Hub-based Simulation and Graphics Hardware Accelerated ...

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2 Δεκ 2013 (πριν από 3 χρόνια και 10 μήνες)

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Network for Computational Nanotechnology

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 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


Network for Computational Nanotechnology

nanoHUB Remote Simulation and Visualization

Network for Computational Nanotechnology

Outline


nanoHUB.org


Challenges and requirements


Related work


Our system design


Performance and optimization


Case studies


Summary and future work


Network for Computational Nanotechnology

nanoHUB.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

Network for Computational Nanotechnology

User 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

Network for Computational Nanotechnology

nanoHUB Simulation Architecture

Internet

Gig Net

Simulation Cluster

Gig Net

Web Server

Virtual Machine

Open Science Grid

and

NSF TeraGrid


Network for Computational Nanotechnology

DEMO!

Network for Computational Nanotechnology

System Requirements


Transparency in service delivery


Scalability to increased workload


Responsiveness to user command


Flexibility in handling simulation data


Extensibility in software and hardware


Network for Computational Nanotechnology

Visualization 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

Network for Computational Nanotechnology

Related Work


Molecular Dynamics Visualization


Surface rendering


Structure rendering


Volume visualization


Electron potential fields


Electronic wave function


Electro
-
magnetic fields

Network for Computational Nanotechnology

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 [Kr
ü
ger et al. 05])


Remote Visualization


Data is too large to transfer over network


Local workstation cannot handle the data


Distance collaboration

Network for Computational Nanotechnology

Practical Obstacles to nanoHUB


VNC session run on cluster nodes with no graphics
hardware acceleration


Cluster nodes are rack mounted machines with
neither AGP nor PCI Express interfaces


nanoHUB’s virtual machine layer cannot directly
access graphics hardware

Network for Computational Nanotechnology

Our 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

Network for Computational Nanotechnology

Schematic View

Internet

Gig Net

Simulation Cluster

Gig Net

Open Science Grid

and

NSF TeraGrid


Web Server

Virtual Machine

Gig Net

Hardware
-
accelerated Render Farm

Client
-
Server

Network for Computational Nanotechnology

Hardware


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


Network for Computational Nanotechnology

Rappture Toolkit


Rapid Application Infrastructure Toolkit


Accelerate development of basic infrastructure


Declare simulator input / output using XML


Automatic generation of GUI

Network for Computational Nanotechnology

nanoVIS


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


Network for Computational Nanotechnology

Vector Field Visualization (Cont.)


Particle Implementation


[Kolb et al. 2004] [Kr
ü
ger 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



n
n
n
n
n
v
t
t
x
x









1
1
Network for Computational Nanotechnology

Client
-
Server Interaction

Rappture

nanoSCALE

Connect



nanoVIS

Client
-
Server


Select

Render Farm

Simulation Cluster

Connect

Data

Spawn

Network for Computational Nanotechnology

Performance 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

Network for Computational Nanotechnology

GPU 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
j
j
j
j
n
i
p
f
m
ceil
L
1
*
1
*
4
2

Volume visualization

Particle system

Network for Computational Nanotechnology

Performance


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.


Network for Computational Nanotechnology

Case Studies


Successfully developed several nanotechnology tools


SQUALID
-
2D


Quantum Dot Lab


BioMOCA


nanoWire

Network for Computational Nanotechnology

2
-
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





Network for Computational Nanotechnology

2
-
D Electron Gas Simulator

Particle Tracing and LIC

Flow and Electron Potential

Network for Computational Nanotechnology

BioMOCA


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

Network for Computational Nanotechnology

Quantum Dot Lab


Goal


Study the wave functions (orbitals) of electrons trapped in a
quantum dot device


Method


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

p orbital

s and p orbitals

s orbital

Network for Computational Nanotechnology

Conclusions


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

Network for Computational Nanotechnology

Future Work


Expand to generic scientific hub
-
based visualization engine


Our system can be adopted to economically deliver accelerated
graphics 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

Network for Computational Nanotechnology

Acknowledgement


Martin Kraus, Nikolai Svakhine, Ross Maciejewski, Xiaoyu Li


Anonymous reviewers for many helpful discussions and
comments


nVIDIA


National Science Foundation under Grant No. EEC
-
0228390