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Agere.talk
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Agere.talk

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  • 1. Concurrent Objects, Agents, and HPC Aki YonezawaAdvanced Institute of Computational Science (AICS) RIKEN, Kobe
  • 2. My Background, Current and in the past• Currently: – in charge of the research and operation divisions of the national supercomputing center, AICS. – personally have a keen interest in large scale agent-based modeling and simulation.• In the past: – have been engaged in research on concurrent objects and actors, reflection, mobile objects, …
  • 3. Synopsis• AICS• Concurrent Objects• Modeling with mobile/concurrent objects• Modeling with Agents, my understanding• (Reflective concurrent objects)• HPC with concurrent objects• (ultra) Large scale simulation with Agents – Evacuation simulation – Traffic simulation
  • 4. Advanced Institute for Computational Science (AICS)Has been established as the internationalCOE for computational sciences and computer science.Since 2010. (currently ~150 staffs, including 65 researchers) Missions are:  Operating the K computer efficiently for users of wide research areas as well as of industry  Carrying out the leading edge research of computational science, promoting strong collaborations between computer scientists and computational scientists  Plan and develop Japans strategy for computational science, including defining the path to Exascale computing
  • 5. Supercomputer K, a 10 peta-flops machine 5
  • 6. A Glimpse of K Computer Number of CPUs (nodes) > 80 K Local file system Number of cores > 640K > 11PB Total memory capacity > 1.7PB 1TB/s 350GB/s Global file systems > 30PBSPARC64 VIIIfx CPU designed and made by Fujitsu.
  • 7. Location of AICS & K Computer Kobe K Computer Mae Station Tokyo 423km (263miles) west from TokyoResearch Computer Building building Computer room 50m*60m=3,000m2 Chillers Electric power up to 30MW Substation Supply Gas-turbine cogeneration 5MW*2 Water Cooling System
  • 8. “HPC” ?• In Japan, “HPC” should be read as Highly Political Computing!• What about in the States or Europe?• Some episode in Japan…
  • 9. Televised Congress Hearingon the plan of K supercomputer (Nov. 2009) Chairwoman asked: • Any reason to be No.1? • why not the second place is enough?. She represented the intention of Ministry of Finance. Hearing verdict : 100% budget reduction A big campaign by scientists including 7 Nobel laureates Whole budget was recovered in March 2010!!!
  • 10. 8 Notable Large-scale Applications on K • Multi-scale Multi-physics Simulation for Heart Disease/Disorder (simulation of whole human heart) • Tsunami Simulation and Disaster Mitigation • Development of Virus Molecular Science Based on 10M All-Atom Simulation • Drug Design Applications • Next-generation Fluid Design System Based on Direct Computation of Turbulence • Reduction of Weather Prediction Period using Global Cloud Resolving Model (NICAM) • Simulation of Supernova Explosions based on Neutrino Heating • Prediction of electro-functions of nano-structures
  • 11. Prof. ImamuraProf. Sugita(Univ. Tokyo)
  • 12. UT Heart Simulator Hisada & Sugiura Finite Element Model • 1000M degrees • of freedon !! • takes two days for one beat UT Heart is a multi-scale and multi-physics simulator, starting with physiological models describing the dynamics of various ion currents and sarcomeric proteins. UT Heart leads to beating of the heart and ejection of blood, which produces the blood pressure and electrocardiogram (ECG)
