GPU Enhanced Remote Collaborative Scientific Visualization...ORNL is managed by UT-Battelle LLC for...

ORNL is managed by UT-Battelle LLC for the US Department of Energy

GPU Enhanced Remote Collaborative Scientific VisualizationBenjamin Hernandez (OLCF), Tim Biedert (NVIDIA)

March 20th, 2019

This research used resources of the Oak Ridge Leadership Computing Facility at the Oak Ridge National Laboratory, which is supported by the Office of Science of the U.S. Department of Energy under Contract No. DE-AC05-00OR22725.

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Contents

• Part I– GPU Enhanced Remote Collaborative Scientific Visualization

• Part II– Hardware-accelerated Multi-tile streaming for Realtime Remote

Visualization

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Oak Ridge Leadership Computing Facility (OLCF)

• Provide the computational and data resources required to solve the most challenging problems.

• Highly competitive user allocation programs (INCITE, ALCC). – OLCF provides 10x to 100x more resources than other centers

• We collaborate with users sharing diversity in expertise andgeographic location

4

How are these collaborations ?

• Collaborations– Extends through the life cycle of the

data from computation to analysis and visualization.

– Are structured around data.

• Data analysis and Visualization – An iterative and sometimes remote

process involving students, visualization experts, PIs and stakeholders

Simulation

Pre-visualization

Evaluation

Visualization

Discovery

Team needs

PI feedback

Viz. expert feedback

5

How are these collaborations ?

• The collaborative future must be characterized by[1]:

1. U.S. Department of Energy.(2011). Scientific collaborations for extreme-scale science (Workshop Report). Retrieved from https://indico.bnl.gov/event/403/attachments/11180/13626/ScientificCollaborationsforExtreme-ScaleScienceReportDec2011_Final.pdf

DiscoveryResources easy

to find

ConnectivityNo resource is an

island

PortabilityResources widely and transparently usable

CentralityResources efficiently,

and centrally supported

“Web-based immersive visualization Tools with the ease of a virtual reality game.”

“Visualization ideally would be combined with user-guided as well as template-guided automated feature extraction, real-time annotation, and quantitative geometrical analysis.”

“Rapid data visualization and analysis to enable understanding in near real time by a geographically dispersed team.”

…

6

How are these collaborations ?

• INCITE “Petascale simulations of short pulse laser interaction with metals” PI Leonid Zhigilei, University of Virginia– Laser ablation in vacuum and liquid environments– Hundreds of million to billion scale atomistic simulations, dozens of time steps

• INCITE “Molecular dynamics of motor-protein networks in cellular energy metabolism” PI(s) Abhishek Singharoy, Arizona State University– Hundreds of million scale atomistic simulation, hundreds of time steps

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How are these collaborations ?

• The data is centralized in OLCF

• SIGHT is a custom platform for interactive data analysis and visualization.– Support for collaborative features are needed for

• Low latency remote visualization streaming• Simultaneous and independent user views• Collaborative multi-display setting environment

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SIGHT:Exploratory Visualization of Scientific Data

• Designed around user needs

• Lightweight tool– Load your data– Perform exploratory analysis– Visualize/Save results

• Heterogeneous scientific visualization– Advanced shading to

enable new insights into data exploration.

– Multicore and manycore support.

• Remote visualization– Server/Client architecture to provide high

end visualization in laptops, desktops, and powerwalls.

• Multi-threaded I/O• Supports interactive/batch

visualization– In-situ (some effort)

• Designed having OLCF infrastructure in mind.

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PublicationsM. V. Shugaev, C. Wu, O. Armbruster, A. Naghilou, N. Brouwer, D. S. Ivanov, T. J.-Y. Derrien, N. M. Bulgakova, W. Kautek, B. Rethfeld, and L. V. Zhigilei, Fundamentals of ultrafast laser-material interaction, MRS Bull. 41 (12), 960-968, 2016.

C.-Y. Shih, M. V. Shugaev, C. Wu, and L. V. Zhigilei, Generation of subsurface voids, incubation effect, and formation of nanoparticles in short pulse laser interactions with bulk metal targets in liquid: Molecular dynamics study, J. Phys. Chem. C 121, 16549-16567, 2017.

