=Paper=
{{Paper
|id=None
|storemode=property
|title=VisIVO Science Gateway: a Collaborative Environment for the Astrophysics Community
|pdfUrl=https://ceur-ws.org/Vol-993/paper1.pdf
|volume=Vol-993
|dblpUrl=https://dblp.org/rec/conf/iwsg/SciaccaBBCKMPPRV13
}}
==VisIVO Science Gateway: a Collaborative Environment for the Astrophysics Community==
https://ceur-ws.org/Vol-993/paper1.pdf
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
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