Network for Computational Nanotechnology

Hub-based Simulation and Graphics Hardware Accelerated

Visualization for Nanotechnology Applications

Hub-based Simulation and Graphics Hardware Accelerated

Visualization for Nanotechnology Applications

Wei Qiao qiaow@purdue.edu

Michael McLennan mmclennan@purdue.edu

Rick Kennell kennell@purdue.edu

David S. Ebert ebertd@purdue.edu

Gerhard Klimeck gekco@purdue.edu

Purdue University

Network for Computational Nanotechnology

Our GoalsOur Goals

Provide advanced interactive visualization of scientific simulations to users worldwide without the user needing special graphics capabilities

Approach - integrate hardware-accelerated remote visualization into nanoHUB.org

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nanoHUB Remote Simulation and VisualizationnanoHUB Remote Simulation and Visualization

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OutlineOutline

nanoHUB.org

Challenges and requirements

Related work

Our system design

Performance and optimization

Case studies

Summary and future work

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nanoHUB.orgnanoHUB.org

A nano-science gateway for nanotechnology education and research

Created by the Network for Computational Nanotechnology (NCN)

Educational material• Animations• Courses • Seminars

Simulation tools accessible from a web browser

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User Community and UsageUser Community and Usage

Nanoelectronics Community• Researchers

• Educators

• Students

Usage (last year)

• More than 10,000 users viewed online materials

• 1,800 users ran more than 54,000 simulation jobs consuming over 28,500 hours of CPU time

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nanoHUB Simulation ArchitecturenanoHUB Simulation Architecture

Internet Gig Net

Simulation Cluster

Gig Net

Web Server

Virtual Machine

Open Science Gridand

NSF TeraGrid

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DEMO!DEMO!

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System RequirementsSystem Requirements

Transparency in service delivery

Scalability to increased workload

Responsiveness to user command

Flexibility in handling simulation data

Extensibility in software and hardware

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Visualization ChallengesVisualization Challenges

Architecture

• Lack state of the art visualization systems

• Mismatch between CPU and GPU resources

Users

• Predominantly remote

• Vast diversity of computing platforms and capabilities

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Related WorkRelated Work

Molecular Dynamics Visualization• Surface rendering• Structure rendering

• Volume visualization• Electron potential fields

• Electronic wave function

• Electro-magnetic fields

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Related Work (Cont.)Related Work (Cont.)

Flow Visualization • Texture synthesis

• CPU ([Wijk 91] and [Cabral and Leedom 93])

• GPU ([Heidrich et al. 99], [Jobard et al. 00], [Weiskopf et al. 2003] and [Telea and Wijk 03])

• Particle tracing• CPU ([Sadarjoen et al. 94])

• GPU ([Kolb et al. 04] and [Krüger et al. 05])

Remote Visualization• Data is too large to transfer over network• Local workstation cannot handle the data • Distance collaboration

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Practical Obstacles to nanoHUBPractical Obstacles to nanoHUB

VNC session run on cluster nodes with no graphics

hardware acceleration

Cluster nodes are rack mounted machines with neit

her AGP nor PCI Express interfaces

nanoHUB’s virtual machine layer cannot directly ac

cess graphics hardware

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Our System DesignOur System Design

Client-server architecture

• nanoVIS render server• Visualization engine library

• Vector flows

• Multivariate scalar fields

• Rappture GUI client• User front end

• nanoSCALE service daemon• Monitors render loads

• Track GPU memory usage

• Starts nanoVIS servers

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Schematic View Schematic View

Internet Gig Net

Simulation Cluster

Gig Net

Open Science Gridand

NSF TeraGrid

Web Server

Virtual Machine

Gig Net

Hardware-accelerated Render Farm

Client-Server

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HardwareHardware

Linux cluster render farm• 1.6GHz Pentium 4

• 512MB of RAM

• nVIDIA Geforce 7800GT graphics hardware

Advantages• Extremely cost effective

• Flexible to upgrade and expand

• Integrates tightly into the nanoHUB architecture

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Rappture ToolkitRappture Toolkit

Rapid Application Infrastructure Toolkit• Accelerate development of basic infrastructure• Declare simulator input / output using XML• Automatic generation of GUI

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nanoVISnanoVIS

Fully accelerated by graphics hardware

Visualize a variety of nanotechnology simulations• Volumetric and multivariate scalar fields

• Texture-based volume visualization

• FFC volume (zinc-blende) [Qiao et al. 2005]

• Vector fields

• GPU particle tracing

• 2D texture synthesis

• Geometric drawing to illustrate simulation geometry

• GL primitive drawing

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Vector Field Visualization (Cont.)Vector Field Visualization (Cont.)

