This document discusses using GPUs on AWS for computational fluid dynamics (CFD). It summarizes that high performance computing (HPC) is used to solve large problems by aggregating computing power. CFD workflows on AWS can benefit from compute, storage, networking and visualization resources. The document also describes how a company called Altair uses GPU-accelerated solvers like Lattice Boltzmann method (LBM) and Smooth particle hydrodynamics (SPH) to improve CFD simulations of things like car aerodynamics and gearbox oil sloshing.

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Transforming Computational Fluid
Dynamics (CFD) with GPUs on AWS
Nicola Venuti
Sr. HPC Specialized SA
AWS
C M P 3 2 7
Dr. Marc Ratzel
Director Aerodynamics CFD
Altair

3. High Performance Computing (HPC) most generally refers to
the practice of aggregating computing power in a way that
delivers much higher performance than one could get out of
a typical desktop computer or workstation in order to solve
large problems in science, engineering, or business
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HPC—Use cases
Clustered (Tightly coupled)
Distributed/Grid (Loosely coupled)
Data light
Minimal
requirements for
high performance
storage
Data heavy
Benefits from
access to high
performance
storage
Fluid dynamics
Weather forecasting
Materials simulations
Crash simulations
Risk simulations
Molecular modeling
Contextual search
Logistics simulations
Seismic processing
Metagenomics
Astrophysics
Deep learning
Animation and VFX
Semiconductor verification
Image processing
Genomics

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Amazon Elastic Compute Cloud (Amazon EC2
instances
General
purpose Dense
storage
Compute
optimized
FPGAs
General
purpose
GPU
Big data
optimized
Graphics
intensive
Memory
optimized
High
I/OM5 D2 X1T3 R5.metal I3.metal C5d F1M5d P3H1 z1dG3X1e I3 C5
High
I/O
Burstable
Compute and
memory intensive
R5
C O M I N G
S O O N
Memory
intensive
z1d.metal
Amazon
Lightsail
In-memory
Virtual
private
servers
C O M I N G
S O O N

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Whiteboarding—CFD workflows
• Traditional HPC—CFD jobs
• Compute
• Storage
• Network
• Visualization

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Fluid dynamics
 C4.8xlarge instance type
 140M cell model
 F1 car Computational Fluid
Dynamics (CFD) benchmark

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2,000+
Engineers, scientists, and creative thinkers
Founded 1985
Headquartered in Troy, MI, U.S.
60,000+
Users
$313M
2016 revenue
5,000+
Customer installations globally
50+ISV partners under our unique,
patented licensing model
69offices
in 24 countries
Altair at a glance (ALTR)

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External aerodynamics (motivation)
Cooling
• Engine
• Transmission
• Brakes
• Condenser
Comfort
• Ventilation
• Heating
• Air conditioning
• Wind noise
Stability
• Directional
• Cross wind
Performance
• Fuel economy
• Emission
• Max. speed
• Acceleration

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Aerodynamics in the design process
Requirements
• A designer can create up to 10 designs per day
• Quick response time (design  aerodynamic evaluation)
• Accuracy/reliable trend prediction
• Provide insight into flow physics/explain why “things” happen
• Standardization of process
Solution
• ultraFluidX, Lattice Boltzmann method (LBM) on GPUs
• Fully detailed car analysis over night (transient)
• Less stringent model requirements than conventional approach
• Robust volume discretization included in solver step
• Easy part replacement “drag & drop”
Sketch
CFD
LBM

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Gearbox oil sloshing
Physics
• Complex, fast moving geometry
• Air & oil (two phase flow)
• Oil phase highly deformed
Solution
• nanoFluidX, Smooth particle hydrodynamics (SPH) on GPUs
• Meshless methods
• Fluid represented by particles
• Runtime few days as opposed to weeks with conventional CFD

13. Thank you!
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Nicola Venuti
Marc Ratzel

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