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Visualization Infrastructure and Services at the MPCDF
Markus Rampp & Klaus ReuterMax Planck Computing and Data Facility (MPCDF)
Interdisciplinary Cluster Workshop on Visualization
Garching, Nov 4, 2015
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Outline
Topics
. overview remote visualization services
· hardware & software infrastructure
· project support
. challenges and outlook
MPCDF Visualization Team
. people involved (part-time, main focus is HPC)
Elena Erastova (visualization projects)
Klaus Reuter (software and hardware coordination, consulting, projects, training)
Markus Rampp (consulting, projects, training)
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Visualization infrastructure for the Max-Planck-Society
MPCDF visualization services:
. provide central software and hardware infrastructure for remote visualization
. target: interactive data exploration & analysis, presentation
. support for adaptation and instrumentation of simulation codes
. guidance for selection, adoption and usage of analysis & visualization software
. dedicated support for individual (particularly demanding) visualization projects
Main conceptual challenges:
. broad range of disciplines in MPG: Plasmaphysics, Astrophysics, . . . , comp. Biology
y many different scientific contexts
y variety of simulation codes: ”home-grown”, commercial, open-source, third-party, . . .
y non-standardized, heterogeneous data structures and formats
y ”legacy” analysis pipelines, . . .
. massive datasets from HPC simulations:(massive: amount of raw data, memory requirements, complexity)
◦ multidimensional (3D + time), multi-variate data
◦ ”unusual” grids: meshless data, special curvilinear coordinates, . . .
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Visualization infrastructure for the Max-Planck-Society
Status
. consulting & dedicated project support since 2008
. MPG visualization cluster operational since Sep. 2010
. open to all MPG scientists and collaboration partners
. many projects supported (some highlights by K. Reuter)
. broad userbase (beyond Garching campus)
Rationale for centralized visualization in the MPG:
. a necessity for a HPC centre rather than an optional service
· huge amounts of output data produced by HPC simulations
· transfer of raw data for local analysis & visualization no more possible
· even dumping the RAM is becoming prohibitive due to I/O constraints
y in-situ visualization (not covered here)
· visualization requires HPC-like resources (specialized hardware, housing, . . . )
· requires substantial expertise on methods, software, . . .y sustainability
. Technological prerequisites
· efficient and transparent remote rendering solution via WAN: VirtualGL/TurboVNC
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Central visualization infrastructure: technical prerequisites
traditional ”X over ssh” (e.g. ssh -X)
. 3D data are transfered to the client
. fails to deliver interactive frame rates
. uses X-server/graphics card of the client
y not suited for 3D applications
server-side rendering
. only (compressed) image stream is transferred
. delivers interactive frame rates with moderate WAN bandwidth
. uses X-server/graphics card(s) of the server
. generic solution (OpenGL)
. mature software solutions/products:
· VirtualGL/TurboVNC (Open Source, ex SUN)
(original illustrations by L. Scheck, by courtesy of LRZ)
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Remote-visualization cluster
Focus:
. enable our (geographically dispersed) scientific users to perform complex visualization tasks with-out special technical prerequisites (software, hardware)y remote visualization
Hardware overview (HP cluster)
. 5 ”standard” visualization nodes each equipped with:
· 2 Intel quadcore CPUs: 8 cores, 144 GB RAM
· 2 NVidia FX 5800 graphics cards
. 1 ”high-end” visualization node:
· 4 Intel hexacore CPUs, 24 cores, 256 GB RAM
· 2 NVidia FX 5800 graphics cards
. 1 login node: viz00.rzg.mpg.de
. dedicated disk system (GPFS, ' 30 TB)
. GPFS filesystem /ptmp of HPC system Hydra mounted
. 2 graphics workplaces (active stereo) in MPCDF offices
Software stack
. SLES 11 (MPCDF standard cluster setup), VizStack middleware (GPUs, X-servers, . . . )
. web-based reservation system (HP, MPCDF)
. remote rendering solution: VirtualGL/TurboVNC (free clients for Linux, MS Windows, MAC)
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User interface
Remote desktop (via TurboVNC)
. a standard desktop in a separate window
. application agnostic
. desktop icons for main applications
. preconfigured according to session properties(number of GPUs, CPUs)
. linux terminal: vglrun <command>
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Software for Visualization
Software for interactive data visualization and analysis
. VisIt: main workhorse for 3D analysis
. Paraview: main workhorse for 3D analysis
. VAPOR: large-scale data (requires preprocessing)
. Voreen: volume rendering
Tools and libraries
. GNU R, IDL, MATLAB, gnuplot, . . .
