NVIDIA GRID™ and Dassault Catia from Proof of Concept to Production April 5, 2016

Fred Devoir, Textron Mgr – IT Infrastructure

Chris Savage, Bell Helicopter Infrastructure Operations Mgr

Introductions

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Fred Devoir Manager – IT Infrastructure & Sr. Architect TEXTRON Inc.

Chris Savage Infrastructure Operations Manager Bell Helicopter

Bell Helicopter – Case Study & Benefits

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• Problem

– PLM Distributed architecture presents significant challenges to manufacturing engineering

– Replication of model data doesn’t account for designer changes that effect manufacturing because cached data is out of date.

• Solution

– Move PLM Data centralized only and serve out models with graphics accelerated VDI.

• Observations

– In general, 40% improvement in load times for large CAD/CAM models

• Benefits

– First time in history manufacturing quality engineers could do quality checks digitally at the supplier using VDI on tablets rather than paper printed CAD.

Physical workstation - Model Load Comparison

Local to Enovia Data (LAN <1ms) Remote to Enovia Data (WAN ~60ms)

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Pilot gear to Full Production 300+ users

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• POC: – OEM server evaluations (HCL) and functional testing

– Power/cooling concerns and density

• Pilot: – Deployment started August 2014, January 2015 Launch

– 16 GPU Servers

– 32 NVIDIA GRID K2 Cards

– Support for 120+ Catia Users.

• Production: – Deployed started January 2015, Q1 2016 Launch

– 54 GPU Servers

– 108 NVIDIA GRID K2 Cards

– Support for 300+ Catia Users.

– Support for 50 Ansys Users.

• GRID M60 Evaluations: – POC deployment July 2015

– 2 GPU Servers

– 4 NVIDIA GRID M60 Cards

– Support for 96 Catia Users (double the density of K2)

Bell Helicopter Typical Performance of Server 1 day : 7 day

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• Workday exhibited in graphs as hard offsets.

• Behaviorally Textron has room to improve server workload optimization during off-peak hours.

– Leverage environment for global engineering teams to get better utilization

• Network performance is not a limiting factor. 300+ users consume a total of <3Gbps at the core switch the entire stack is connected too.

– Each server consuming <30mbps

• Average server has 8 Engineers performing full assembly Catia manipulation

• Full assembly models roughly 40GB in RAM per user.

Textron Persona Overview – Engineering VDI

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• 80% of Bell Helicopter engineering users are large power user VMs • 10% Jumbo, 10% small or medium

Environment Considerations

• Use Citrix Provisioning Services (PVS) rather than Machine Creation Services (MCS)

– MCS is very disk IOP dependent

– PVS only caches the bits that are being used by the OS and the application

• Use local SSD WriteCache with Memory acceleration.

• Electrical Power consumption and density should be considered

• CCU vs Dedicated VMs

– Environment management is critical to end user experience.

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Typical Setup for Catia VMs

• NVIDIA vGPU profile “M60-1Q” and “K240Q” or “M60-2Q” and K260Q”

• 16-64GB RAM –depending on the size of the models

• 120GB base OS vDisk (Streamed from PVS)

• 250-750GB WriteCache vDisk (local SSD in the hypervisor)

• 2-4 vCPU – 2 is the minimum number of vCPUs since Catia is a single threaded application

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NVIDIA Graphics Driver - Global Settings

• Use the “Dassault Systems CATIA - Compatible” global preset inside the NVIDIA control panel.

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CATIA : Preselect in Geometry View

• Deselect “Preselect in geometry view”

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NOTE: If it is selected then there is constant cross-highlighting between 3D and Tree occurring. !

CATIA : Level of detail

• Set the “Static” at about 10% (4.0) and “While Moving” at about 75% (30)

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NOTE: This greatly increases the ability of the graphics cards to process the zoom, pan, and rotate inputs from the user.

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Resources

• NVIDIA Performance Engineering Labs NVIDIA GRID DASSAULT CATIA V5/V6 SCALABILITY GUIDE Published March 2016 http://www.nvidia.com/CATIA-Scalability-Guide

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