Scientific Visualization Services at RZG

Transcription

1 Scientific Visualization Services at RZG Klaus Reuter, Markus Rampp Garching Computing Centre (RZG) 7th GOTiT High Level Course, Garching, 2010

2 Outline 1 Introduction 2 Details on the visualization infrastructure at RZG 3 Demonstration of a session on the visualization cluster 4 Visualization projects at RZG 5 Various use cases 6 Summary

3 Motivation: Scientific Visualization scientific visualization = visualization of 3D phenomena with emphasis on realistic renderings of volumes, surfaces,... visual impression & quantitative analysis of data

4 Motivation: Scientific Visualization scientific visualization = visualization of 3D phenomena with emphasis on realistic renderings of volumes, surfaces,... visual impression & quantitative analysis of data visualization process numerical simulation (z.b. fusion research, astrophysics, meteorology, geophysics,...) measurements (z.b. satellites, medical diagnostics [CT, MRT],...) data transformation mapping display

5 Motivation: Scientific Visualization scientific visualization = visualization of 3D phenomena with emphasis on realistic renderings of volumes, surfaces,... visual impression & quantitative analysis of data visualization process numerical simulation (z.b. fusion research, astrophysics, meteorology, geophysics,...) measurements (z.b. satellites, medical diagnostics [CT, MRT],...) data transformation mapping display advantage: utilize the high bandwidth and processing speed of the human vision system to extract information from large datasets, to complement data analysis algorithms source: VTK Textbook, Kitware

6 Visualization support for the Max Planck Society necessity to centralize visualization services HPC codes generate huge amounts of data ( TBs) raw data cannot be transferred to the scientist s workstation visualization requires HPC like resources (hardware, housing) expert s knowledge on methods and software is needed

7 Visualization support for the Max Planck Society necessity to centralize visualization services HPC codes generate huge amounts of data ( TBs) raw data cannot be transferred to the scientist s workstation visualization requires HPC like resources (hardware, housing) expert s knowledge on methods and software is needed mission of RZG provide a central infrastructure for interactive remote data analysis and visualization support for adaptation and instrumentation of simulation codes support for selection and usage of analysis and visualization software dedicated support for individual demanding visualization projects

8 Challenges for visualization support broad range of disciplines in the Max Planck Society (e.g. plasma physics, astrophysics, solid state physics, chemistry,... ) many different scientific contexts variety of simulation codes: home grown, commercial, open source, closed source non-standardized heterogeneous data formats often individual post processing needed

9 Challenges for visualization support broad range of disciplines in the Max Planck Society (e.g. plasma physics, astrophysics, solid state physics, chemistry,... ) many different scientific contexts variety of simulation codes: home grown, commercial, open source, closed source non-standardized heterogeneous data formats often individual post processing needed HPC codes generate big data sets (big: amount of data, memory requirements, complexity) multidimensional, e.g. 4D (1 temporal, 3 spatial coordinates) several variables, e.g. scalar, vector, tensor fields gridded data (e.g. regular or curved grids), point clouds

10 1 Introduction 2 Details on the visualization infrastructure at RZG 3 Demonstration of a session on the visualization cluster 4 Visualization projects at RZG 5 Various use cases 6 Summary

11 The HP visualization cluster at RZG 1 login node with 8 CPU cores, 144 GB RAM 4 visualization nodes with 8 CPU cores, 144 GB RAM and 2 GPUs (NVidia Quadro FX 5800) each 1 visualization node with 24 CPU cores, 256 GB RAM and 2 GPUs InfiniBand interconnect GPFS file system (30 TB) GPFS file systems of the HPC machines mounted (BlueGene/P, Power6) operating system: SLES 11 (SP1) 2 dedicated graphics workstations (active stereo)

12 Technical background on remote rendering (1) Problem: X Window System renders locally on the client which, in general, does not have powerful hardware and network connection source:

13 Technical background on remote rendering (1) Problem: X Window System renders locally on the client which, in general, does not have powerful hardware and network connection source: Solution: Redirection of GL instructions, transport of image data

14 Technical background on remote rendering (2) Method 1: VirtualGL Image Transport source:

15 Technical background on remote rendering (3) Method 2: VNC X Server (X Proxy), VNC Client source:

16 Software available on the RZG visualization cluster VisIt (LLNL), ParaView (Kitware) VisIt architecture tools for analyzing multidimensional data in Desktop computer various formats (a.o. VTK, XDMF & HDF5) GUI Viewer methods: cuts, surfaces, volume rendering,... open source, expandable by plug-ins massively parallel (MPI) Python API non-interactive rendering Vapor (NCAR) Parallel Database Parallel Database Parallel compute wavelet-based compression Parallel compute Parallel compute server engine Parallel compute server engine Parallel compute engine Parallel compute engine ideal for Cartesian grids ( brick of values ) compute engine compute engine engine engine Data excellent interactivity files splotch (MPA), for SPH data Remote computer Voreen (Mu nster Univ.), cf. Vapor Blender, POV-Ray (ray tracing tools) tools developed at RZG (OpenGL) source: Klaus Reuter (RZG) RZG Visualization Services GOTiT Course, Garching, 2010

