VisIVO Science Gateway: a Collaborative Environment for the Astrophysics Community Eva Sciacca ∗ , Marilena Bandieramonte ∗‡ , Ugo Becciani ∗ , Alessandro Costa ∗ , Mel Krokos † , Piero Massimino ∗ , Catia Petta ‡ , Costantino Pistagna ∗ , Simone Riggi ∗ and Fabio Vitello ∗ ∗ INAF-Osservatorio Astrofisico di Catania, Italy † University of Portsmouth, United Kingdom ‡ Dipartimento di Fisica e Astronomia, Universitá di Catania, Italy Email: eva.sciacca@oact.inaf.it Abstract—VisIVO Science Gateway is a web based, workflow presented in [4], focusing on some complex case studies enabled environment wrapped around a WS-PGRADE/gUSE to support specialized astrophysics communities (see Section portal integrating seamlessly large-scale multi-dimensional as- V) which are managed through a workflow sharing frame- trophysical datasets with applications for processing and visual- work (see Section IV). Our gateway is wrapped around WS- ization based on Distributed Computing Infrastructures (DCIs). PGRADE [5], a highly-flexible interface for the grid User We present the main tools and services supported including an application for mobile access to the gateway. We discuss issues Support Environment2 (gUSE) and provides access to VisIVO in sharing workflows and report our experiences in supporting Server tools [6] (see Section II), thus enabling execution specialised communities. We present a number of workflows de- of complex workflows through a comprehensive collection veloped recently for visualization and numerical simulations and of modules for processing and visualization of astrophysical outline future workflows currently under development. Finally, datasets. we summarise our work on the gateway with pointers to future developments. A number of customized workflows is configured by de- fault to allow local or remote uploading of datasets, datasets Keywords—Science Gateways; Workflow Systems; Collaborative filtering and creation of scientific movies. These workflows Environments; Astrophysics; Large-Scale Datasets; Visualization; are provided with specific user interface portlets to enable DCIs intuitive parameter setting for standard users while hiding the complexity of the underlying system and infrastructures. The I. I NTRODUCTION mobile application employs user accounts from the gateway Visualization can play an important role in the context and offers a handy platform for astrophysical communities to of large-scale multi-dimensional astrophysical datasets, e.g. share results and experiences of analysis and exploration of in understanding, interpreting and verifying their intrinsic their datasets. characteristics [1]. Often a number of data exploration tools For displaying 2D or 3D plots, astrophysicists typically are employed for visual discovery in order to identify regions deploy software packages programs such as Gnuplot, Super- of interest within which to apply computationally expensive Mongo, or scripting languages such as Python, Matlab or algorithms (e.g. see [2]). Such processes typically involve IDL. VisIt3 or ParaView4 offer a combination of 2D and 3D distributed solutions for storage and processing. Recently sci- plotting capabilities, real-time and offline analysis, scripting ence gateways have gained popularity as they allow seamless and graphical control. VisIt has been provided with grid integration of datasets, tools and applications enabled for services for scientific collaborative visualization in UNICORE executing on generic distributed computing infrastructures (or Grids [7]. ParaView has been extended to offer grid services [8] DCIs). and a plugin has been developed to provide interactive remote Science gateways provide services to support searching, visualization for collaborative environments based on video managing and uploading/downloading (thus allowing sharing) streams [9]. of applications and datasets. They enable user communities Nevertheless scientific visualization can be a fairly complex to deploy their applications through common graphical user process involving several steps, e.g. filtering data, choosing a interfaces, thus allowing scientists to focus on the actual ap- representation and desired level of interactivity and customiz- plications instead of learning and managing the required infras- ing the manner in which the data is displayed. None of the tructures. The processes supported by gateways are organized aforementioned tools are provided with a science gateway to as scientific workflows [3] that explicitly specify dependencies interface them with workflow services. Within VisIVO Science among underlying tasks for orchestrating distributed resources Gateway and VisIVO Mobile ready to-use workflows can be appropriately. downloaded, parametrized and executed under a controlled This paper reports on the on-going