The interactive visualization of the complex and massive three-dimensional data produced by HPC programs is a challenge.
In the framework of the COAST "Computational Astrophysics" program at DSM/Irfu, our group has acquired an expertise in scientific visualization through the development of two projects: SDvision and PymSES.
The SDvision (Saclay Data Visualization) software is deployed in the IDL "Interactive Data Language" platform.
IDL is the dominant data analysis and visualization package in astrophysics.
It gives access to hardware-accelerated rendering techniques through its "Object Graphics" interfaces to OpenGL, as well as multi-threaded usage of multiple-core processors.
The graphics objects can be coupled with GLSL (OpenGL Shading Language) shader algorithms to perform both scientific computation and visualization by graphics card.
The SDvision visualization software consists in 96000 lines of code addressing the issue of handling a multiplicity of types of variables: scalar fields, vector fields and clouds of points.
The SDvision software can be controlled either interactively or through scripts describing a sequence of commands. This latter option is used mainly to produce videos exposing the evolution in the simulation or to explore the volume following predefined selected routes. It has facilities to produce Stereo3D outputs and 8-cam outputs for autostereoscopic screens. The favored data structure for scalar and vector fields is the uniform grid. More sophisticated data structures such as AMR (Adaptive Mesh Refinement) are handled by projection onto uniform grid of adequate resolution.
The algorithms implemented SDvision favor the use of shared-memory architectures equipped by multiple-core processors and massive available RAM (to benefit to the ray-casting rendering technique) and with high-range graphics units (for the acceleration of polygon rendering and use of GLSL Shaders). Our group has its own visualization platforms with the typical following properties: 512 GB RAM, 4 octocore processors with dual-thread settings (64 computing cores) and NVIDIA Quadro FERMI 6000 graphics units.
We also are pioneering users of the remote visualization facilities offered by CEA/CCRT and TGCC such as CESIUM (now decommissioned) and the dedicated visualization nodes of AIRAIN and CURIE now in operation.
The PymSES python modules have been developed with the specific objective to visualize the AMR Adaptive Mesh data structure associated with the RAMSES simulation code.
It offers two highly efficient CPU-intensive volume rendering algorithms: a ray-casting volume reconstruction based on direct propagation of the rays through the AMR structure, and a splatting algorithm, based on the application of a predefined texture on each cell of the AMR tree. This visualization tool offers both an interactive mode and a batch mode to produce sequence of images (videos).
Originally developed at Irfu/Sap, the algorithms employed in this visualization software were the subject of further research & development in the context of a Ph.D. thesis.
Since the launch of the COAST program in 2005, the group has produced visualizations in the many fields of astrophysics under study at Irfu/SAp: Large-Scale Structure formation in a cosmological context, formation and evolution of spiral galaxies, interaction of galaxies, interstellar medium, star formation, solar magnetism, polar dwarves in binary systems, accretion disks, protoplanetary nebulae, and ring formation around giant planets. In 2008, following an initiative by DSM, the group started collaborations outside the fields of astrophysics.
This most notably led to produce visualizations of the turbulences in the ITER fusion plasma as obtained with GYSELA code, as shown in Figure 4. Other collaborations include particle accelerators simulations (IFMIF-EVEDA injector project for fusion research at Irfu/SACM), theoretical nuclear physics (visualization of wavefunctions in actinides collisions at Irfu/SPhN), cosmic-rays detectors (WatTo detector at Irfu/SEDI), and solid-state physics (ELF Electron Localization Function at DSM/IRAMIS). The group has been engaged in the visualization of observational astrophysical data, using the very same algorithms and expertise gained in the context of HPC simulations. The first initiative was proposed in the context of the XMM-LSS Survey of X-ray clusters, to study the three-dimensional distribution of these objects and compare them with the density fields obtained on galaxy redshift surveys in the CFHTLS-W1 and COSMOS fields.
This project is pursued in the context of XXL Survey with the largest ever observing time allocated on the XMM-Newton Satellite. Other contributions to the Irfu/SAp observing programs included the visualization of datacubes (a data structure expressed in the right-ascension, declination, and wavelength coordinate system) for the ALMA and IRAM observatories.
In 2011 we started a collaboration with a group led by R. Brent Tully at the University of Hawaii to explore the cosmography of the Local Universe. Visualization is an essential tool in this project.
Catalogs of peculiar velocities of galaxies are used as input to a Wiener Filter reconstruction algorithm. This algorithm produces as output the three-dimensional velocity field of gravitational origin in which matter (galaxies and dark matter) is flowing. This velocity field and its by-products (density field, potential fied, shear tensor, cosmic velocity-web) are visualized using the very same algorithms used to visualize HPC simulations.
The visualization of the velocity on the basis of streamlines reconstruction led to the identification of a Basin of Attraction in which the flow converge onto a unique attractor. This concept of Basin of Attraction was proposed to give a proper definition of superclusters of galaxies. The identification of the nature and extent of our Home supercluster Laniakea lead to a publication in Nature (Figure 6). Our visualization expertise was used to produce a video which became the most viewed science video of the Nature channel on YouTube with 3.3 million views), demonstrating the impact on society of these research and technology (https://www.youtube.com/watch?v=rENyyRwxpHo).