scalable peer-to-peer network for highly synchronized simulations shun-yun hu institute of physics,...
TRANSCRIPT
Scalable Peer-to-peer Network for Highly Synchronized Simulations
Shun-Yun Hu
Institute of Physics, Academia Sinica
2005/03/11
Outline
Introduction Voronoi-based Overlay Network (VON) Simulation Results Conclusion
A Look at Simulations
Simulations are important tools in scientific research
Larger scale and higher resolution (more accurate and detailed simulations) are constantly sought
However, computational resource can be limited
An Untapped Potential
300 Million PCs on the Internet (2000 est.)
Up to 80% to 90% of CPU is wasted
Large supply of computing resource, growing rapidly
An Example: SETI@Home
Search for Extraterrestrial Intelligence (SETI) UC Berkeley Project launched in May 1999
PC User downloads a screen saver Calculations are done using idle CPU time
2005/03 statistics (in 6 years) 5.3 M world-wide participants 2.2 M years of single-processor CPU 54 teraflop machine (current top 3: 70.72, 51.87, 35.86)
Simulation: Folding@Home
Stanford Project launched in Sept. 2000 Seeks to determine protein’s 3D structure
Screensaver that downloads “work units” 2002 Statistics:
30,000 volunteers 1 M days of single-processor CPU
Published 23 papers in: Science, Nature, Nature Structural Biology, PNAS, JMB, etc.
The Grand Question
Can we build the ultimate simulator for large-scale simulation utilizing millions of computers world-wide?
Potential applications: Nuclear reaction Star clusters Atomic-scale modeling in material science Weather, earthquakes Biology (protein, ecosystem, brain, ...)
Current Limitations
Current methodology Centralized server + many clients Client requests “work unit” to process Communication is minimized Clients do not communicate
Issues: Only suitable for “embarrassingly parallel” simulations Sophisticated server-side algorithm and management required
An alternative: peer-to-peer (P2P) computing
What is Peer-to-Peer (P2P)?
[Stoica et al. 2003] Distributed systems without any centralized control
or hierarchical organization Runs software with equivalent functionality
Examples File-sharing: Napster, Gnutella, eDonkey VoIP: Skype DHT: Chord, CAN, Pastry
Peer-to-Peer Overlay
A P2P overlay network source: [Keller & Simon 2003]
Promise & Challenge of P2P
Promises Growing resource, decentralized
Scalable Commodity hardware Affordable
Challenges Topology maintenance dynamic join/leave Efficient content retrieval no global knowledge
A Simulation Scenario
How can we utilize P2P for simulation-purpose?Answer: depends on what you want to simulate
We observe that many simulations… are spatially-oriented (i.e. based on coordinate systems) run in discrete time-steps require synchronization at each time-step exhibit localized interaction (i.e. short-range interaction)
example: molecular dynamics (MD) simulation
Scenario Defined for P2P
Many simulated entities (nodes) on a 2D plane ( > 1,000) Positions (coordinates) may change at each time-step How to synchronize positions with those in Area of Interest
(AOI)?
Area of Interest
P2P Design Goals
Observation: the contents are information from AOI neighbors P2P content discovery is a neighbor discovery problem
Solve the Neighbor Discovery Problem in a fully-distributed, message-efficient manner.
Specific goals: Scalable Limit & minimize message traffics Fast Direct connection with AOI neighbors
Outline
Introduction Voronoi-based Overlay Network (VON) Simulation Results Conclusion
Voronoi Diagram
2D Plane partitioned into regions by sites, each region contains all the points closest to its site
Can be used to find k-nearest neighbor easily
Neighbors
Site
Region
Design Concepts
Identify enclosing and boundary neighbors Each node constructs a Voronoi of all AOI neighbors Enclosing neighbors are minimally maintained Mutual collaboration in neighbor discovery
Circle Area of Interest (AOI)
White self
Yellow enclosing neighbor (E.N.)
L. Blue boundary neighbor (B.N.)
Pink E.N. & B.N.