developments of Vi- sIVO Science Gateway1 and VisIVO Mobile application, first 2 http://www.guse.hu 3 https://wci.llnl.gov/codes/visit 1 http://visivo.oact.inaf.it:8080 4 http://www.paraview.org environment. The visualization and filtering parameters can Fig. 1. VisIVO Server processing pipeline. be chosen interactively and the workflow configuration and submission to DCIs is performed without exposing technical details so that end users can focus on their applications instead of devoting efforts in learning and managing the underlying infrastructures. II. V ISUALIZATION T OOLS VisIVO [6] is an integrated suite of tools and services for effective visual discovery within large-scale astrophysical datasets. VisIVO is realised as: • VisIVO Desktop [10], a stand alone application for interactive visualizations running on standard PCs; • VisIVO Server, a grid-enabled high performance visu- III. V IS IVO S CIENCE G ATEWAY AND V IS IVO M OBILE alization platform, and A PPLICATION • VisIVO Library [11] developed specifically to port The existing VisIVO Web [12] has been integrated within VisIVO Server on gLite middleware5 . the WS-PGRADE/gUSE generic gateway [13] to offer new, easily accessible opportunities not only to scientific users, e.g. Users of each realization can obtain meaningful visual- astrophysical researchers, but also to the wider public, e.g. izations rapidly while preserving full and intuitive control high-school education or innovative citizen science activities. of relevant visualization parameters. This section focuses on This work is supported by the SCI-BUS project8 providing VisIVO Server6 which can be installed on any web server operation and maintenance of the gateway as well as end- with a database repository and contains the following distinct users support for training activities. A special focus of the modules: VisIVO Importer, VisIVO Filters and VisIVO Viewer work has been placed on standardization and quality control (see Figure 1). issues in order to increase the chances of adoption (by other relevant user communities) of the developed technologies and VisIVO Importer converts user-supplied datasets into Vi- methodologies. sIVO Binary Tables (VBTs). A VBT is a highly-efficient data representation realized through a header file containing all A. VisIVO Science Gateway Main Services necessary metadata and a raw data file storing actual data values. VisIVO Importer supports conversion from several The VisIVO Science Gateway is designed as a workflow popular formats such as: ASCII and CSV, VOTables or FITS enabled grid portal that is wrapped around WS-PGRADE Tables without imposing any limits on sizes or dimensionality. providing visualization and data management services to the VisIVO Filters is a collection of data processing modules scientific community by means of an easy-to-use graphical to modify a VBT or to create a new VBT from existing environment for accessing the full functionality of VisIVO VBTs. The filters support a range of operations such as Server. Complex workflows can be created and executed on scalar distribution, mathematical operations or selections of a variety of infrastructures (e.g. clouds, desktop and service regions. VisIVO Viewer is the visualization core component grids or supercomputers) to obtain comprehensive exploration based on the Visualization ToolKit7 . It creates 3D images and analysis of large-scale astrophysical datasets. The gateway from multi-dimensional datasets rendering points, volumes and offers role-based authorization modules and supports secure isosurfaces. Moreover there is support for customized look up login. tables and visualizations using a variety of glyphs, such as Currently a number of main roles are implemented for cubes, spheres or cones. VisIVO Viewer can be also used to access as follows: guests, standard and advanced users and produce images in a given sequence of azimuth, elevation, and administrators [4]. Standard users can upload and manage zooming values that can be externally mounted to produce their datasets through portlets without any knowledge about movies. the (conveniently hidden) underlying grid-infrastructure and To create customized renderings from astrophysical data middleware. By using interactive widgets users can construct tables VisIVO Importer is first utilized to convert user datasets customized renderings, or store data analysis and visualization into VBTs. Then, one or more VisIVO Filters can be applied results for future reference. Their datasets are managed inter- to process these datasets, and finally VisIVO Viewer is invoked nally through a relational database preserving their metadata to display these renderings. Figure 1 illustrates the typical se- and maintaining data consistency. Figure 2 shows the main quence of steps required within the VisIVO