C.-Y. Shih, R. Streubel, J. Heberle, A. Letzel, M. V. Shugaev, C. Wu, M. Schmidt, B. Gökce, S. Barcikowski, and L. V. Zhigilei, Two mechanisms of nanoparticle generation in picosecond laser ablation in liquids: the origin of the bimodal size distribution, Nanoscale 10, 6900-6910, 2018.

1010

SIGHT’s System Architecture

SIGHT ClientWeb browser - JavaScript

OLCF

SIGHT

Mul

ti-th

read

ed

Dat

aset

Par

ser Ray Tracing

Backends1

NVIDIAOptix

IntelOSPray

Dat

a Pa

ralle

l A

naly

sis2

Frame ServerWebsockets

SIMD ENCTurboJPEG

GPU ENCNVENC NVPipe

1S7175 Exploratory Visualization of Petascale Particle Data in Nvidia DGX-1Nvidia GPU Technology Conference 20172P8220 Heterogeneous Selection Algorithms for Interactive Analysis of BillionScale Atomistic Datasets

Nvidia GPU Technology Conference 2018

11

Node(s)

Design of Collaborative Infrastructure - Alternative 1

SIGHT

User 1

Node(s)

Data

SIGHT

Node(s)

SIGHT

User 3User 2

12

Job ?

Job 2Job 1

Node(s)

Design of Collaborative Infrastructure - Alternative 1

SIGHT

User 1

Node(s)

Data

SIGHT

User 2

Job 3

Node(s)

SIGHT

User 3

13

Job 1

Job 3Job 2Job 1

Node(s)

Design of Collaborative Infrastructure - Alternative 1

SIGHT

User 1

Node(s)

Data

SIGHT

Node(s)

SIGHT

User 3User 2

! !

Co-scheduling !Job coordination !Communication !

14

Job 1

Design of Collaborative Infrastructure - Alternative 2

Node(s)

SIGHT

User 1

Data

User 3User 2

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Design of Collaborative Infrastructure

• ORNL Summit System Overview– 4,608 nodes– Dual-port Mellanox EDR InfiniBand network– 250 PB IBM file system transferring data at

2.5 TB/s

• Each node has– 2 IBM POWER9 processors – 6 NVIDIA Tesla V100 GPUs – 608 GB of fast memory (96 GB HBM2 + 512

GB DDR4) – 1.6 TB of NV memory

Node(s)

SIGHT

User 1

Data

User 3User 2

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Design of Collaborative Infrastructure

• Each NVIDIA Tesla V100 GPU– 3 NVENC Chips– Unrestricted number of concurrent

sessions

• NVPipe– Lightweight C API library for low-latency

video compression

– Easy access to NVIDIA's hardware-accelerated H.264 and HEVC video codecs

1717

GUI Events

Enhancing SIGHT Frame Server Low latency encoding

Frame ServerWebsockets

NVENC

Ray Tracing Backend

SIGHT ClientWeb browser - JavaScript

(x, y, button)KeyboardGUI Events

Sharing Optix buffer (OptiXpp)

Buffer frameBuffer = context->createBuffer( RT_BUFFER_OUTPUT, RT_FORMAT_UNSIGNED_BYTE4, m_width, m_height );

frameBufferPtr = buffer->getDevicePointer(optxDevice);...

compress (frameBufferPtr);Framebuffer

Visualization Stream

(x, y, button)Keyboard

1818

Enhancing SIGHT Frame Server Low latency encoding

Opening encoding session:

myEncoder = NvPipe_CreateEncoder (NVPIPE_RGBA32,NVPIPE_H264, NVPIPE_LOSSY, bitrateMbps * 1000 * 1000, targetFps);

if (!m_encoder)return error;

myCompressedImg = new unsigned char[w*h*4];

Framebuffer compression:

bool compress (myFramebufferPtr){...myCompressedSize = NvPipe_Encode(myEncoder,

myFramebufferPtr, w*4, myCompressedImg, w*h*4, w, h, true);

if (myCompressedSize == 0 )return error;

…}

Closing encoding session:

NvPipe_Destroy(myEncoder);

GUI EventsFrame Server

Websockets

NVENC

Ray Tracing Backend

SIGHT ClientWeb browser - JavaScript

Framebuffer

Visualization Stream

(x, y, button)Keyboard

CameraParameters

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Enhancing SIGHT Frame Server Low latency encoding

• System Configuration:– DGX-1 Volta– Connection Bandwidth

• 800Mbps (ideally 1 Gbps)

