parallel volume rendering on the ibm blue gene/ptpeterka/talks/peterka-egpgv08... · 2010-10-05 ·...
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![Page 1: Parallel Volume Rendering on the IBM Blue Gene/Ptpeterka/talks/peterka-egpgv08... · 2010-10-05 · Intro: Wanted - 50 Reward Hongfeng Yu, UCD Tom Peterka, ANL Kwan-Liu Ma, UCD. 3](https://reader033.vdocument.in/reader033/viewer/2022050213/5f5eebb83a7df03ad9671ac6/html5/thumbnails/1.jpg)
Tom Peterka
Parallel Volume Rendering on
the IBM Blue Gene/P
www.ultravis.org
Radix Laboratory for Scalable Parallel System Software
Mathematics and Computer Science Division
Argonne National Laboratory
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2Argonne National
LaboratoryEurographics Parallel Graphics and Visualization Symposium ‘08 April 15, 2008 Tom Peterka
www.ultravis.org
Rob Ross, ANL
Intro: Wanted - 50 Reward
Hongfeng Yu, UCD Tom Peterka, ANL
Kwan-Liu Ma, UCD
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3Argonne National
LaboratoryEurographics Parallel Graphics and Visualization Symposium ‘08 April 15, 2008 Tom Peterka
www.ultravis.org
Bigger graphics clusters?
• Cost of scalability
• Power, cooling, space
• Not a rendering bottleneck
Intro: Supercomputer software rendering
- Déjà vu?
Availability
• Short runs available ad hoc
• Longer runs scheduled
• Dedicated resources
Cost effectiveness
• Short runs easy to schedule
• Backfill available cycles
• Policy decisions
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4Argonne National
LaboratoryEurographics Parallel Graphics and Visualization Symposium ‘08 April 15, 2008 Tom Peterka
www.ultravis.orgBackground: Big iron
BUT
NO GRAPHICS!
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5Argonne National
LaboratoryEurographics Parallel Graphics and Visualization Symposium ‘08 April 15, 2008 Tom Peterka
www.ultravis.orgBackground: Data
• Supernovae shock wave
• Structured grid
• 8643
• 5 variables in netCDF
• Preprocess to extract single variable
• Time-varying, 200 time steps
• Each time step 2.5 GB
• Courtesy John Blondin, NC State,
and Tony Mezzacappa, ORNL
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6Argonne National
LaboratoryEurographics Parallel Graphics and Visualization Symposium ‘08 April 15, 2008 Tom Peterka
www.ultravis.orgBackground: Parallel volume rendering
tframe = tio + trender + tcomposite
• MPI programming model
• Distributed memory
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7Argonne National
LaboratoryEurographics Parallel Graphics and Visualization Symposium ‘08 April 15, 2008 Tom Peterka
www.ultravis.orgImplementation: Data distribution & I/O
MPI_File_open(…)
MPI_Type_create_subarrray(…)
MPI_Type_commit(…)
MPI_File_set_view(…)
MPI_File_read_all(…)
File view:
bytes
Dataset view:
logical blocks
Process view:
processes…
P0 P1 Pn
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8Argonne National
LaboratoryEurographics Parallel Graphics and Visualization Symposium ‘08 April 15, 2008 Tom Peterka
www.ultravis.orgImplementation: Ray casting
• Trilinear interpolation
• Front-to-back color, opacity
accumulation
• Early ray termination
• Static data partition
• Each process completes
subimage of its subvolume data
i = ( 1.0 – old) * inew + iold
= ( 1.0 – old) * new + old
where i = intensity, = opacity
P0 data P1 data
P3 dataP2 data
P0 image P1 image
P2 image P3 image
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9Argonne National
LaboratoryEurographics Parallel Graphics and Visualization Symposium ‘08 April 15, 2008 Tom Peterka
www.ultravis.orgImplementation: Direct-send compositing
• Load balanced
• Non-scheduled
• O (n4/3 + n) where
n = number of
cores
• O(m) where m =
number of pixels
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10Argonne National
LaboratoryEurographics Parallel Graphics and Visualization Symposium ‘08 April 15, 2008 Tom Peterka
www.ultravis.orgResults: Strong scaling
• End-to-end results, including file I/O
• Vis-only time .8 s
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11Argonne National
LaboratoryEurographics Parallel Graphics and Visualization Symposium ‘08 April 15, 2008 Tom Peterka
www.ultravis.orgResults: Absolute time distribution
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12Argonne National
LaboratoryEurographics Parallel Graphics and Visualization Symposium ‘08 April 15, 2008 Tom Peterka
www.ultravis.orgResults: Relative time distribution
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13Argonne National
LaboratoryEurographics Parallel Graphics and Visualization Symposium ‘08 April 15, 2008 Tom Peterka
www.ultravis.orgResults: Efficiency
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14Argonne National
LaboratoryEurographics Parallel Graphics and Visualization Symposium ‘08 April 15, 2008 Tom Peterka
www.ultravis.orgResults: Improved efficiency
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15Argonne National
LaboratoryEurographics Parallel Graphics and Visualization Symposium ‘08 April 15, 2008 Tom Peterka
www.ultravis.orgConclusions
Successes
• Visualization on leadership machines at ultrascale
• End-to-end performance is a delicate balance
• I/O matters (E2E time, not just rendering time)
• Combination of systems and visualization solutions
Current work
• Load balance
• Hiding I/O cost
• Improved image quality
• Larger data
Future work
• Less structured data
• Interaction
• Novel display environments
• Exploit multi-cores
• Other architectures
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16Argonne National
LaboratoryEurographics Parallel Graphics and Visualization Symposium ‘08 April 15, 2008 Tom Peterka
www.ultravis.orgQuestions and challenges
Technical
• Compositing
• Interactivity
• In situ visualization:
implementation
Nontechnical
• Machine availability
• Machine utilization
• In situ visualization:
collaboration
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Tom Peterka
Parallel Volume Rendering on
the IBM Blue Gene/P
www.ultravis.org
Radix Laboratory for Scalable Parallel System Software
Mathematics and Computer Science Division
Argonne National Laboratory