ow2con'14 - xlcoud, 3d rendering in the cloud, marius preda, institut mines telecom
TRANSCRIPT
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XLcloud – 3D Rendering in the Cloud
Open Stack Summit, Paris, November, 2014
Marius PREDA, TELECOM SudParis
Philippe GRAVEZ, CEA
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Summary
! Some words about the project ! Remote rendering principle and optimizations ! Remote Rendering middleware
! Demos
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Consortium ! ATEME: H264/MPEG-4/AVC low latency
compression algorithms
! Bull: cloud infrastructure, distributed systems architecture and HPC cluster
! CEA List: interactive simulation of natural phenomenons, virtual reality
! EISTI: marketplace, OpenERP integration
! Inria Reso: energy efficiency
! TSP (Artemis Lab): remote rendering, video compression, multimodality
! Silkan: massively distributed (HPC) software architecture for interactive simulation
! OW2: communication, dissemination
Origin of the project
Winner of 1st Call for Projects “Cloud Computing” by the FSN in 2011
! Project length: 36 months ! Official Start: 01/01/2012 ! Coordinator: Bull
! Open Source (Apache V2) ! http://gitorious.ow2.org/xlcloud ! http://xlcloud.org/
! http://gitorious.ow2.org/xlcloud
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XLcloud objective
Establish the demonstration of a High Performance Cloud Computing (HPCC) platform designed to run effectively a
representative set of compute intensive workloads including interactive games and 3D graphics applications
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Capabilities of the XLcloud platform ! The platform exposes an on-demand Platform-as-a-Service service
endpoint (and GUI) which developers can use to manage software stacks of nearly any scale and complexity without sacrificing control
! The platform's main design paradigm is to make it easy to manage the complete application lifecycle, including resource provisioning, configuration management, application deployment, software updates, monitoring, and access control.
! The software stack artefacts produced by XLcloud are primarily targeted toward delivering two kinds of virtual clusters, but could easily be extended to produce other types of virtual platforms: ● An HPC Virtual Cluster based on SLURM
● A Remote Rendering Virtual Cluster based on Ogre3D Today’s(topic(
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Remote Rendering
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Scene%analyzer%
User(interac1on(
Scene(descrip1on((
Data(AI(
Local(Input(
Network(parameters(
Remote(Input(
Segmenta1on(map(
Depth(map(
Mo1on(vectors(
Applica.on%Engine%
Scene(Graph(
Rendering%engine%
2D/3D(graphics(
Video%Encoder%
Graphics%Encoder%
RTP/UDP%Packe.zer%
%
Streamer%
Network(ready(packets(
Compressed(stream(
Player%
Joueur(
Interac1vity(
Terminal(parameters(
Network%
Server(
Client(
Network%
How%remote%rendering%works?%
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Scene%analyzer%
User(interac1on(
Scene(descrip1on((
Data(AI(
Local(Input(
Network(parameters(
Remote(Input(
Segmenta1on(map(
Depth(map(
Mo1on(vectors(
Applica.on%Engine%
Scene(Graph(
Rendering%engine%
2D/3D(graphics(
Video%Encoder%
Graphics%Encoder%
RTP/UDP%Packe.zer%
%
Streamer%
Network(ready(packets(
Compressed(stream(
Player%
Joueur(
Interac1vity(
Terminal(parameters(
Network%
Server(
Client(
Network%
How%remote%rendering%works?%
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Scene%analyzer%
User(interac1on(
Scene(descrip1on((
Data(AI(
Local(Input(
Network(parameters(
Remote(Input(
Segmenta1on(map(
Depth(map(
Mo1on(vectors(
Applica.on%Engine%
Scene(Graph(
Rendering%engine%
2D/3D(graphics(
%%
Video%Encoder%
Graphics%Encoder%
RTP/UDP%Packe.zer%
%
Streamer%
Network(ready(packets(
Compressed(stream(
Player%
Joueur(
Interac1vity(
Terminal(parameters(
Network%
Server(
Client(
Network%
How%remote%rendering%works?%
xlcloud(added(value(
Transmoder%
Depth%MAP%Quality%control%
Buffers%extrac.on%
op.miza.ons%
PreJProcessing%
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Scene%analyzer%
User(interac1on(
Scene(descrip1on((
Data(AI(
Local(Input(
Network(parameters(
Remote(Input(
Segmenta1on(map(
Depth(map(
Mo1on(vectors(
Applica.on%Engine%
Scene(Graph(
Rendering%engine%
2D/3D(graphics(
%%
Video%Encoder%
Graphics%Encoder%
RTP/UDP%Packe.zer%
%
Streamer%
Network(ready(packets(
Compressed(stream(
Player%
Joueur(
Interac1vity(
Terminal(parameters(
Network%
Server(
Client(
Network%
How%remote%rendering%works?%
xlcloud(added(value(
Transmoder%
Depth%MAP%Quality%control%
RoA%Quality%control%
Region(Of((AKen1on(Map(
Buffers%extrac.on%
op.miza.ons%
PreJProcessing%
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Examples of applications in Remote Rendering
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The problem that the Cloud can solve for Remote Rendering
Video(bitrate(Network(Bitrate((kb/s)(
Time((s)(
Frozen(image(
Frozen(image(
Frozen(image(
Frozen(image(
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Reduce the video bitrate to keep it under the network bitrate
Network(Bitrate((kb/s)(
Time((s)(
The problem that the Cloud can solve for Remote Rendering
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The objective is simple, the results when applying traditional rate control are just … horrible
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Problem Statement
! Find new representation spaces of video sequences that allows to reduce the debit beyond the theoretical limit of the pixel space while preserving the user experience
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Proposed Approach
! Statistically detect regions of interest (ROI)
! Create importance maps
! Concentrate the coding error in “less important” areas
! Correlate importance maps with the characteristics of the image, measured in real time
! Perform a segmentation of the image in relation to these characteristics
! Build a hybrid space
! Compress the hybrid space
! Transmit, decode and display the rendered hybrid space
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What is the “important” info?