  • 13. Tsunami simulations using the tsunami-coupled equation of motion in 3DMaeda and Furumura (2011) Pure and Applied Geophysics - under review Navier-Stokes eq ∂u ∂u ∂u ∂u ∂p  ∂ 2u ∂ 2u ∂ 2u  +u +v +w = − +ν  2 + 2 + 2  + g x ∂t ∂x ∂y ∂z ∂x  ∂x ∂y ∂z  ∂v ∂v ∂v ∂v ∂p  ∂ 2v ∂ 2v ∂ 2v  + u + v + w = − +ν  2 + 2 + 2  + g y ∂t ∂x ∂y ∂z ∂y  ∂x ∂y ∂z  ∂w ∂w ∂w ∂w ∂p  ∂2w ∂2w ∂2w  +u +v +w = − +ν  2 + 2 + 2  + g z ∂t ∂x ∂y ∂z ∂z  ∂x ∂y ∂z  [Present] Resolution: 1km CPU Time: 2 hour (ES 64 node) [Expected] Resolution: 0.25 km CPU Time: < 10 min (K Computer) It’s possible to issue Tsunami Warning
  • 14. Introducing my early career• 1974, joined Carl Hewitt’s group, MIT• 1977, finished PhD• 1985, ABCL (concurrent OO language), at pre-1st ECOOP• 1986, 1st OOPSLA, ABCL/1 paper “Object-Oriented Concurrent Programming” (MIT Press)• 1988, 3rd OOPSLA, ABCL/R paper• 1900, 5th OOPSLA/ECOOP PC chair “ABCL: an Concurrent Object-Oriented System” (MIT Press)…
  • 15. My idea of Concurrent Objects (1975)For modeling and programming,…• Concurrent Object = Encapsulated(Stateful Object + thread) Object Concurrent thread/cpu => Object• Asynchronous message passing among concurrent objects• Actor Approach -C.Hewitt and H.Baker: Laws for communicating Parallel Processes, IFIP1977 -G.Agha: A Model for Concurrent Computation in Distributed Systems, MIT Press 1987
  • 16. Concurrent objectsEach CO• has a globally unique identity/name,• may have a protected, yet updatable local memory,• has a set of procedures that manipulates the memory,• receives a message that activates one of the procedures,• has a single FIFO queue for arrived messages, and• has autonomous thread(s) of control. - In each CO, memory-updating procedures are activated one at a time with the message arrival order. - The contents of the memory of a CO, which is the local state of the CO.
  • 17. my Modeling of Real World with Concurrent Objects Modelingdomain Representing concurrent
  • 18. Real World Interacting Agents Concurrent Objects & Message Passing WEBEntities,people,machines & theirinteractions
  • 19. Example: Modeling a small Post Office (1977)Mail Box in counter-sectionPostOffice door clients
  • 20. Modeling Post Office in COs• Post Office Building  the door  door CO• Counter with clerks  counter CO• Mail Box  mailbox CO• Customers  customer COs not messages!
  • 21. Modeling Movement of Customers• Two ways: 1. a customer object is transmitted in a message 2. a customer object moves by itself (autonomously) Object (or its code) migrates autonomously!!
  • 22. Mobility increases CO’s power• Increases CO’s – modeling power – intellectual power• Entering a new environment – > increase accessibility of potentially available information and knowledge If COs are equipped with appropriate means for access to surrounding environments, their power increases.
  • 23. JavaGO: our Mobile Concurrent Objects Language (1998) and its application systems
  • 24. Realization of Self-migration/mobile Concurrent Objects – JavaGo Language Adding (Goto: L) statement to the language T.Sekiguchi & L1 H.Masuhara … (go-to L1) L2• JavaGo Language and its implementation that enables programmers to write concurrent objects moving around network nodes (1999)
  • 25. JavaGoX: transformation for transparent thread migration• Java’s support for mobile objects – dynamic class loading – “serialization” of object states• JavaGoX enables efficient migration of running objects – by inserting code for saving/restoring execution stack into heap – implemented as a bytecode transformation system Cf. Sakamoto, Sekiguchi, Yonezawa: Bytecode Transformation for Portable Thread Migration in Java, in ASA/MA00, 2000 for details.
