Implementing NVIDIA GRID Solutions in Unmanned Vehicle Ground Control Systems

Transcription

1 Implementing NVIDIA GRID Solutions in Unmanned Vehicle Ground Control Systems March 2016 Nathan Wincey Copyright 2016 Lockheed Martin Corporation: No part of this publication may be reproduced or transmitted in any form, or by any means, electronic or mechanical, including photocopy, recording, or any information storage and retrieval system, without written permission form Lockheed Martin. 1

2 Outline Objective Summary SWaP Improvements Development Timeline Open Architecture Benefits Built in Training/Simulation Challenges Faced Solutions to Challenges 2

3 Objective Summary To Provide an Transportable Ground Control Station with an extreme amount of computing power in the smallest functional building block to: Command/Control multiple UAV(s) simultaneously. Process downlinked imagery data. Host built-in training/simulation/stimulation capabilities (Prepar3D, MetaVR TM, SpeedGoat, etc ). Be configurable to perform various number of user defined tasks quickly (Image Exploitation, SIGNIT Analysis, etc..). 3

4 SWaP Improvements Mission Control Element (Supporting Six Crew Stations) Eight 4U Cases (4 Processing, 4 Power) Total Weight: ~1200lbs Total Pwr: 6KVA Two 4U Cases (1 Processing, 1 Power) Total Weight: ~300lbs Total Pwr: 3KVA 4

5 GPU Hardware GPU Technologies POCs / Products Development Timeline TACS 1.0 Imperium xgcs VMware vsga Beta TACS 1.5 Citrix/nVidia vgpu Beta VMware/nVidia vgpu Beta Bare Metal GPU Pass-through (Windows/Linux) (Linux only) VMware vsga Citrix/nVidia vgpu VMware/nVidia vgpu Quadro 4000 FirePro 7900 Tesla M60 GRID K2 5

6 Open Architecture Benefits Standards Use of Open Standards (Comm Protocol - TCP/IP, Bus Interface SAS/SATA, Bus Standard PCIe, Hardware COTS, Form Factor, etc.) Modular Support for multiple deployment scenarios Replication of components using exact copies Configurable Industrial COTS to Rugged based on cost/schedule/purpose Ability to add performance as component technology advances or component costs decrease Scalable Ability to add to the system as system needs increase Other Highly Available Configuration Node/Server System; Disk System - RAID1 (VMs) and RAID5 (File Share); Power System 6

7 Built in Training/Simulation 3D Scene Generators (requires DirectX 11) MetaVR TM VRSG TM (Virtual Reality Scene Generator) Provides simulated UAV camera payload video with embedded KLV metadata for operator training. Allows the GCS hardware to be used for self contained embedded training when not flying real missions. Prepar3D Is a foundational technology used on many of Lockheed Martin s advanced training programs (including the JSF F-35, C-130J, and F-16 aircraft). Looking at ways to integrate this training/simulation capability into Fury UAS specific scenarios hosted on the Imperium xgcs. 7

8 Challenges VMware vsga has limited DirectX/OpenGL support and GPU horsepower. Thin Client device testing resulted in poor performance of quad monitor setups with live video streams and 3D graphic applications running simultaneously. Using the standard K260Q vgpu profile for a quad monitor setup didn t allow a high enough VM density to be feasible for our use case (at least 4 VMs per GPU socket required). Convert high performance 3D graphic Linux machines into VMs. 8

9 Challenge Solutions - vsga MaxAppFrameRate and Win32FrameRate Reg Settings Modifying these frame rate limits helped in improving the overall FPS performance of the vsga VMs when running multiple 3D applications. Modify PCoIP settings Reduce maximum initial image quality FastSendDatagramThreshold modification (latest version of View Agent uses recommended value by default now) Modifying Windows Theme and Desktop Composition settings Changing Windows GUI settings aided in reducing the VRAM and GPU load on vsga which delivered a smoother and higher performing application experience. Replace vsga with NVIDIA GRID vgpu technology When applications require full DirectX/OpenGL support vgpu provides this support as well as increased GPU horsepower from it s native nvidia driver that interfaces directly with the GPU hardware versus the API intercept method like vsga. 9

10 Challenge Solutions Client Devices PCoIP Tera2 Zero Clients (current implementation) Found to provide the highest level of FPS performance in the smallest SWaP footprint. Modify PCoIP reg settings for increased Multi-monitor performance. Increased Window dragging performance across monitors pcoip.enable_new_color_coding 0 pcoip.enable_temporal_image_caching 0 Testing new Quad monitor Thin Client (future client option) HP t730 Native Quad 4K monitor support AMD RX-427BB APU (2.7 GHz, 4 cores) 10

11 Challenge Solutions vgpu Profile Add VM specific.vmx parameter settings vgpu VMs that require 1 GB VRAM for apps used K240Q profile (2X VM Density vs K260Q profile) pcipassthru0.cfg.num_heads 4 pcipassthru0.cfg.max_resolution 1920x1080 vgpu VMs that require 512 GB VRAM for apps used K220Q profile (4X VM Density vs K260Q profile) pcipassthru0.cfg.num_heads 4 pcipassthru0.cfg.max_resolution 1920x

12 Challenge Solutions 3D Linux VM Traditionally use GPU Pass-through for 3D Linux VMs Works, but is 1:1 mapping no consolidation. Linux vgpu Current testing is on going, but vgpu performance appears comparable to Windows vgpu performance. Supports native access from Thin clients with VMware Blast Extreme. Doesn t support native Zero Client access with PCoIP. 12

13 Summary Conclusion Utilizing a Virtual Machine architecture with nvidia s vgpu feature set has allowed the penetration of high performance 3D intensive UAV Systems into the virtual machine realm. This has allowed these systems to significantly reduce SWaP, remain rapidly scalable in 3D intensive environments, and provide openarchitecture solutions in cost-effective packages. More Information: Fury UAS: SharkFin Software: Imperium xgcs: Prepar3D Software: Contact Info: Nathan.b.wincey@lmco.com 13

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