Player’s attention is game specific and it depends on the graphical content, action, interaction
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What is the “important” info? “Ask” the user …
5 players, 2 games (Doom3 and 0AD), 1 eye-tracker and many playing hours
To get this …
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What is the “important” info? “Ask” the user …
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Intra-player analysis
Strong correlation if rp>0,5
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Inter-player analysis
Doom3, rp > 0,85 (between ¯xDoom3 and all the tests)
0 A.D., rp > 0,69 (between ¯x0AD and all the tests)
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Region of Attention Model
Modulate the quality of the video encoding based on RoA
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Additional control: adaptive video coding based on depth map
! Control of region quality based on distance from viewpoint ! Inverse exponential function to correct non-linear depth values ! Variable range support
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"↓5 , %↓2 (
"↓6 , %↓1 ("↓3 ,
%↓1 (
"↓1 , %↓3 (
"↓2 , %↓2 (
"↓4 , %↓2 (
"↓7 , %↓4 (
Displacing the errors in the video frame is fine, however it has theoretical limits imposed by the “Pixel” space
! Additional improvement: represent the image by using two spaces
! Regions containing edges – represented as color pixels
! Edge free regions – represented as polynomial coefficients
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Hybrid Space – Visual Results Original%Image%
Hybrid%Space%
Pixel%Informa.on%
Polynomial%Informa.on%
Reconstructed%Image%
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Remote Rendering Middleware
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Node(2(Node(1(
Remote Rendering – 3 Layers structure
Lobby(
Lobby(Agent( Lobby(Agent(
RR(Session(1(
RR(Session(2(
RR(Session(3(
RR(Session(1(
RR(Session(2(
RR(Session(3(
Applica.on%Catalog%
Client%
Remote%Rendering%Virtual%Cluster%
! Cluster : Lobby Component ! Node : Lobby Agent Component ! Application
Load%Table%
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Remote Rendering – 3 Layers Structure ! Sequence Diagram for Remote Rendering Session Submission
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Node(1(
Remote Rendering – 3 Layers Structure Lobby(
Lobby(Agent(
X(server(
Applica1on(1(
Applica.on%Catalog%
Client%
Remote%Rendering%Virtual%Cluster%
! Session Initialization
! X Server launch
! RR Middleware components launch
! Application launch
Load%Table%
Streaming(server(
X(server(
Applica1on(2(
Streaming(server(
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Remote Rendering Middleware - Components ! Remote Rendering Library ! Graphics Engine Video Output (GEVO) ! Interaction Module (NetInput) ! Open Source Graphics Engine (OGRE) ! Ogre-GEVO Plugin ! Video Encoder ! Local Dependencies ! Streaming Server ! Lobby Agent
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RR Middleware – Application Integration
RR%Middleware%
RR(Dependencies(
Remote%Rendering%Library%
GEVO%J%RRStream% Interac.vity%(NetInput)%
Video%Encoder%
Ogre%–%GEVO%Plugin%
3D%Applica.on%Ogre%Applica.on%
Streaming%Server%
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Demo
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Games
4(sessions,(1280x(720(Nvidia(K340(grid(Intel(Xeon(2(cores(
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Games
4(sessions,(1280x(720(Nvidia(K340(grid(Intel(Xeon(2(cores(
CPU(
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Games
4(sessions,(1280x(720(Nvidia(K340(grid(Intel(Xeon(2(cores(
CPU(
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Games
4(sessions,(1280x(720(Nvidia(K340(grid(Intel(Xeon(2(cores(
GPU(
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Games
4(sessions,(1280x(720(Nvidia(K340(grid(Intel(Xeon(2(cores(
GPU(
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Games
4(sessions,(1280x(720(Nvidia(K340(grid(Intel(Xeon(2(cores(
Network(
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Games
4(sessions,(1280x(720(Nvidia(K340(grid(Intel(Xeon(2(cores(
Network(
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! A demonstration of a High Performance Cloud Computing platform
! More computation is available, better compression is possible ! Modulate the compression quality with respect to user attention and
application behavior
! Generate in real time complementary representation spaces for images
Concluding Remarks
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XLCloud: the interventional
radiology use case
OW2con’14 Paris, 5 November 2014
Philippe Gravez, Virtual Reality Project Manager, CEA LIST, FR
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XLCloud: the interventional radiology use case