  • 26. An Application of Mobile Concurrent Objects Automatic Software Install/Update System
  • 27. Installing/updating software in PCs on LAN is painful• There are many PCs on a single LAN.• Users of the PCs do not install/update software – They are busy or non-geek
  • 28. Installing/updating software in PCs on LAN is painful• There are many PCs on a single LAN.• Users of the PCs do not install/update software – They are busy or non-geek
  • 29. Features of Mobile Automatic Installation System• Mobile software (called mobile installers) migrates from administrative PCs to client PCs and installs/updates software automatically – Administrative costs can be largely reduced • Number of client PCs does not matter • Totally unattended installation/update is possible• Encryption and digital signature are supported – Risks of illegally-copied software and virus programs are addressed
  • 30. Overview of Our System Administrative PC Mobile Installer Target Software Encryption Decryption Key Client PCs Digital Signature
  • 31. Overview of Our System Administrative PC Mobile Installer Target Software Encryption Decryption Key Client PCs Digital Signature
  • 32. Overview of Our System Administrative PC Mobile Installer Target Software Encryption Decryption Key Client PCs Digital Signature
  • 33. Overview of Our System Administrative PC Mobile Installer Target Software Encryption Decryption Key Client PCs Digital Signature
  • 34. Reflection in COs and its Application [ABCL/R language in OOPSLA88]Reflection makes COs more intelligent, and closer to Agents?
  • 35. Computational Reflection Computation about Oneself: Introspection & Self Modification MCPs: meta- M[S] interpreters, meta-objects SA reflective system S can reason base-levelabout or act upon itself via thecausally-connected selfrepresentation M[S]. Representation in a Reflective Tower (Smith, 1982) Pioneers: S is reified as R[S] within the meta-circular processor 3-Lisp (B. C. Smith, 1982) MCP1. MCP1 is also reified in MCP2, and so forth. 3-KRS (P. Maes, 1986) Reflective behaviors are realized as normal operations in the meta-levels (MCPs).
  • 36. Why Reflection in COs?• Customizable meta-objects can introduce – new language features – Modification of semantics of objects. – Application/Domain-Specific Language Features• Highly Dynamic Behaviors – On-the-Fly Code Acquisition – Dynamically Adaptable Behaviors – Object Migration – Time control of distributed simulation• A New Dimension of Modularization – Descriptions of Non-Functional Requirements/constraints using Meta-Levels (Meta-Objects) • This idea later evolved into AOP
  • 37. ABCL/R One Concurrent Object –> One Meta-Object Each object has its own meta- the meta-object of O methods execution object that solely governs its engine computation. The meta- (evaluator) object reifies the structure of the object. reified message The meta-object also has its internal meta-object. So the reflective state tower exists for each object. message queue The base-level object can send messages to its meta- reflective object: this realize reflective O message behaviors. incoming message an object (base-level)Watanabe & Yonezawa, OOPSLA 88
  • 38. Now, a Bit of Agents
  • 39. What is an Agent (Macal&North’06)• An agent is identifiable, – a discrete individual with a set of characteristics and rules governing its behaviors and decision-making capability• An agent is autonomous and self-directed, – An agent can function independently in its environment.• An agent is situated (and mobile), – living in an environment with which it interacts along with other agents. – has protocols for interaction with other agents, such as for communication, and the capability to respond to the environment• An agent may be goal-directed, – Has goals to achieve (not necessarily objectives to maximize) with respect to its behaviors.
  • 40. An Agent at a Glance (Macal&North’06)
  • 41. Agents have more abilities than COs• ( + goals to achieve)• + Automatic update of location information and other information• + can modify its self (~a la reflection in CO)• + sensor facilities for environment information – sight/vision – field force –…• + actuator facilities –…
  • 42. Applications of COs in large scales- Linden’s “Second Life” / Online Virtual World- In the context of HPC, Charm++ and NAMD
  • 43. Back to Original Motivation of COs Real World Interacting Agents Concurrent Objects & Message Passing WEB Entities,people, machines & their interactionsLinden’s Second Life … is a natural outcome from the motivation of COs:
  • 44. Why COs for Second Life• The idea of concurrent objects has been adopted in Second Life (Linden Lab.) because: – COs can directly simulating virtual world objects, – which enables easy modeling and easy/safe concurrent programming!