– NVENC Encoder• H264 PROFILE BASELINE• 32 MBPS, 30 FPS

– Turbo JPEG• SIMD Instructions on• JPEG Quality 50

– Decoder• Broadway.js (FireFox 65)• Media Source Extensions (Chrome 72)• Built-in JPEG Decoder (Chrome 72)

NVENC NVENC+MP4 TJPEGEncoding HD (ms) 4.65 6.05 16.71Encoding 4K (ms) 12.13 17.89 51.89Frame Size HD (KB) 116.00 139.61 409.76Frame Size 4K (KB) 106.32 150.65 569.04

Average

Broadway.cs MSE Built-in JPEG Decoding HD (ms) 43.28 39.97 78.15Decoding 4K (ms) 87.40 53.10 197.63

20

Enhancing SIGHT Frame Server Low latency encoding

21

Enhancing SIGHT Frame Server Low latency encoding

22

Enhancing SIGHT Frame Server Simultaneous and independent user views

• A Summit node can produce 4K visualizations with NVIDIA Optixat interactive rates.

User 1 User 2

User 3 User 4

4K

HD HD

HD HD

EVEREST

User A User B16:9 16:9

32:9

23

Enhancing SIGHT Frame Server Simultaneous and independent user views

2424

Thread 0 Frame Server

Enhancing SIGHT Frame Server Simultaneous and independent user views

Ray Tracing Backend

User 1

(x, y, button)KeyboardGUI Events

User 2

User 3

GUI Event queue

Thread 1 Frame Server

Thread 2 Frame Server

CameraParameters

(Usr Id)

Frame Buffer queue Viz. frame

(Usr Id)

Viz. frame

25

Enhancing SIGHT Frame Server Simultaneous and independent user views

• Video

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Discussion

• Further work– Multi-perspective/orthographic

projections– Traditional case: stereoscopic projection– Annotations, sharing content between

users, saving sessions

• How AI could help to improve remote visualization performance:

– NVIDIA NGX Tech

• AI Up-Res – Improve compression rates – Different resolutions

• Render and stream @ 720p, decoding at HD, 2K, 4K according to each user display

• AI Slow-Mo– Render at low framerates

• Optix Denoiser– Ray tracing converge faster

27

Thanks!

Benjamin HernandezAdvanced Data and Workflows Group

Oak Ridge National Laboratoryhernandezarb@ornl.gov

• AcknowledgmentsThis research used resources of the Oak Ridge Leadership Computing Facility at the Oak Ridge National Laboratory, which is supported by the Office of Science of the U.S. Department of Energy under Contract No. DE-AC05-00OR22725.

Datasets provided by Cheng-Yu Shi and Leonid Zhigilei, Computational Materials Group at University of Virginia, INCITE award MAT130.

Tim Biedert, 03/20/2019

HARDWARE-ACCELERATED MULTI-TILE STREAMING FOR REALTIME REMOTE VISUALIZATION

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INSTANT VISUALIZATION FOR FASTER SCIENCE

TraditionalSlower Time to

Discovery

CPU Supercomputer Viz Cluster

Simulation- 1 Week Viz- 1 Day

Multiple Iterations

Time to Discovery =

Months

Tesla

PlatformFaster Time to

Discovery

GPU-Accelerated Supercomputer

Visualize while you

simulate/without

data transfers

Restart Simulation Instantly

Multiple Iterations

Time to Discovery = Weeks

Days

Data Transfer

• Interactivity

• Scalability

• Flexibility

Value Proposition

3

CO-PROCESSINGPARTITIONED

SYSTEMLEGACY

WORKFLOW

SUPPORTING MULTIPLE VISUALIZATION WORKFLOWS

Separate compute & vissystem

Communication via file system

Compute and visualization on same GPU

Communication via host-device transfers or memcpy

Different nodes for different roles

Communication via high-speed network

4

VISUALIZATION-ENABLED SUPERCOMPUTERS

http://blogs.nvidia.com/blog/2014/11/19/gpu-in-

situ-milky-way/

CSCS Piz Daint NCSA Blue Waters

Galaxy formation

http://devblogs.nvidia.com/parallelforall/hpc

-visualization-nvidia-tesla-gpus/

ORNL Titan

Molecular dynamics Cosmologyhttp://www.sdav-scidac.org/29-

highlights/visualization/66-accelerated-cosmology-

data-anal.html

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GEFORCE NOW

You listen to music on Spotify. You watch movies on Netflix. GeForce Now lets you play games the same way.