Server processing portlets of the Gateway connecting to VisIVO Importer, Filters pipeline. and Viewer services. Both remote and local datasets can be uploaded - i.e. 5 http://glite.cern.ch residing on a remote URL or locally on a user’s PC. For 6 http://sourceforge.net/projects/visivoserver 7 http://www.vtk.org 8 http://www.sci-bus.eu Fig. 2. Main VisIVO Gateway portlets. VisIVO Gateway automatically displays all applicable VisIVO Filter operations allowing input of the relevant parameters. Finally the VisIVO Viewer is employed for image display. A right click on any processed dataset in the Data Management portlet is used in conjunction with the View button to cre- ate user-prescribed VisIVO Viewer views. VisIVO Gateway further allows users to generate scientific movies. These can be useful not only to scientists to present and communicate their research results, but also to museums and science centres to introduce complex scientific concepts to general public audiences. Users can create a Panoramic Movie by moving a camera along a motion path of 360o in azimuth and +/- 90o in elevation within the dataset’s domain. Customized Movies can be produced by intermediate snapshots specified as camera positions/orientations and the gateway generates a movie with a camera path containing these specified positions/orientations. Dynamic Movies can be created by interpolating several steps of a time evolution of a cosmological dataset. The user can browse a cosmological time evolution and choose two or more coherent datasets. The designed workflow will then produce remote files the user must specify URL and optionally a the necessary number of intermediate VBTs by calculating user name and password for authentication. Depending upon particle positions and applying boundary conditions as nec- the size of the datasets under consideration, remote uploads essary. This approach can be very useful, e.g. in revealing could last a long period. To resolve this situation VisIVO galaxy formation or observing large-scale structures such as Gateway allows an off-line mode by means of a workflow galaxy clusters. submission so that users can issue upload commands and then The creation of a movie represents a significant challenge simply close their current session - a follow up e-mail typically for the underlying computational resources as often hundreds gives notification once the uploading operation is completed. or thousands of high quality images must be produced. For this The workflow employed for remote importing is illustrated in reason Parameter Sweep (PS) workflows [14] are employed. Figure 3. It allows generation of significant information for This is particularly relevant to the visualization-oriented work- meta data exploration, e.g. statistics on data values, histogram flows presented in Section V. As the respective communities calculation and plotting or a sample extraction of uploaded typically employ a large number of parameters that have datasets. Such meta data is available through the Properties to be varied within user-defined ranges, several hundreds to portlet and some can be modified by the user (e.g. renaming thousands of workflow executions might be necessary. As an VBTs or related fields). example a panoramic movie is generated with the workflow shown in Figure 4, it generates four movies with different Fig. 3. Remote VisIVO Importer Workflow. camera position paths on the generator port: from 0o to 360o azimuth rotation, from 0o to 90o elevation rotation, from 90o to −90o elevation rotation and from −90o to 0o elevation rotation. The generation of these four movies is executed in parallel and is finally merged through a collector port as shown in Fig. 4. Fig. 4. Panoramic Movie Workflow. B. VisIVO Mobile Application Once the data file is uploaded a sequence of simple The VisIVO Mobile application (see Fig. 5) allows smart- actions is required to rapidly obtain meaningful visualizations. phone devices to exploit VisIVO Gateway functionalities to Typically various VisIVO Filter operations are performed, and access large-scale astrophysical datasets residing on a server repository for analysis and visual discovery. Through interac- C. Implementation Details and Computing Infrastructures tive widgets, customized visualizations (images or movies) can be generated and stored on the remote server. The application The VisIVO Science Gateway is based on the collaborative notifies users when requested visualizations are available for and community oriented application development environment retrieving on their smartphones and allows sharing of data, WS-PGRADE/gUSE. There is full integration in the portal images and movies via e-mail or by exploiting common social framework Liferay which is highly customizable thanks to the networks. adoption of portlet technology