. VTK, HDF5, SILO, . . .
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Special-purpose software
. Splotch: a (non-interactive), parallel ray tracer for SPH data.
. VMD (Visual Molecular Dynamics): a molecular graphics software.
. POV-Ray: a freeware multi-platform ray-tracing package.
. Blender: an open source, cross platform suite of tools for 3D creation.
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Application support
Documentation
. http://www.rzg.mpg.de/services/visualisation/
Training
. courses (http://www.rzg.mpg.de/services/visualisation/scientificdata/presentations)
· K. Reuter: RZG-Services zur Visualisierung wissenschaftlicher Datensatze, DV-Treffen der Max-Planck-Institute,Gottingen, Sep 15, 2010
· K. Reuter: Scientific Visualisation Services at RZG, Seventh GOTiT High Level Course, Garching, Oct 19, 2010
· M. Rampp: Introduction to VisIt, LRZ course on ”Visualisation of Large Data Sets on Supercomputers”, 2010 –2011
· M. Rampp: Introduction to VisIT, 11th Summer school on scientific visualization, CINECA Bologna/Italy, Jun 13,2012
· M. Rampp: Visualization of HPC simulation data: overview and tutorial, ISSS-12, Prague (2015)
· overview talks at Max-Planck-Institutes: MPA, Garching (2009), FHI, Berlin (2011), MPI f. Biophysics, Frankfurt(2014), . . .
Project support
. dedicated support for visualization projects at different levels:
· from basic ”first level” support to comprehensive visualization and analysis tasks
· requires (considerable) insight to scientific domain
· several completed and ongoing projects, in close collab. with the users/scientists:http://www.rzg.mpg.de/services/visualisation/scientificdata/projects
. contact: [email protected]
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MPG/MPCDF reference applications
Projects with MPCDF support (in close collab. with research groups)
. application domains:
· Plasmaphysics: MHD turbulence simulations for nuclear fusion research (IPP)
· Stellar astrophysics: Supernova simulations, NS mergers (MPA)
· Cosmology : Structure and star formation (MPE)
· Molecular dynamics: Materials research for plasma-wall-interaction (IPP), DFT (FHI)
· CFD: DNS simulations of turbulent Taylor-Couette flows (MPI-DS)
. data structures/grids:
· regular: cartesian, polar (2D, 3D), block-structured (”Yin-Yan”)
· irregular: (mapped) point clouds
. data sizes, dimensions:
· up to 20483 (cartesian), 1000× 180× 360 (polar), 2048× 769× 1153 (cylindrical)
· up to ' 106 (particles in 3D), ' 107 (nodes in 3D unstructured mesh)
· all: multi-variable (scalar, vector), time-dependent
see also:
http://www.rzg.mpg.de/services/visualisation/scientificdata/projects
Presentation by K. Reuter
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MPG/MPCDF reference applications
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MPG/MPCDF reference applications
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MPG/MPCDF reference applications
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MPG/MPCDF reference applications
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MPG/MPCDF reference applications
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Challenges & outlook
Technological
. hitting the limits of general-purpose software tools (VisIT, Paraview): interactivity, memory
demands: O(10003) data
y use GPUs in HPC system, e.g. MPG Hydra with Nvidia K20x GPUs
y enables in-situ visualization: a big buzz or something interesting to watch ?
◦ basic technique: implement library calls in simulation code(APIs for C, FORTRAN)
◦ mediates callbacks to visualization tool
◦ supported by Paraview (”catalyst”), VisIT (”libsim”)
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Challenges & outlook
Organizational
. dedicated project support is of key importance (but not scalable)
. beyond scientific data analysis and insights
· ever increasing standards and expectations for public understanding of science
· are we the right people to ”direct” professional animations (TV documentaries) ?
· do we need ”real” scientific data for this at all ?
· credits ? y Max-Planck Visualization Award
. highly efficient and innovative algorithms often don’t make it into usable software
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Challenges & outlook
Organizational
. dedicated project support is of key importance (but not scalable)
. beyond scientific data analysis and insights
· ever increasing standards and expectations for public understanding of science
· are we the right people to ”direct” professional animations (TV documentaries) ?
· do we need ”real” scientific data for this at all ?
· credits ? y Max-Planck Visualization Award
. highly efficient and innovative algorithms often don’t make it into usable software
Outlook
. remote visualization on HPC system Hydra
(to replace visualization cluster in early 2016)