Instantly stream the latest titles from our powerful cloud-gaming supercomputers. Think of it as your game console in the sky.

Gaming is now easy and instant.

ROAD TO EXASCALEVolta to Fuel Most Powerful US Supercomputers

1.641.50

1.39 1.41 1.37

1.7

1.41.5

V100 P

erf

orm

ance

Norm

alize

d t

o P

100

1.5X HPC Performance in 1 Year

Summit

Supercomputer

200+ PetaFlops

~3,400 Nodes

10 MegawattsSystem Config Info: 2X Xeon E5-2690 v4, 2.6GHz, w/ 2X Tesla P100 or V100.

6

VISUALIZATION TRENDSNew Approaches Required to Solve the

Remoting Challenge

Increasing data set sizes

In-situ scenarios

Interactive workflows

New display technologies

Globally distributed user bases

7

STREAMINGBenefits of Rendering on Supercomputer

Scale with SimulationNo Need to Scale Separate Vis Cluster

Cheaper Infrastructure All Heavy Lifting Performed on the Server

Interactive High-Fidelity Rendering Improves Perception and Scientific Insight

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FLEXIBLE GPU ACCELERATION ARCHITECTURE

* Diagram represents support for the NVIDIA Turing GPU family

** 4:2:2 is not natively supported on HW

*** Support is codec dependent

Independent CUDA Cores & Video Engines

9

CASE STUDY

Streaming of Large Tile Counts

Frame rates / Latency / Bandwidth

Synchronization

Comparison against CPU-based Compressors

Strong Scaling (Direct-Send Sort-First Compositing)

Hardware-Accelerated Multi-Tile Streaming for Realtime Remote Visualization

Tim Biedert, Peter Messmer, Tom Fogal, Christoph Garth

Eurographics Symposium on Parallel Graphics and Visualization (EGPGV) 2018 (Best Paper)

DOI: 10.2312/pgv.20181093

10

CONCEPTUAL OVERVIEWAsynchronous Pipelines

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BENCHMARK SCENESNASA Synthesis 4K

SpaceLow Complexity

OrbitMedium Complexity

IceHigh Complexity

StreamlinesExtreme Complexity

12

CODEC PERFORMANCE

13

BITRATE VS QUALITY

0,6

0,65

0,7

0,75

0,8

0,85

0,9

0,95

1

1 2 4 8 16 32 64 128 256

SSIM

Bitrate (90 Hz) [Mbps]

Space (H.264) Orbit (H.264) Ice (H.264) Streamlines (H.264)

Space (HEVC) Orbit (HEVC) Ice (HEVC) Streamlines (HEVC)

Structural Similarity Index (SSIM)

14

HARDWARE LATENCY

0

2

4

6

8

10

12

14

16

18

20

1 2 4 8 16 32 64 128 256

Late

ncy

[m

s]

Bitrate (90 Hz) [Mbps]

Encode (HEVC, Space)

Encode (HEVC, Orbit)

Encode (HEVC, Ice)

Encode (HEVC, Streamlines)

Encode (H.264, Space)

Encode (H.264, Orbit)

Encode (H.264, Ice)

Encode (H.264, Streamlines)

Decode (HEVC, Space)

Decode (HEVC, Orbit)

Decode (HEVC, Ice)

Decode (HEVC, Streamlines)

Decode (H.264, Space)

Decode (H.264, Orbit)

Encode HEVC - Encode H.264 - Decode HEVC - Decode H.264

15

HARDWARE LATENCY

0

2

4

6

8

10

12

14

16

3840x2160 2720x1530 1920x1080 1360x766 960x540 672x378 480x270

Late

ncy

[m

s]

Resolution

Encode (HEVC) Encode (H.264) Decode (HEVC) Decode (H.264)

Decreases with Resolution

16

CPU-BASED COMPRESSION

0

300

600

900

1200

1500

1800

2100

2400

0

20

40

60

80

100

120

HEVC(GPU)

H.264(GPU)

HEVC(CPU)

H.264(CPU)

TurboJPEG BloscLZ LZ4 Snappy

Ba

ndw

idth

[M

B/s]