Interactive simulation / Virtual Reality for training / Cloud Introduction
CEA List & technological research I. CEA List & XLCloud
Interactive simulation Motivations for XLCloud 2 use cases
II. Interventional radiology What is it? Esprimed VR training tool
III. Implementation Architecture of XDE applications Remote deployment
Conclusion
| PAGE 2
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CEA List research institute Smart digital systems – Paris area – Staff ~ 750
| PAGE 3
Contribute to business competitiveness through innovation and technology transfer
41%
34%
25%
Funding
Project Industrial CEA
FROM RESEARCH TO INDUSTRY
FUNDAMENTAL RESEARCH
PUBLICATIONS
APPLIED RESEARCH
PATENTS
PRODUCTS
MASS PRODUCTION
Microcavity
Laser diode pump
Contacts
10 mm Microcavity
Laser diode pump
Contacts
10 mm
PILOT LINE
PROTOTYPES
Funding
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XLCloud: the interventional radiology use case
Interactive simulation / Virtual Reality for training / Cloud Introduction
CEA List & technological research I. CEA List & XLCloud
Interactive simulation Motivations for XLCloud 2 use cases
II. Interventional radiology What is it? Esprimed VR training tool
III. Implementation Architecture of XDE applications Remote deployment
Conclusion
| PAGE 4
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Interactive dynamic simulation
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Interactive simulation lab: addressed topics
| PAGE 6
Physics
Soft parts Fluids Multibody multi-contact nonsmooth dynamics
Dynamic multi- objective control
Dynamic simulation, Interactions
Interactive multi-physics
simulation 1
Digital human
2
Accurate manipulation
in VR
Interactive Dosimetry
«Advanced» interactions: haptics,
tracking, HMD, …
Mixed Reality
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Interactive simulation lab: some applications
| PAGE 7
Physics
Interactive multi-physics
simulation 1
Digital Human
2 Assistance for assembling,
maintenance or recycling
Ergonomic assessment,
cobot design …
Training (VR / MR) Virtual prototyping, scenario validation
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Motivations for XLCloud / Use cases
XLCloud HPC Interactive video streaming
Access to HPC capabilities CFD & dosimetry simulation
High interactivity Virtual Reality requirement
Ease of deployment Collaborative working / scalability
Several persons, remotely located, interacting with the same virtual mock-up from heterogeneous computer interfaces
! 2 XLCloud use cases Interactive smoke simulation (with Silkan) (HPC++) Interventional radiology (with Esprimed) (Interactivity++) | PAGE 8
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XLCloud: the interventional radiology use case
Interactive simulation / Virtual Reality for training / Cloud Introduction
CEA List & technological research I. CEA List & XLCloud
Interactive simulation Motivations for XLCloud 2 use cases
II. Interventional radiology What is it? Esprimed VR training tool
III. Implementation Architecture of XDE applications Remote deployment
Conclusion
| PAGE 9
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Interventional radiology
" Interventional radiology is a medical sub-specialty of radiology which utilizes minimally-invasive image-guided procedures to diagnose and treat diseases in nearly every organ system (Wikipedia)
" This means that surgeon and medical staff are always exposed to X-rays
" This is becoming a world wide issue raised by safety authorities
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The Esprimed interventional radiology training tool
| PAGE 12
© Data / scenario!!
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XLCloud: the interventional radiology use case
Interactive simulation / Virtual Reality for training / Cloud Introduction
CEA List & technological research I. CEA List & XLCloud
Interactive simulation Motivations for XLCloud 2 use cases
II. Interventional radiology What is it? Esprimed VR training tool
III. Implementation Architecture of XDE applications Remote deployment
Conclusion
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XDE architecture
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XDE virtual cluster with XLCloud / scalability
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Network of Orocos agents
Remote rendering
According to the considered application and the local resources, select the components to be virtualized
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Conclusion
Development of a training tool for interventional radiology A business supported use case Main motivation: ease of deployment in hospital facilities Open a path toward scalability and collaborative work
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