defined in the Java Specifica- tion Request 168 and 28611 , and compatible to modern web applications. The implemented portlets are developed with the Fig. 5. VisIVO Mobile screenshots on an iPad device: navigation through the imported datasets and produced images and scientific movies (upper figure); Java Vaadin web Framework12 . This open source framework and dataset remote importing (lower figure). has been employed to implement server side Java Servlet based web applications using the full power and flexibility of Java without taking care of the client side since it compiles the Java source code to JavaScript which can then be run on any browser. The current architecture of VisIVO Science Gateway has a distributed configuration on different machines enhancing the service performances as shown in Figure 6. The front-end services contain WS-PGRADE and Liferay and the back-end services include the gUSE components. The database server resides on the back-end machine. The VisIVO community of advanced users are enabled to create, change, invoke, and monitor workflows accessing to all of the components of WS- PGRADE/gUSE, while standard users are provided with the easy-to-use specific web based user interfaces described in Section III-A including the gUSE Application Specific Module (ASM) API [15] to reuse the implemented workflows stored in the local repository of gUSE. The VisIVO Mobile application configures and submits workflows residing on the VisIVO Gateway by means of the gUSE Remote API as described in section III-B. Fig. 6. VisIVO Gateway Architecture. The current version of VisIVO Mobile is implemented in Objective-C optimized for the Apple iPhone, iPod and iPad, and, in the near future, it will be ported to other popular smartphone devices. End users can login with the same credentials as on the gateway and the application provides the password coding in SHA cryptography exploiting the built-in functionalities of the Liferay9 environment and querying the remote database to verify access credentials. The configuration and submission of workflows residing on the VisIVO Gateway is performed by means of the gUSE Remote API [13]. This API interfaces to the core gUSE services without the WS- PGRADE user interface component. Thus running and manag- ing scientific workflows is realized by command line solutions consisting of curl10 based access wrapped in shell scripts. The API exposes usage of gUSE components through a simple web The VisIVO Science Gateway currently exploits the service interface, resulting in wide adaptability by a diverse set Cometa Consortium grid13 . This infrastructure is distributed in of tools and programming languages. 11 http://jcp.org/en/jsr 9 http://www.liferay.com 12 http://www.vaadin.com 10 http://curl.haxx.se/ 13 http://www.consorzio-cometa.it seven sites of Sicily. All sites have the same hardware and soft- and it can be extended with other engines on demand. Such ware configuration allowing high interoperability and realizing extensions translate between workflow languages and facilitate an homogeneous environment. The computing infrastructure is the nesting of workflows into larger workflows even when based on IBM Blade Centre each containing up to 14 IBM those are written in different languages and require different LS21 blades interconnected with the low latency Infiniband- interpreters for execution. This functionality can enable scien- 4X network, to provide High Performance Computing (HPC) tific collaborations to share and offer workflows for reuse and functionalities on the grid. There are currently about 2000 execution. Shared workflows can be executed on-line, without CPU cores and more than 200 TBs of disk storage space installing any special client environment for downloading available on this HPC e-Infrastructure. As reported in [4] workflows. the VisIVO Science Gateway is undergoing testing under the ETICS system [16] based on the Metronome software [17] by V. S UPPORTING C OMMUNITIES 4D Soft14 . Web testing has been adopted by 4D Soft mainly because it is platform and application independent for testing A number of challenging workflows has been prototyped in different environments and supports different technologies recently to support highly specialised scientific communities in a uniform way through test libraries. Currently a number mainly in astrophysics. This section discusses our experiences of tests is under development suitable for the VisIVO Mobile with the visualisation-oriented workflows Muon Portal and application. LasMOG, and the simulation-oriented workflow FRANEC. The former are deployed for detecting nuclear threat materials (see IV. S HARING W ORKFLOWS V-A) and investigating large-scale modified gravity models (see V-B) respectively. The latter is exploited for carrying out Building