Late

ncy

[ms]

Encode Decode Bandwidth

Encode/Decode Latency and Bandwidth

17

FULL TILESSTREAMING

18

N:N WITH SIMULATED NETWORK DELAYMean Frame Rates + Min/Max Ranges

0

20

40

60

80

100

120

0 ms 50 ms 150 ms 500 ms

Fra

me

Ra

te [

Hz]

Network Delay

1 2 4 8 16 32 64 128 256

Server: Piz DaintClient: Piz Daint

Ice 4K

H.264: 32 MbpsHEVC: 16 Mbps

Delay + 10% jitter

19

N:N STREAMINGPipeline Latencies

Server: Piz DaintClient: Piz Daint

Ice 4K

H.264: 32 Mbps

MPI-basedsynchronization

0

5

10

15

20

25

30

35

40

45

1 2 4 8 16 32 64 128 256

Late

ncy

[m

s]

Tiles

Synchronize (Servers) Encode Network Decode Synchronize (Clients)

20

N:1 STREAMINGClient-Side Frame Rate

Server: Piz DaintClients: Site A (5 ms)

Site B (25 ms)

Ice 4K

H.264: 32 Mbps

0

10

20

30

40

50

60

70

80

90

1 2 4 8 16 32 64 128 256

Fra

me

Ra

te [

Hz]

Tiles

Site A (1x GP100) Site B (1x GP100) Site B (2x GP100)

21

STRONG SCALING

22

N:1 STRONG SCALINGClient-Side Frame Rate

Server: Piz DaintClients: Site A (5 ms)

Site B (25 ms)

Ice 4K

H.264: 32 Mbps

0

50

100

150

200

250

300

350

400

1 2 4 8 16 32 64 128 256

Fra

me

Ra

te [

Hz]

Tiles

Site A (1x GP100) Site B (1x GP100) Site B (2x GP100)

23

N:1 STRONG SCALINGClient-Side Frame Rate For Different Bitrates

Server: Piz DaintClient: Site A (5 ms)

Ice 4K

H.264

0

20

40

60

80

100

120

140

160

180

200

1 2 4 8 16 32 64 128 256

Fra

me

Ra

te [

Hz]

Tiles

4 Mbps @ 90 Hz 72 Mbps @ 90 Hz 256 Mbps @ 90 Hz

24

EXAMPLE: OPTIX PATHTRACERRendering Highly Sensitive to Tile SizeServer: Piz Daint

Client: Site A (5 ms)H.264

0

10

20

30

1 2 4 8 16 32 64 128 256

Fra

me

Ra

te [

Hz]

Tiles

Regular Tiling Auto-Tuned Tiling

25

INTEROPERABILITY

26

STANDARD-COMPLIANT BITSTREAMWeb Browser Streaming Example

EGL-based shim GLUT

Stream unmodified simpleGL example fromheadless node to web browser (with interaction!)

JavaScript client

WebSocket-based bidirectional communication

On-the-fly MP4 wrapping of H.264

27

RESOURCES

28

VIDEO CODEC SDKAPIs For Hardware Accelerated Video Encode/Decode

What’s New with Turing GPUs and Video Codec SDK 9.0

• Up to 3x decode throughput with multiple decoders on professional cards (Quadro & Tesla)

• Higher quality encoding - H.264 & H.265

• Higher encoding efficiency (15% lower bitrate than Pascal)

• HEVC B-frames support

• HEVC 4:4:4 decoding support

NVIDIA GeForce Now is made possible by leveraging NVENC in the datacenter and streaming the result to end clients

https://developer.nvidia.com/nvidia-video-codec-sdk

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NVPIPE

Simple C API

H.264, HEVC

RGBA32, uint4, uint8, uint16

Lossy, Lossless

Host/Device memory, OpenGL textures/PBOs

https://github.com/NVIDIA/NvPipe

Issues? Suggestions? Feedback welcome!

A Lightweight Video Codec SDK Wrapper

30

ConclusionGPU-accelerated video compression opens up novel and fast solutions to the large-scale remoting challenge

Video Codec SDKhttps://developer.nvidia.com/nvidia-video-codec-sdk

NvPipehttps://github.com/NVIDIA/NvPipe

We want to help you solve your large-scale vis problems on NVIDIA!

Tim Biederttbiedert@nvidia.com