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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 Issues: Centralized server + many clients Client requests “work unit” to process Communication is minimized Clients do not communicate 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: VoIP: DHT: Napster, Gnutella, eDonkey Skype 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 fullydistributed, message-efficient manner. Specific goals: Scalable Fast Limit & minimize message traffics 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 Region Site Design Concepts Use Voronoi to solve the neighbor discovery problem 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 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 Joining node Acceptor’s region 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: AOI: Trials: Time-steps: 1000x1000 150 10 ~ 250 nodes 1000 Behavior model Random movement: Constant velocity: Movement duration: random direction 5 units/step 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 (%) Topology Consistency Measurements 100 99 98 97 96 95 94 93 92 91 90 0 50 100 150 Number of Nodes 200 250 Drift Distance Drift Distance 100 90 average 80 maximum 70 60 50 40 30 20 10 0 0 50 100 150 Number of Nodes 200 250 Scalability (1) Transmission Size Per Node Per Second 6.0 5.0 Size (kb) send (max) 4.0 send (avg) recv (max) 3.0 recv (avg) 2.0 1.0 0.0 0 25 50 75 100 125 150 175 200 225 250 Number of Nodes Scalability (2) Average Neighbor Size Measurements 18 16 Neighbor Size 14 12 connected 10 AOI 8 6 4 2 0 0 50 100 150 Number of Nodes 200 250 Scalability (3) Comparison of Voronoi-based P2P and Client-Server 180 160 Size (kb) 140 send (avg) 120 recv (avg) 100 CS-send (avg) 80 CS-recv (avg) 60 40 20 0 0 25 50 75 100 125 150 175 200 225 250 Number of Nodes 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 Bart Whitebook Jon Watte (France Telecomm R&D, Solipsis) (butterfly.net) (there.com) Dr. Wen-Bing Horng Dr. Jiung-yao Huang (洪文斌老師) (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 ms Small protein folds in tens of ms Current record: 1ms (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 10ms, 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) Average transmission size per node per second 5.0 send (basic) 4.5 recv (basic) 4.0 send (dAOI) Size (kb) 3.5 recv (dAOI) 3.0 2.5 2.0 1.5 1.0 0.5 0.0 0 25 50 75 100 125 150 Number of Nodes 175 200 225 250 Scalability (2) Maximum transmission size per second among all nodes 6.0 send (basic) recv (basic) 5.0 send (dAOI) recv (dAOI) Size (kb) 4.0 3.0 2.0 1.0 0.0 0 25 50 75 100 125 150 Number of Nodes 175 200 225 250 Scalability (3) Average neighbor size for basic and dynamic AOI models 18 connected (basic) 16 connected (dAOI) 14 AOI (basic) Neighbor Size AOI (dAOI) 12 10 8 6 4 2 0 0 50 100 150 Number of Nodes 200 250 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