observation and forecasting products. Model forecasts and satellite products from operational services, such as ECMWF and CMEMS, can be visualized in SeaConditions. In addition, layers of information related to bathymetry, sea level and ocean-colour data (chl a and water transparency) are displayed. Ocean forecasts at high spatial resolutions are included in the version of SeaConditions presented here. SeaConditions provides a user-friendly experience with a fluid zoom capability, facilitating the appropriate display of data with different levels of detail. SeaConditions is a single point of access to interactive maps from different geophysical fields, providing high-quality information based on advanced oceanographic models. The SeaConditions services are available through both web and mobile applications. The web application is available at www.sea-conditions.com and is accessible and compatible with present-day browsers. Interoperability with GIS software is implemented. User feedback has been collected and taken into account in order to improve the service. The SeaConditions iOS and Android apps have been downloaded by more than 105 000 users to date (May 2016), and more than 100 000 users have visited the web version.122 55