17 1 Introduction 2 Details on the visualization infrastructure at RZG 3 Demonstration of a session on the visualization cluster 4 Visualization projects at RZG 5 Various use cases 6 Summary

18 An interactive session on the visualization cluster Scenario user starts a remote desktop session via the reservation system at user connects to the remote session using a VNC viewer live demonstration Documentation vizcluster

19 1 Introduction 2 Details on the visualization infrastructure at RZG 3 Demonstration of a session on the visualization cluster 4 Visualization projects at RZG 5 Various use cases 6 Summary

20 Visualization projects at RZG Selection of ongoing and completed projects plasma physics: MHD turbulence astrophysics: type-ii supernova explosion astrophysics: visualization of SPH data see also

21 MHD turbulence background: turbulence in magnetized plasmas, dynamo effect software: VAPOR for interactive analysis, VisIt for rendering animations resolution: , Cartesian periodic grid simulation: J. Pratt, W.-C. Müller (IPP)

22 Type-II supernova explosion background: supernova explosion of a collapsed 15M star, first 3D simulations of the long-term evolution simulation: N. Hammer simulation code: PROMETHEUS/HOTB visualization: multi-channel volume rendering with VisIt; elements Ni56, O16, C12 appear in blue, green, and red; final frames are obtained by combining these as RGB channels [Hammer et al., ApJ 714, 1371 (2010)]

23 Type-II supernova explosion background: supernova explosion of a collapsed 15M star, first 3D simulations of the long-term evolution simulation: N. Hammer simulation code: PROMETHEUS/HOTB visualization: multi-channel volume rendering with VisIt; elements Ni56, O16, C12 appear in blue, green, and red; final frames are obtained by combining these as RGB channels [Hammer et al., ApJ 714, 1371 (2010)]

24 Type-II supernova explosion background: supernova explosion of a collapsed 15M star, first 3D simulations of the long-term evolution simulation: N. Hammer simulation code: PROMETHEUS/HOTB visualization: multi-channel volume rendering with VisIt; elements Ni56, O16, C12 appear in blue, green, and red; final frames are obtained by combining these as RGB channels [Hammer et al., ApJ 714, 1371 (2010)]

25 Visualization of SPH data using standard software (1) Problem SPH simulations generate point clouds (i.e. non-gridded data) quantitative analysis and volume rendering require gridded data how to generate a 3D grid from 107 points?? Klaus Reuter (RZG) RZG Visualization Services GOTiT Course, Garching, 2010

26 Visualization of SPH data using standard software (2) Solution: code development for Delaunay triangulation of the point cloud implementation based on VTK: CPU time scales as O(N 2 ), huge memory footprint impossible for large datasets implementation based on Qhull: CPU time scales nearly linearly, domain decomposition solves the memory problem and allows to work in parallel quantitative analysis can be performed on the resulting gridded data, e.g. using ParaView in cooperation with C. Simion (TUM), data by S. Kochfar (MPE) (

27 1 Introduction 2 Details on the visualization infrastructure at RZG 3 Demonstration of a session on the visualization cluster 4 Visualization projects at RZG 5 Various use cases 6 Summary

28 Ray tracing for scientific visualization? stunning pictures created by ray tracers are well known from computer generated cartoons (entertainment industry) POV-Ray ( Blender ( source: source: Klaus Reuter (RZG) RZG Visualization Services GOTiT Course, Garching, 2010

29 Ray tracing for scientific visualization? stunning pictures created by ray tracers are well known from computer generated cartoons (entertainment industry) POV-Ray ( Blender ( source: source: ray tracing is useful in scientific visualization as well! Klaus Reuter (RZG) RZG Visualization Services GOTiT Course, Garching, 2010

30 Visualizing magnetic field lines with POV-Ray magnetic field lines from an MHD dynamo simulation (K. Reuter, PhD thesis, 2010)

31 Visualizing gyrokinetic turbulence with IDL & POV-Ray GENE simulation & IDL diagnostics by M.J. Pueschel textures from IDL are mapped onto a torus using POV-Ray

32 VAPOR use case: enstrophy density in an MHD flow enstrophy density in a turbulent MHD dynamo simulation (K. Reuter, PhD thesis, 2010)

33 Summary Main points visualization is part of high performance computing remote visualization platform is available at RZG raw data can remain where it is generated support is provided by RZG s application support team demonstration of an interactive session and of selected projects Links