  • 45. COs in Second Life• Jim Purbrick, Mark Lentczner, “Second Life: The World’s Biggest Programming Environment”, Invited Talk at OOPSLA2007, said: – Objects and avatars cooperate and coordinate each other by exchanging messages. COness! – each object or avatar is programmed to • Have its own state, • Have its own method to respond to an incoming message, • Have different responses to different states, and • Have its own thread. – About 2 millions of objects are programmed in Second Life and they are in action.
  • 46. Application of JavaGoX’s transformation method to Second Life• our JavaGoX [ASA/MA’00] – a bytecode transformation system that enables migration of running objects on JVM• Second Life employs similar transformation for their new Mono-based script Sims//COs execution engine – for migrating objects between “regions” • a region is managed by one server region Image from “EVOLVING NEMO” in New World Notes at Second Life Blog (http://secondlife.blogs.com/nwn/2005/06/evolving_nemo.html)
  • 47. Concurrent Object Languages for HPC ABCL/f (U. Tokyo) Charm++ (U. Illinois)
  • 48. ABCL/f implementation on Fujitsu AP1000 (PPoPP 93)• One of the earliest attempts to implement concurrent objects as a high performance parallel programming language on distributed memory massively parallel 512 processors (sparc) Kenjiro Taura, Uni. Tokyo © Information Processing Society of Japan
  • 49. Charm++• CO-based language system for parallel/supercomputers developed by Sanjay Kale’s group (U. Illinois) since 1993.• Used for development of NAMD (Molecular Dynamics), OpenAtom(quantum chemistry), ChaNCA(Galaxy generation)• Operational for various supercomputers to date – Full Jaguar PF Cray XT5 (224,076 cores) – K computer (700,000 cores), Blue Gene/Q (65,536 cores)• Has a framework for dynamic load balancing by moving Cos from node to node
  • 50. What is Charm++?• Platform independent library for writing parallel programs – C++, C, and Fortran are supported• Based on the notion of concurrent objects – Charm++ programs consist of: • Parallel running objects (named “chare”), and • Communication among them via asynchronous method invocation
  • 51. Overview of Programming Model: Logical View ・・・ Chare (Concurrent Objects) ・・・ Asynchronous Method Invocation
  • 52. Overview of Programming Model: Physical View Chares migrate btwn processors Processor Processor Processorterconnect Interconnect Interconnect Intercon
  • 53. Overview of Charm++ ImplementationProcessor Processor Chare A Chare B Charm++ Charm++ Scheduler Runtime Runtime Proxy of Interconnect chare B Message Queue
  • 54. Overview of Charm++ ImplementationProcessor Processor Invoke a Chare A method of Chare B chare B Charm++ Charm++ Scheduler Runtime Runtime Proxy of Interconnect chare B Message Queue
  • 55. Overview of Charm++ ImplementationProcessor Processor Invoke a Chare A method of Chare B chare B Invoke the local proxy of chare B Charm++ Charm++ Scheduler Runtime Runtime Proxy of Interconnect chare B Message Queue
  • 56. Overview of Charm++ ImplementationProcessor Processor Chare A Chare B The proxy creates a message for Charm++ invoking chare B Charm++ Scheduler Runtime Runtime Message Proxy of Interconnect chare B Message Queue
  • 57. Overview of Charm++ ImplementationProcessor Processor Chare A Chare B Runtime transmits the message via interconnects Charm++ Charm++ Scheduler Runtime Runtime Message Proxy of Interconnect chare B Message Queue
  • 58. Overview of Charm++ ImplementationProcessor Processor Chare A Chare B Scheduler dispatches the message to invoke chare B Charm++ Charm++ Scheduler Runtime Runtime Message Proxy of Interconnect chare B Message Queue
  • 59. Applications of Charm++• Molecular dynamics: NAMD – http://charm.cs.uiuc.edu/research/moldyn• Quantum molecular dynamics: OpenAtom – http://charm.cs.uiuc.edu/research/qmmm/• Rocket simluation: RocStar – http://www.csar.uiuc.edu/rocstar/index.html• Cosmology simulation: ChaNGa – http://www- hpcc.astro.washington.edu/tools/changa.html• etc.