large workflows from scratch to address scientific stellar evolution simulations. These workflows will be sup- communities can be time-consuming, as it is inherently a ported in ER-flow18 so that they can be stored into the SHIWA multi-disciplinary process. As an example, although astro- workflow repository together with related meta-data, allowing physicists might be able to appreciate the benefit to their investigation of their interoperability and dissemination across work in using a workflow, they are less interested in the relevant communities through the SHIWA simulation platform. technical details for developing it, this is a task that is naturally associated with the developers (typically computer scientists). Advanced users can exploit such workflows as templates Manually monitoring the evolving structure of workflows, e.g. for building new customized workflows to suit particular by email or written documentation, can be quite challenging. requirements of scientific communities, e.g. by modifying The plan is then to not only educate non computer science appropriately constituent building blocks customized LasMOG scientific communities in using workflows, but to also provide workflows can be generated. Standard users can then execute them with high level tools so that they can access the results these workflows in an interactive and user-friendly way by of these workflows intuitively. Effective collaboration requires means of the supplied portlets. Any user can submit jobs to ways to facilitate exchange between different groups, in partic- the underlying DCIs without requiring a priori any specific ular enabling sharing and realizing re-use and interoperability. technical expertise related to the particulars of the DCI con- The SHIWA project15 (SHaring Interoperable Workflows for figuration. large-scale scientific simulations on Available DCIs) provided We are currently in the planning stages of developing a solutions to facilitate sharing and exchanging of workflows number of new visualisation-oriented workflows to be de- between workflow systems and DCI resources through the ployed for rapid discovery of supernova light curve anoma- SHIWA Simulation Platform (SSP) consisting of: lies19 and validation of models reconstructing the large scale structure of the universe2021 . Furthermore two simulation- • SHIWA Repository16 : A database where workflows oriented workflows are under development, the first one will and meta-data about workflows can be stored. The be deployed for studying trajectories of interstellar comets database is a central repository for users to discover passing through the Solar System and the second one will be and share workflows within and across their commu- focused on modelling the dynamical evolution of meteoroid nities. streams. The vision is that, once a sufficient number of • SHIWA Portal17 : A web portal that is integrated visualisation-oriented and simulation-oriented workflows has with the SHIWA Repository and includes a workflow been developed, to analyse any similarities in depth towards executor engine that can orchestrate various types of developing templates for generating classes of workflows to workflows on a number of computational grid/cloud address the needs of specialized scientific communities. The platforms. remaining of this section focuses on the Muon Portal, LasMOG and FRANEC workflows. Through the SHIWA Portal one can define and run sim- ulations on the SHIWA Virtual Organisation which is an e- A. Muon Portal infrastructure that gathers computing and data resources from various DCIs, including the European Grid Infrastructure. The The deflection of muonic particles present in the secondary portal (via third party workflow engines) provides support cosmic radiation results from crossing high atomic number for a number of commonly used academic workflow engines materials (such as uranium or other fissile materials). This can 14 http://etics3.4dsoft.hu 18 http://www.erflow.eu 15 http://www.shiwa-workflow.eu 19 http://supernovae.in2p3.fr/∼guy/salt 16 http://shiwa-repo.cpc.wmin.ac.uk 20 http://www.mpa-garching.mpg.de/gadget 17 http://shiwa-portal2.cpc.wmin.ac.uk/liferay-portal-6.1.0 21 https://github.com/cmcbride/bgc utils significantly improve on the success rate of current nuclear modified gravity (i.e. without introducing dark energy) as an threat detection methods which are based on X-ray scan- alternative to dark energy models [23]. ners [18], especially in terms of capacity for identification and location of illicit materials inside cargo containers, even Observing the large scale structure of the universe could considering the possibility of screens designed to mask their in principle provide new test of GR on cosmic scales. This existence [19]. kind of test cannot be done without the help of