Particle Implementation• [Kolb et al. 2004] [Krüger et al.

2005]• Framebuffer Object (FBO)• Vertex Buffer Object (VBO)• Particles stay in GPU memory

2D texture synthesis• Complement particles

Particle Data

FBO Texture

Vector Field

VBO

Vertex Data

Pixel Shader

GPU

Particle Render

nnnnn vttxx

11

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Client-Server InteractionClient-Server Interaction

Rappture

nanoSCALEConnect

nanoVIS

Client-Server

Select

Render Farm

Simulation Cluster

ConnectData

Spawn

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Performance and OptimizationPerformance and Optimization

Work load consideration• GPU heavy

• Rendering

• CPU light• Network communication

GPU-oriented optimization• GPU load estimation scheme• Node selection scheme based on estimated GPU load

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GPU Load EstimationGPU Load Estimation

Fragment processing cost• Number of rasterized fragments • Computation per fragment

Unified measurement for particle system and volume • Hard to compare cost of particle rendering to advection• Experimental data allows a unified measurement

• Render cost is factor of 0.2 to advection

Estimation equation• Primary cost of the shader execution is texture access

s

jjj

jni pf

mceilL

1

*1*4

2

Volume visualization Particle system

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PerformancePerformance

Measure turn around time (from issue command to image received)

128 x 128 x 128 scalar field 512x512 render window Simulated user interaction

• Transfer function modification, rotation, zoom, cutting plane, etc.

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Case StudiesCase Studies

Successfully developed several nanotechnology tools• SQUALID-2D• Quantum Dot Lab• BioMOCA• nanoWire

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2-D Electron Gas Simulator2-D Electron Gas Simulator

Goal• Study the effects of impurity in a nanowire

Device composition• Electrodes are positioned on the top• GaAs and AlGaAs semiconductor layers• A narrow channel constraining the electrons in the middle

Experiments• Vary magnetic field• Electron flows • Electron potential fields

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2-D Electron Gas Simulator2-D Electron Gas Simulator

Particle Tracing and LICFlow and Electron Potential

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BioMOCABioMOCA

Goal• Study the flow of ions through a pore in a cell membrane

Method• Compute random walks of ions through a channel with a fixed

geometry within a cell membrane.

Cell Wall

Cell Wall

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Quantum Dot LabQuantum Dot Lab

Goal• Study the wave functions (orbitals) of electrons trapped in a quantu

m dot device Method

• Configure incidental light source, shape and size of the quantum dot

p orbital s and p orbitalss orbital

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ConclusionsConclusions Hub-based remote visualization is a powerful, flexible solution

• Seamlessly delivers hardware-accelerated visualization to remote scientists with minimal requirements on their computing environments

Intuitive interface and ease of use are key for wide-usage• Enables rapid development and deployment of new simulation tools

Tight integration into the simulation and interactive performance can speed scientific discovery and change science work flow

nanoVis tools is huge success

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Future WorkFuture Work

Expand to generic scientific hub-based visualization engine

• Our system can be adopted to economically deliver accelerated gra

phics to other hub-based multi-user environments

Expand to large data support

GPGPU nano-electronics simulations and integrated visualization

More accurate GPU load estimation using nVidia newly released

NVPerfKit 2.1 for Linux

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AcknowledgementAcknowledgement

Martin Kraus, Nikolai Svakhine, Ross Maciejewski, Xiaoyu Li Anonymous reviewers for many helpful discussions and com

ments nVIDIA National Science Foundation under Grant No. EEC-0228390