1. 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

2. 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

3. Network for Computational Nanotechnology nanoHUB Remote Simulation and Visualization

4. Network for Computational Nanotechnology Outline nanoHUB.org Challenges and requirements Related work Our system design Performance and optimization Case studies Summary and future work

5. 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

6. 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

7. Network for Computational Nanotechnology nanoHUB Simulation Architecture InternetGig Net Simulation Cluster Gig Net Web Server Virtual Machine Open Science Grid and NSF TeraGrid

8. Network for Computational Nanotechnology DEMO!

9. 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

10. 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

11. Network for Computational Nanotechnology Related Work Molecular Dynamics Visualization Surface rendering Structure rendering Volume visualization Electron potential fields Electronic wave function Electro-magnetic fields

12. 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 [Krüger et al. 05]) Remote Visualization Data is too large to transfer over network Local workstation cannot handle the data Distance collaboration

13. 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 nanoHUBs virtual machine layer cannot directly access graphics hardware

14. 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

15. 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

16. 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

17. 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

18. 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

19. Network for Computational Nanotechnology 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 FBOTexture Vector Field VBO Vertex Data Pixel Shader GPU Particle Render

20. Network for Computational Nanotechnology Client-Server Interaction Rappture nanoSCALE Connect nanoVIS Client-Server Select Render Farm Simulation Cluster Connect Data Spawn

21. 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

22. 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 Volume visualization Particle system

23. 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.

24. Network for Computational Nanotechnology Case Studies Successfully developed several nanotechnology tools SQUALID-2D Quantum Dot Lab BioMOCA nanoWire

25. 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

26. Network for Computational Nanotechnology 2-D Electron Gas Simulator Particle Tracing and LIC Flow and Electron Potential

27. 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

28. 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

29. 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

30. 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

31. 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

32. Network for Computational Nanotechnology Vector Field Visualization GPU accelerated particle tracing Similar to [Kolb et al. 2004] and [Krüger et al. 2005] Particle position equation : GPU Eulerian integration using fragment shader:

33. Network for Computational Nanotechnology Node Selection Selection criteria Sufficient GPU memory to fit the data Least amount of GPU workload Historical bias

34. Network for Computational Nanotechnology Selection Details nanoVIS receives a render request nanoVIS estimates the GPU workload of the request nanoVIS sends estimate to local nanoSCALE using pipe nanoSCALE broadcasts cost sum to all peer render nodes Rappture contacts an initial host Initial host chooses a target host to start nanoVIS service with a redirection threshold: Initial host updates its record of the target hosts load average Target host broadcasts its most recent workload Initial host update its record

35. Network for Computational Nanotechnology Summary Developed / deployed a remote visualization hardware and client-server software architecture for nanoHUB.org Flexibly handle a variety of nanoscience simulation data GPU load estimation model and render node selection scheme Seamlessly deliver hardware-accelerated visualization to remote scientists with minimal requirements on their computing environments Enable rapid development and deployment of new simulation tools Our system can be adopted to economically deliver accelerated graphics to other hub-based multi-user environments