Green AOI neighbor
D. Blue unknown neighbor
Use Voronoi to solve the neighbor discovery problem
Procedure (JOIN)
1) Joining node sends coordinates to any existing node
Join request is forwarded to acceptor
2) Acceptor sends back its own neighbor list
joining node connects with other nodes on the list
Acceptor’s region
Joining node
Procedure (MOVE)
1) Positions sent to all neighbors, mark messages to B.N.
B.N. checks for overlaps between mover’s AOI and its E.N.
2) Connect to new nodes upon notification by B.N.
Disconnect any non-overlapped neighbor
Boundary neighbors
New neighbors
Non-overlapped neighbors
Demonstration
Simulation video General movements (30 nodes, 800x600 world) Local vs. global view
Outline
Introduction Voronoi-based Overlay Network (VON) Simulation Results Conclusion
Simulation Method
Condition World-size: 1000x1000 AOI: 150 Trials: 10 ~ 250 nodes Time-steps: 1000
Behavior model Random movement: random direction Constant velocity: 5 units/step Movement duration: random (1-25 steps)
Consistency Metrics
Topology Consistency [Kawahara, 2004]
Number of observed AOI neighbors
Number of actual AOI neighbors
Drift Distance [Diot, 1999]Distance between observed position and actual position
(average over all nodes)
Topology Consistency
Topology Consistency Measurements
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Scalability (1)
Transmission Size Per Node Per Second
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Scalability (2)
Average Neighbor Size Measurements
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Number of Nodes
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AOI
Scalability (3)
Comparison of Voronoi-based P2P and Client-Server
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Outline
Introduction Voronoi-based Overlay Network (VON) Simulation Results Conclusion
Summary
Idle CPU and networks are untapped potential resources for large-scale simulation
Current approaches do not support simulations that require frequent synchronization / updates
A promising solution: Voronoi-based P2P Overlay Leverage knowledge of each peer to maintain topology Properties: scalable, efficient, fully-distributed Enable simulations with frequent localized synchronization
Future Works
3D Voronoi
Heterogeneous node capacities
Node failures
Application to actual research problems
Acknowledgements Dr. Jui-Fa Chen (陳瑞發老師 ) Dr. Wei-Chuan Lin (林偉川老師 ) Members of the Alpha Lab, TKU CS
Guan-Ming Liao (廖冠名 ) Dr. Chin-Kun Hu (胡進錕老師 ) LSCP, Institute of Physics, Academia Sinica
Joaquin Keller (France Telecomm R&D, Solipsis) Bart Whitebook(butterfly.net) Jon Watte (there.com)
Dr. Wen-Bing Horng (洪文斌老師 ) Dr. Jiung-yao Huang (黃俊堯老師 )
Protein Folding Problem
Find native state (lowest free energy) 3D structure given a 1D sequence of amino acids
Timescale limitation of classical MD methods Secondary structure folds in 0.1 ~ 10 s Small protein folds in tens of s Current record: 1s (villin headpiece) full-atomic simulation of 1 ns takes one CPU day 100 ~ 10,000 gap (it might take decades)
Folding@Home Parallelization Dynamics of complex
system involves crossing of free energy barriers
Most time is spent in free energy minimum “waiting”
Possible to simulate using trajectories much shorter than folding time
“ensemble dynamics” (same coords, different velocities)
Simulation Specifics
free energy barrier crossing is identified by spike in energy variance
Fs peptide (5-residue) (fold time 10ns and 160 +/-10ns)
Artificial mini-protein BBA5 (23-residue) Tens of thousands of 5-20ns trajectories (total of 700us) Mean folding time is 10s, 10 out of 10,000 folds in 10ns
Procedure (LEAVE)
1) Simply disconnect
2) Others then update their Voronoi
new B.N. is discovered via existing B.N.
Leaving node (also a B.N.)
New boundary neighbor
Scalability (1)
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recv (basi c)
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recv (dAOI)
Average transmission size per node per second
Scalability (2)Maximum transmission size per second among all nodes
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Scalability (3)Average neighbor size for basic and dynamic AOI models
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connected (dAOI)
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AOI (dAOI)
Problems of Voronoi Approach
Message traffic Circular round-up of nodes Redundant message sending
(inherent to fully-distributed design)
Incomplete neighbor discovery Can happen with inconsistent / incorrect neighbor list Fast moving node