  • 60. NAMD(Nano-scale Molecular Dynamics Simulator)
  • 61. NAMD• Nano scale molecular dynamics simulator for supercomputers• Major application of Charm++• Developed jointly by two groups in Illinois Univ. Prof. Sanjay Kale (Computer Science) Prof. Schulten (Theoretical Biology)• Received Gordon Bell Prize in 2006• Running on Juager PF (224,076 cores) for 100M atoms proteins
  • 62. This still from a Quicktime movie represents a view of the drugburied in the binding pocket of the A/H1N1 neuraminidase protein. Molecular Structure
  • 63. Analysis of Molecular Structure of Swine Flu by NAMDShowed:• Structural changes of A/H1N1 protein induce anti-tamiful effects.
  • 64. Experimental Results: NAMD on K computer Kamada(2012) 20 ApoA1 (92,224 atoms) 18 ATPase (327,506 atoms) 16 STMV (1,066,628 atoms) 14CPU time per step [ms] 12 10 8 6 4 2 0 8 cores /node 0 512 1024 1536 2048 Number of nodes • Benchmarks were obtained from http://www.ks.uiuc.edu/Research/namd/utilities/ • 7 workers per node
  • 65. Now, Back to Agents
  • 66. What is an Agent? (Macal&North’06)• An agent is identifiable, – a discrete individual with a set of characteristics and rules governing its behaviors and decision-making capability• An agent is autonomous and self-directed, – An agent can function independently in its environment.• An agent is situated, – living in an environment with which it interacts along with other agents. – has protocols for interaction with other agents, such as for communication, and the capability to respond to the environment• An agent may be goal-directed, – Has goals to achieve (not necessarily objectives to maximize) with respect to its behaviors.
  • 67. An Agent at a Glance (Macal&North’06)
  • 68. Agents have more abilities than COs• ( + goals to achieve)• + Automatic update of location information and other information• + can modify its self (~a la reflection in CO)• + sensor facilities for environment information – sight/vision – field force –…• + actuator facilities –…
  • 69. Ultra Large Scale Agent-Based Simulation in Japan• Evacuation from Tsunami Attack• Whole city traffic simulation (Kyoto)
  • 70. Mitigation of Tsuname Damages - A TOTAL Simulation APPROACH -1. An earthquake breaks out2. Propagation of strong vibration3. Simulation of the Tsunami – Broadcast early warning4. Simulation of flooding cities5. Simulation of building damages6. Simulation of human evaluation – Evacuation guidance
  • 71. Earthquake Vibration Simulation Using available geological stratum dataProf. FurumuraUniv. Tokyo 71
  • 72. Simulation of an Expected Tsunami in Japan
  • 73. Flooding in a City
  • 74. Buildings in part of Tokyo• GIS tile ID : 09ld171 (near Shinjuku)• No. of buildings : 14,000• SRA model : Fiber element model• output size : 12 GB• Strong ground motion data from 1995 Kobe earthquake
  • 75. Evacuation Drill: before building damages assumed Shelter Building
  • 76. Human Evacuation after Earthquake Shelter Building
  • 77. Agent-Based Human Evacuation Simulation• Goal: Total evacuation time to be reduced – Find best evacuation plans• Parameters: # of people, evacuation routes,…• Modeling/predicting human behavior – By monitoring human behavior, using physical driver simulator – By measuring human actions –…
  • 78. Measuring people’s movement in an evacuation drill
  • 79. Large scale evacuation simulations• Understanding the dynamics of mass evacuation is important – to find strategies to save as many as possible, – in future urban developments, etc.• Based on Multi-Agent Simulations (MAS) – Suitable for simulating heterogeneous and complex phenomena• Mass evacuation is contagious in nature and have cascade effect – Whole affected area has to be simulated as a single domain• HPC enhancements are necessary to simulate millions in a city like Tokyo – Monte-Carlo simulations are necessary – Poor scalability has been reported in literature
  • 80. Multi Agent Simulation• Agents in evacuation simulation Agent Thought – agent mimic people interacting with Ability each other and the environment Speed Ability Direction Vision Thought Speed • Agents See the environment Passing Path • Uses its Think to make a decision • Move according to the decision See() – numerous kinds of agent to model Think() Move() heterogeneous crowds • Different levels of abilities such as see, think and act • Different information about the surrounding – residents, visitors, officials, etc. • Different levels of responsibilities – police, fire fighters, community leaders, etc.
