The document discusses how manufacturing companies are facing challenges related to increasing engineering productivity, reducing product development time, and efficiently using expensive simulation software licenses. It describes how SGI solutions like their Scale-up and Scale-out computing platforms and workload scheduling tools help address these challenges by enabling high performance computing across geographically distributed engineering facilities. As an example, SGI and ANSYS set a new record by running an ANSYS Fluent simulation on over 145,000 CPU cores, significantly reducing the simulation time.

1. 1
Meeting Manufacturing’s Need for
Production Supercomputing
Tony DeVarco
Director of Virtual Product Design
Manufacturing Solutions

2. 2©2016 SGI

3. 3©2016 SGI
Challenges Manufacturing Companies Are Facing
Image Courtesy of ANSYS
• The need to increase engineering productivity.
• Speed up product development time.
• Reduce physical prototyping to save time and costs.
• Inefficient use of expensive ISV licenses.
• Need to maintain multiple geographically distributed engineering
facilities.
• Distributed workstations cannot interact with computer resources and
data in the corporate data centers.
• Need to replace workstations with low-cost, remote clients.

4. 4©2016 SGI
Pre/Post
Model and Mesh
Creation and
Visualization
CEM
Computational
Electromagnetic
s
CFD
Computational
Fluid Dynamics
CSM*
Explicit
Workload Scheduling
SGI® Management Center and SGI® Performance Suite
SGI® Scale-up and Scale-out Computing Solutions
Workload Scheduling Tool
Linux OS
Nastran
ANSYS® Mechanical
ABAQUS/Standard
ADINA
Permas
LS-DYNA
PAM-CRASH
RADIOSS
ABAQUS
Madymo
ANSYS® FLUENT
OpenFOAM®
StarCCM+
PowerFLOW
CFD++
Pam-Flow
FEKO
ANSYS® HFSS
CST
FMSlib
ANSA
Hypermesh
Gambit
Patran
Altiar Hyperworks
ANSYS EKM
SimManager
ABAQUS/CAE
d3View
SDM
Simulation Data
Management
CSM*
Implicit
Nastran
ANSYS® Mechanical
ABAQUS/Standard
ADINA
Permas
Altair RADIOSS
LS-DYNA
PAM-CRASH
ABAQUS
Madymo
Altair AcuSolve
ANSYS® FLUENT
OpenFOAM®
StarCCM+
PowerFLOW
ANSYS® HFSS
Altair FEKO
CST
ANSYS Maxwell 3D
Altair Hypermesh
ABAQUS/CAE
ANSA
Gambit
Patran
Altiar Hyperworks
ANSYS EKM
SimManager
d3View
SGI® Vizserver®
SGI Hardware 3rd Party Software CAE Segments
* CSM Is Computational Structural Mechanics
SGI Software SGI Services
SGIServices
SGI® Solution Environment and
CAE Application Segments

5. 5
SGI and ANSYS
Scaling Fluent 17.2 to a New
Record of 145,152 cores

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SGI ICE XA System Used
SGI and ANSYS
Scaling Fluent 17.2 to a New Record of 145,152 Cores
• A great example using a balanced production supercomputer is illustrated in a
recent setting of a new commercial CFD benchmark for the widely-used ANSYS
application.
• Specifically, ANSYS and SGI application engineers worked together to achieve
a new world record, scaling ANSYS Fluent® on SGI® ICE™ XA, which is one of
the world’s fastest commercial distributed-memory supercomputer platforms.

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0
20000
40000
60000
80000
100000
120000
140000
160000
0 20000 40000 60000 80000 100000 120000 140000 160000
Speedup
Cores
Combustor 830M -- Fluent 17.2
ICE-XA -- E5-2697v4 -- 2.30 GHz Linear Scaling
Gas Combustor 830M cell Model, Courtesy of ANSYS
In the test, SGI ran Fluent on 145,152 CPU cores, which is
over 16,000 more cores than the previous record. The benefit
of scaling to this level is that the total wall clock time to
complete a simulation can be significantly reduced. In this
case, a single simulation was completed in 13 seconds. In
contrast, the same simulation run on 1,296 cores took 20
minutes.

8. 8
An industry example that
highlights the advantages of
scaling CFD workloads on a
SGI® UV™ System

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SGI® Origin 2000, 1996
MIPS R10K
SGI Origin 3000, 1999
MIPS R12K
SGI® Altix® 3000, 2003
Intel Itanium
SGI Altix 4700, 2006
Intel Itanium SGI UV 1000, 2010
Intel E7
SGI UV 300, 2014
Intel E7
SGI UV 2000/3000, 2012/2015
Intel E5
Seven Generations of Shared Memory Systems:
1996–2015

10. 10
The Best of DMP and SMP in One Cache-Coherent System with One OS
SGI®
InfiniteStorage™
NVIDIA®
K5200 or M6000
Multiple Users and
Multiple Jobs
Preprocessing,
Mesh Generation
and Model
Decomposition
Running Solvers
Post Processing and
Visualization
UV rack layout for illustration purposes only.
Consolidate Workloads into
One Easy-to-Administer System

11. 11©2016 SGI
Image courtesy London Computational Solutions
• London Computational Solutions, headed by Mark Taylor,
former Head of CFD at McLaren F1, is working
with Elemental Cars, an advanced track car manufacturer.
• The goal of this effort is to improve the cornering speed of
its RP1 car by using design elements to create an
aerodynamic downforce that increases the vertical force on
the car’s tires creating more grip with the road.
Elemental Cars

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• “When I was Principal Aerodynamicist for McLaren F1 Racing, and now as
CEO of London Computational Solutions (LCS),” states Mark Taylor, “I
relied on the SGI® UV™ shared memory platform to deliver the
robustness, reliability, and efficiency to scale our CFD simulations to
meet a tight manufacturing deadline and our aerodynamic performance
targets.”
• “When LCS was presented with the aerodynamic challenge to improve the
cornering speeds for a new British road-legal track car called Elemental
RP1, I knew I could meet their requirements
of a quick turnaround because the running
of our CFD software on SGI UV would
perform as advertised and just work.”
Image courtesy London Computational Solutions
Elemental Cars

13. 13©2016 SGI
Examples of a fifth order accurate simulation of a full automotive car geometry.
Please have insideHPC put this
Top View animaRon here
Please have insideHPC put this
Bo*om View animaRon here
Elemental Cars

14. 14©2016 SGI