simulations as the structure formation process is highly non-linear. Large- We have developed a visualisation-oriented workflow suit- scale simulations are thus performed for modified gravity able for inspection of cargo containers carrying high atomic models, e.g. from the Large Simulation for Modified Gravity number materials, by displaying tomographic images [20]. (LaSMoG) consortium. Preliminary results of this workflow have been reported in [4]. The datasets containing coordinates of the muon tracker planes Fig. 8. LasMOG processing: portlet interface, workflow and selected results. are first uploaded to our gateway and filtered by using the Point of Closest Approach (POCA) algorithm [21] to create a representation containing the scattering deflection of cosmic radiations. The result is then visualized using point rendering. Further processing is then applied based on user-defined thresholds, followed by conversion into data volumes using the deflection angle field distribution by employing the 3D Cloud- in-Cell (CIC) [22] smoothing algorithm. Finally, a tomography is performed for inspection. Figure 7 shows the most recent development and results of the entire computational process starting from: a) parameter setting through the supplied portlet, then b) submitting the implemented workflow, and finally c) outputting resulting images obtained using isosurface render- ing for the filtered (top image) and raw (bottom image) datasets respectively. Fig. 7. Muon Portal processing: portlet interface, workflow and selected results. The workflow shown in Figure 8 implements a customised visualization for aiding analysis of modified GR simulations, more specifically inspecting datasets to discover anomalies by comparing appropriately with datasets coming from standard GR models. The main computational steps are summarised as follows: • Two datasets corresponding to snapshots of standard gravity (DS ) and modified gravity (DM ) model sim- ulations are processed. • Sub-samples of the point distributions with a reduced number of points in the two datasets are generated. Then, for each of these sub-samples a panoramic movie is created (as shown in the resulting top image of Figure 8). • A point distribute operation is performed on DS and DM to create new volume datasets (VS and VM respectively) using a field distribution algorithm on a regular mesh. B. LasMOG • A volume property on the same computational domain is distributed on a regular mesh producing a density The acceleration of the Universe is one of the most chal- field. lenging problems in cosmology. In the framework of general relativity (GR), the acceleration originates from dark energy. • A new volume V∆ is computed where each of its However, to explain the current acceleration of the Universe, voxels shows a difference of values in the density the required value of dark energy must be incredibly small. between VS and VM . It is then filtered with a lower Recently efforts have been made to construct models for bound threshold and all the voxels satisfying the filters are saved in a text file for further analysis purposes Fig. 9. FRANEC processing: portlet interface, workflow and selected results. (as shown in the resulting bottom image of Figure 8). • Several renderings of V∆ are performed: ◦ Volume rendering; ◦ Isosurface rendering of the density field to produce panoramic movies using different iso- values (as shown in the resulting bottom image of Figure 8); ◦ Ortho-slice rendering i.e. orthogonal slice planes through the volume dataset. C. FRANEC FRANEC is a state-of-the-art [24] numerical code for stellar astrophysics. This code is perfectly suited for computing evolutions of stars on the basis of a number of different physi- cal inputs and parameters. A single run of FRANEC produces one synthetic model (SM). To produce an isochrone, for a given chemical composition, through a FIR (Full Isochrone Run), it is necessary to execute a large number of SMRs (SM runs) varying the initial mass of the stellar models. Once these evolutionary tracks and isochrones (and other additional data) are computed, they can be distributed in datasets over different sites. The simulations of stellar models produce simulation out- put files with a set of associated metadata. Such metadata are linked to all parameters concerning the numerical evolutionary code. In this way it is possible to store and easily search and re- 4) Output Post-Processing module consists of the fol- trieve the obtained data by many sets of stellar simulations, and lowing jobs: furthermore get access to a large amount of homogeneous data • TAR produces a compressed archive of the such as tracks and isochrones computed by using FRANEC. main outputs. The FRANEC workflow (see Figure 9) has a modular architec- • GNUPLOT produces the output plots (e.g. the