GTC 2015 Game Engine -like architecture to build advanced Scientific - - PowerPoint PPT Presentation

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GTC 2015 Game Engine -like architecture to build advanced Scientific - - PowerPoint PPT Presentation

Nov 03, 2023 14 likes •125 views

GTC 2015 Game Engine -like architecture to build advanced Scientific Software applications mik@hue.no In Oil & Gas since 2001 (also tested in Medical and CAE) Deployed by Oil Majors and Commercial Software vendors. Interpretation,

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GTC 2015

“Game Engine”-like architecture to build advanced Scientific Software applications

mik@hue.no

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Processing & Imaging Interpretation, Modeling & Characterization Reservoir Engineering Production Misc E&P

In Oil & Gas since 2001 (also tested in Medical and CAE) Deployed by Oil Majors and Commercial Software vendors.

This is a state-of-the-art product, not research…

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Bringing the technical foundation of the gaming industry, to scientific and engineering software.

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Accelerated compute framework Core Engine

  • rchestrates visualization, compute and
  • data. Developers access through cross-

platform APIs.

Big Data handling State-of-the-art visualization

Visualization-driven interaction and compute, with a single processing pipeline for both.

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HueStreams: 3D Wavelet compression / decompression

  • Multi-CPU/GPU Parallelized 3D wavelet compression/decompression
  • Can be used for any volumetric data: seismic, medical, atmospheric,….
  • Numerically stable between CPUs and GPUs
  • Auto-detects fastest decompression method at run-time (CPU or GPU)
  • Linear GPU scaling
  • On-demand parallel asynchronous data loading and production

1:15 compression example Intel i7 - 4 cores GeForce 650m

(old MacBook Pro)

K6000/K40 4 x K40 Compression 1.5 GB/sec TBA TBA TBA Decompression 2 GB/sec 3.5 GB/sec 10 GB/sec ~40 GB/sec

Technology Sequential read HDD 259 MB/sec SSD 585 MB/sec Flash PCI-e 2.7 GB/sec Our 5X is lossless; but customers have been confortable with 15-30X compression.

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GPU compression for interactive visualization and computation

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From “slow on a 16 nodes CPU cluster” to “fully interactive on K6000”

On the fly interactive streamlines computation and new properties calculations, etc…

Total dataset = 26 GB 1.1 Billion Tetrahedrons

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CFD FD example: mple:

  • 2 Formula1 cars with full CFD simulation (pressure, velocity and velocity magnitude properties).
  • Total dataset = 26 GB = 1.1 Billion Tetrahedrons
  • Simulation model:

– 5.1 GB Vertices – 7.4 GB Properties data – 16 GB Topology/Geometry

  • Traditional:

32-64 GB (2-4X blow up for rendering/algorithm structures)

  • HueSpace:

2.3 GB (7X structural optimization, used directly for rendering and algorithms) Over 14-28 X DATA SIZE OPTIMIZATION

Scale out for geometry data with multi-GPU single system: 12GB GPU ≈ over 1 Billion cells 16 GPUs ≈ over 16 Billion cells

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Seismic Demo

  • Public data from Australia
  • 24,000 Km2 (almost the size of Switzerland or Massachusetts)
  • 7 seismic surveys
  • 1.5 TB combined

Amazon AWS G2 instance w/GRID K520 1536 cores + 4GB GDDR5 Calgary Scientific PureWeb-enabled demo

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“We cannot simply if-then-else out of this…”

Jen-Hsun Huang NVIDIA’s CEO