  • 81. Simulation: 2,000,000 agentsin a part of Kochi City Using K omputer
  • 82. Environment of evacuation simulation• Space in which human or human organization acts• Automatically generated from GIS data – Space/Environment is modeled as a grid – Grid cells are classified as occupied and un-occupied GIS data of the real environment Raster model for MAS in vector format
  • 83. Agent’s visual perception and moves• Sophisticated vision: – Scan the environment in high resolution (at 0.5o intervals) – Identify paths, obstacles, neighbors and slow moving groups within 30m• Moves: – avoiding the obstacles such as slow moving groups of agents – choose the path closest to the direction of destination – avoid collision with individuals 60m Direction of destination Walking direction
  • 84. Visual Sight– an official searching people astray -
  • 85. Scalability of evacuation simulation module 12 K Ideal gradient 11 lilac (6 core Xeon E5620) 10 Log(runtime,2) 9 Number of agents = 8 500,000 7 Dotted lines show the Area 19.2 km2 in Kochi city ideal gradient, not the ideal run time 6 3 4 5 6 7 8 9 10 Log(CPU cores,2)  C++ in K seems to be slow  May be STL is not well optimized
  • 86. strategies for reducing pre-evacuation time Tsunami evacuation is highly time critical ◦ Anticipating Tokai-Tonankai-Nankai tsunami may arrive in 10 minutes Evacuation time = pre-evacuation time + travel time  Pre-evacuation time: waking, change clothes, putting stuffs together,  Pre-evacuation time may be as large as 30 minutes Appoint officials: deliver the warning personally ◦ People tends to neglect the warnings from announcements, TV, radio, etc. ◦ To personally deliver warning and give path to nearest evacuation center Need to study the effectiveness with different influential power
  • 87. Official agents personally advising residents to evacuate early
  • 88. Findings• Preliminary study on pre-evacuation time reduction – Delivering the warning personally could reduce evacuation time significantly – Employing officials + community leaders could save more lives• High parallel scalability is attained – Can handle several millions of agents on thousands of CPU cores
  • 89. Traffic Flow Simulation with Agents• S. Kato, G. Yamamoto,.. (IBM Res. Report RTO 759, 2008)
  • 90. Simulator Architecture MASMITS Agent Space Simulation Space Driver Agent Message Driver ModelDriver Agent MessageDriver Model
  • 91. Agent Space Simulation Space• Collection of Agents• Agent: messages – current states of traffic – Model of a car and – the alignment of roads to driver behavior driver agents – current speed and positions of vehicles,Messages fromS-space to A-space – distance sbetween cars,• NextSpeedMessage – the curvature and gradient• EnterSpeedMessage of roads on which individual vehicles are running.• NextRoadMessage• EnterRoadMessage Keeps states of roads and agents• ExitRoadMessage• StartMessage
  • 92. Simulation Results (single processor) Kyoto City Road Network• simulated the traffic of the Kyoto city.• the road network of Kyoto, 32,654 links and 22,782 nodes.• 894,802 pairs of origin and destination are assigned.• 894,802 vehicle agents start their origins toward destination at the same time.• time step size is 0.1.• used the IBM xSeries 335 (2.8GHz processor, 4GBmemory).
  • 93. Traffic Simulation for a Kyoto festival day
  • 94. Nation-wide Traffic Simulation on a Supercomputer• Nation-wide Road network: – 993,731 intersections, 2,552,160 roads• The agent simulator written in X10. – X10 language (IBM) for HPC – X10: PGAS OO language with places and async. activities • No MPI, or No OpenMP!!• Tsubame2: 1.19peta flops @ Titech
  • 95. Thanks for your attention!

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