ture making it easy to identify reusable modules for building ones included in Figure 9). other workflows. Modules can be differentiated on the basis of their functionality: VI. C ONCLUSIONS 1) EOS Computation module provides the Equation of State in tabular form. The input values are the Metal- Traditionally the common practice among astronomers for licity Z and the type of mixture (combination of data exploration tools was to employ small, individually cre- chemical elements heavier than helium). ated and executed applications. This scenario is not applicable 2) OPACITY Computation module produces a table of to modern large-scale datasets. Modular web applications for Opacity from pre-calculated tables. Given the Metal- data analysis and visual discovery making effective usage of licity value Z and the type of mixture it obtains a modern e-infrastructures can be instrumental in reaching out new table of opacity which is interpolated from the astrophysical communities and aiding them in new scientific pre-calculated ones. discoveries. 3) FRANEC is the core module of the workflow. It A workflow-oriented gateway allows scientists to share produces the models of stellar evolution starting from their analysis workflows and identify best practices for inves- the output of the two modules EOS and OPACITY tigating their datasets. More importantly, they can automate and a set of input parameters given by the user to workflows for repeated analysis with changed parameters, perform the evolution: the mass (in Solar Units) of which in the past was a manual, slow and very error prone the structure, the mass fraction of the initial helium, process. This way scientists can focus on core scientific the mass fraction of the heavy elements abundance, discoveries rather than wasting time on data analysis on dealing the efficiency of superadibatic convection, the mass with inadequate resources. loss , the core convective overshooting during the H- burning phase , the diffusion index and the evolu- VisIVO Gateway provides a web based portal for setting tionary stage index . It produces a set of parameter up, running and evaluating visualizations in astrophysics for values varying in relation to time, quantities varying large-scale datasets exploiting DCIs resources. The gateway in relation to the radius of the model, the chemical includes a data repository containing images and movies composition of the core (vs. time), surface chemicals produced from imported datasets, as well as repositories of (vs. time), and energy resolution flows(vs. time). fundamental workflows, which can be used as templates for generating new workflows to be distributed by the users of the [8] G. Song, Y. Zheng, and H. Shen, “Paraview-based collaborative visu- system. alization for the grid,” Advanced Web and Network Technologies, and Applications, pp. 819–826, 2006. We presented several portlets running in a Liferay portal [9] M. Hereld, E. Olson, M. Papka, and T. Uram, “Streaming visualization environment together with a mobile application making the for collaborative environments,” in Journal of Physics: Conference gateway accessible from modern mobile platforms. For a Series, vol. 125, no. 1. IOP Publishing, 2008. number of specialised astrophysical communities we have [10] M. Comparato, U. Becciani, A. Costa, B. Larsson, B. Garilli, C. Gheller, and J. Taylor, “Visualization, exploration, and data analysis of complex discussed workflows and the issues involved in developing astrophysical data,” Publications of the Astronomical Society of the them. The modularity achieved by subdividing workflows into Pacific, vol. 119, no. 858, pp. 898–913, 2007. a number of core tasks ensures re-usability and provides high [11] U. Becciani, A. Costa, N. Ersotelos, M. Krokos, P. Massimino, C. Petta, flexibility. End users do not need to be aware of set-up options and F. Vitello, “Visivo: A library and integrated tools for large astro- or be aware of the computing infrastructure operating behind physical dataset exploration,” in Astronomical Data Analysis Software the scenes. and Systems XXI, vol. 461, 2012, p. 505. [12] A. Costa, U. Becciani, P. Massimino, M. Krokos, G. Caniglia, We envisage building a specialized repository of astro- C. Gheller, A. Grillo, and F. Vitello, “Visivoweb: a www environment physics workflows core modules to share them among com- for large-scale astrophysical visualization,” Publications of the Astro- munities using the SHIWA platform. Our vision for these is nomical Society of the Pacific, vol. 123, no. 902, pp. 503–513, 2011. to be used not only by astrophysical communities but to also [13] P. Kacsuk, Z. Farkas, M. Kozlovszky, G. Hermann, A. Balasko, K. Karoczkai, and I. Marton, “Ws-pgrade/guse generic dci gateway be potentially exploited within other scientific contexts. This