2023.12.07

PCCC23（第23回PCクラスタシンポジウム）「HPC基盤技術と生成AI」
HPC Infrastructure Technology and Generative AI

VRシミュレーションシステム
VR Simulation System
Greg Gutmann
株式会社計算科学／株式会社分子ロボット総合研究所
CSKK & Molecular Robotics Research Institute1Greg Gutmann: CSKK, MRRI

Computing Background2

Greg Gutmann• Distributed GPGPU Simulation with Distributed Workload Balancing• Near perfect scaling (~95% efficient)• ~10 millisecond simulation budget• Tested on 10 Nvidia Titan Pascal GPU connected by PCIe

• Technology Expertise:• GPGPU• Networking (sockets)• VR (Rendering / HCI)• AI (Nvidia DLI)VR Hand

DNA Origami: 440,000 atoms (~2021)(~2017)
10 million particles• PhD. Work: Microtubule Simulation

Greg Gutmann: CSKK, MRRIVR Simulation

Tuned for molecular-molecular interactions
Less time needed thanconventional MD
methods
Example:1. Clicked start
2. Left for lunch 30 min
3. Returned to thisThe Nucleocapsid protein binds
to RNA to form ribonucleoprotein(RNP) structures for packaging
the genome into the viral capsid.
doi:10.1016/j.antiviral.2013.12.009
doi:10.3390/v6082991

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Greg Gutmann: CSKK, MRRI

Objectives of VR Simulation Framework4• Simulate large molecular-molecular interactions• To aid in the development of Molecular Robotics or other micrometer scale systems• Enable direct interactive simulation by users [Slide 6]• Human-in-the-loop: User can gain a deep understanding of dynamics, while gaining results• Enhance communication around nano to micro scale phenomenon [Slide 7]• Boost knowledge/interest in the work to support the research• Create a scalable / flexible interactive VR framework [Slide 9]• In terms of: Cost, models, local / remoteGreg Gutmann: CSKK, MRRI

Recent AI Models
Example: Alpha fold5• Alpha fold’s purpose is the shape of proteins• We do use Alpha fold within our group to generate proteins to load into
our simulation (Kondo-san, Kansai University)• Our VR simulation’s purpose is to simulate molecularmolecular interactions at a larger scale

Image from Google Search• 100s of thousands to over a million atom range

We do have interest in exploring other AI models for VR simulation, adding existing models ordeveloping our own, to be covered in “Next Steps”

Greg Gutmann: CSKK, MRRI

Benefits of Interactive VR simulation
A coarse-grained simulation• Experiment with new ideas• Molecular Legos• Visualize and test interactions• Electrostatic• Lennard-Jones• Hydrogen bonds• Experiment at scales whichcannot quickly be done withtools like MD and MO

COVID RNA and Nucleocapsid Proteins6

Greg Gutmann: CSKK, MRRI

Benefits as a Communication Tool
• Explain ideas withdynamic visual simulation• Increase people's intuition
and understanding of thenanoscale-world• Generate more interest in
your work
COVID RNA and Nucleocapsid Proteins7

Greg Gutmann: CSKK, MRRI

What We Have Achieved for VR
Version that is Ready for Users

8Ease of access:• Can run on a single gaming laptopModel:• Stable DNA strands with hydrogen bonds• Aggregation of H2O in minor groups• DNA can self-repair up to about 3 turns of unwinding

DNA in Explicit Solution

Greg Gutmann: CSKK, MRRI

Our Computational Framework Targets
Active Prototype / Testing

A simulation framework that can:• Scale between:• Nvidia Jetson boards• Personal Computers• Multi-GPU nodes• Run either:

ServerJetsonCloud9VRVR• Locally• In the Cloud• Models:• Ability to swap models

Collaborative Environments
Our paper: DOI: 10.1515/jib-2022-0017
Clients: Kanagawa --> Server: Sakura Internet in Hokkaido

Greg Gutmann: CSKK, MRRI

Our Computational Framework Targets
Next Steps for Large Scale Interactive VR

10• The ability to switch between:• Conventional simulation and• AI force fields

As GPU hardware performance has shifted to AI
operations such as Tensor cores and TF32 types
Also, quantum effects are expected to increase stability of DNA [1]• Challenges adapting existing AI molecular simulation:• Existing solutions are fast but usually not interactive VR rates [3]• How to still visualize different aspects of the dynamics independently• Existing solutions mostly trained on only small molecules [2,3]• Solutions?: Self-Supervised, Online learning from conventional simulation [4,5][1] https://pubs.acs.org/doi/abs/10.1021/ja102004b, [2] https://doi.org/10.1063/5.0007276, [3] https://www.nature.com/articles/s41586-021-03819-2, [4] doi.org/10.3390/technologies9010002, [5] doi.org/10.3389/fmolb.2021.812248

Greg Gutmann: CSKK, MRRI

Call for Users and Feedback

11• Actively looking for users for our single machine version• Collaborations• User feedback• End users• Let us know how you could envision using such a platform• Direct usage?
• Is there other particle like data to view?

Contact information
Molecular Robotics Research Institute: gutmann@molecular-robot.com
Computational Science Inc.: gutmann@cskk.jp
VR Simulation Specific: gutmann@vr-sim.com

Using Virtual Atomic Hands

Greg Gutmann: CSKK, MRRI

Thank YouAcknowledgements
Nishi Katsuya
Computational Science
https://www.cskk.jp/Akihiko Konagaya
Molecular Robotics Research Institute• Venture Members• Research labs at: Kansai Daigaku, Kyoto Daigaku, Tokyo Daigaku
https://en.molecular-robot.com/en/

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