framework for a large variety of user communities,” Journal of Grid activity will also be instrumental in future work for creating an Computing, vol. 10, no. 4, pp. 601–630, 2012. Astro-Gateway Federation establishing a network of Science [14] P. Kacsuk, K. Karoczkai, G. Hermann, G. Sipos, and J. Kovacs, “WS- Gateways to benefit astrophysical communities sharing tools PGRADE: Supporting parameter sweep applications in workflows,” in and services, data, repositories, workflows and computing Workflows in Support of Large-Scale Science, 2008. WORKS 2008. infrastructures. Third Workshop on. Ieee, 2008, pp. 1–10. [15] A. Balasko, M. Kozlovszky, A. Schnautigel, K. Karóckai, I. Márton, T. Strodl, and P. Kacsuk, “Converting p-grade grid portal into e-science ACKNOWLEDGMENT gateways,” International Workshop on Science Gateways, pp. 1–6, 2010. [16] A. Meglio, M. Bégin, P. Couvares, E. Ronchieri, and E. Takacs, “Etics: The research leading to these results has received funding the international software engineering service for the grid,” in Journal from the European Commission’s Seventh Framework Pro- of Physics: Conference Series, vol. 119. IOP Publishing, 2008, p. gramme (FP7/2007-2013) under grant agreement no 283481 042010. SCI-BUS (SCIentific gateway Based User Support) and the [17] A. Pavlo, P. Couvares, R. Gietzel, A. Karp, I. Alderman, M. Livny, and FP7 project under contract no 312579 ER-flow (Building C. Bacon, “The NMI build & test laboratory: Continuous integration framework for distributed computing software,” in The 20th USENIX an European Research Community through Interoperable Large Installation System Administration Conference (LISA), 2006, pp. Workflows and Data). 263–273. [18] J. Katz, G. Blanpied, K. Borozdin, and C. Morris, “X-radiography of R EFERENCES cargo containers,” Science and Global Security, vol. 15, no. 1, pp. 49– 56, 2007. [1] A. Hassan and C. Fluke, “Scientific visualization in astronomy: Towards [19] S. Riggi, V. Antonuccio, M. Bandieramonte, U. Becciani, F. Belluomo, the petascale astronomy era,” Publications of the Astronomical Society M. Belluso, S. Billotta, G. Bonanno, B. Carbone, A. Costa et al., of Australia, vol. 28, no. 2, pp. 150–170, 2011. “A large area cosmic ray detector for the inspection of hidden high-z [2] M. Borkin, S. Offner, E. Lee, H. Arce, and A. Goodman, “Visualiza- materials inside containers,” in Journal of Physics: Conference Series, tion and analysis of synthetic observations of embedded protostellar vol. 409, no. 1. IOP Publishing, 2013, p. 012046. outflows,” in Bulletin of the American Astronomical Society, vol. 43, [20] M. Bandieramonte, “Muon tomography: tracks reconstruction and vi- 2011, p. 25813. sualization techniques,” Nuovo Cimento C - Colloquia and Communi- [3] A. Belloum, M. Inda, D. Vasunin, V. Korkhov, Z. Zhao, H. Rauwerda, cations in Physics, to appear. T. Breit, M. Bubak, and L. Hertzberger, “Collaborative e-science ex- [21] D. Sunday, “Distance between lines and segments with their closest periments and scientific workflows,” Internet Computing, IEEE, vol. 15, point of approach,” 2004. [Online]. Available: http://softsurfer.com/ no. 4, pp. 39–47, 2011. Archive/algorithm 0106/algorithm 0106.htm [4] E. Sciacca, M. Bandieramonte, U. Becciani, A. Costa, M. Krokos, [22] R. Hockney and J. Eastwood, Computer simulation using particles. P. Massimino, C. Petta, C. Pistagna, S. Riggi, and F. Vitello, “Visivo Taylor & Francis, 1992. workflow-oriented science gateway for astrophysical visualization,” in [23] G.-B. Zhao, B. Li, and K. Koyama, “N-body simulations for f (r) gravity 21st Euromicro International Conference on Parallel, Distributed and using a self-adaptive particle-mesh code,” Physical Review D, vol. 83, Network-Based Computing (PDP’13). IEEE Computer Society Press, no. 4, p. 044007, 2011. 2013. [24] A. Pietrinferni, S. Cassisi, M. Salaris, and F. Castelli, “A large stellar [5] P. Kacsuk, “P-grade portal family for grid infrastructures,” Concurrency evolution database for population synthesis studies. i. scaled solar and Computation: Practice and Experience, vol. 23, no. 3, pp. 235–245, models and isochrones,” The Astrophysical Journal, vol. 612, no. 1, 2011. p. 168, 2008. [6] U. Becciani, A. Costa, V. Antonuccio-Delogu, G. Caniglia, M. Com- parato, C. Gheller, Z. Jin, M. Krokos, and P. Massimino, “Visivo– integrated tools and services for large-scale astrophysical visualization,” Publications of the Astronomical Society of the Pacific, vol. 122, no. 887, pp. 119–130, 2010. [7] M. Riedel, W. Frings, S. Dominiczak, T. Eickermann, D. Mallmann, P. Gibbon, and T. Dussel, “Visit/gs: Higher level grid services for scientific collaborative online visualization and steering in unicore grids,” in Parallel and Distributed Computing, 2007. ISPDC’07. Sixth International Symposium on. IEEE, 2007.