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High Performance Computing and the FLAME Framework

High Performance Computing and the FLAME Framework. Prof C Greenough, LS Chin and Dr DJ Worth STFC Rutherford Appleton Laboratory Prof M Holcombe and Dr S Coakley Computer Science, Sheffield University. Why High Performance Computing?.

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High Performance Computing and the FLAME Framework

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  1. High Performance Computing and the FLAME Framework Prof C Greenough, LS Chin and Dr DJ Worth STFC Rutherford Appleton Laboratory Prof M Holcombe and Dr S Coakley Computer Science, Sheffield University

  2. Why High Performance Computing? • Application can not be run on a conventional computing system • Insufficient memory • Insufficient compute power • High Performance Computing (HPC) generally now means: • Large multi-processor system • Complex communications hardware • Specialised attached processors • GRID/Cloud computing CLIMACE Meeting - 14 May 2009

  3. Issues in High Performance Computing • Parallel system are in constant development • Their hardware architectures are ever changing • simple distributed memory on multiple processors • share memory between multiple processors • hybrid systems – • clusters of share memory multiple processors • clusters of multi-core systems • the processors often have a multi-level cache system CLIMACE Meeting - 14 May 2009

  4. Issues in High Performance Computing • Most have high speed multi-level communication switches • GRID architectures are now being used for very large simulations • many large high-performance systems • loosely coupled together over the internet • Performance can be improved by optimising to a specific architecture • Can very easily become architecture dependent CLIMACE Meeting - 14 May 2009

  5. The FLAME Framework CLIMACE Meeting - 14 May 2009

  6. Characteristics of FLAME • Based on X-Machines • Agents: • Have memory • Have states • Communicate through messages • Structure of Application: • Embedded in XML and C-code • Application generation driven by state graph • Agent communication managed by library CLIMACE Meeting - 14 May 2009

  7. Characteristics of FLAME • The Data Load • Size of agents internal memory • The number of size of message boards • The Computational Load • Work performed in any state change • Any I/O performed • FLAME Framework • Programme generator (serial/parallel) • Provides control of states • Provide communications network CLIMACE Meeting - 14 May 2009

  8. Initial Parallel Implementation • Based on : • the distribution of agents – computational load • distribution of message boards – data load • Agents only communicate via MBs • Cross-node message information is made available to agents by message board synchronisation • Communication between nodes are minimised • Halo regions • Message filtering CLIMACE Meeting - 14 May 2009

  9. P1 P4 P7 P10 P11 P2 P5 P8 P9 P3 P6 P12 Geometric Partitioning Processors Pi halos radius CLIMACE Meeting - 14 May 2009

  10. Parallelism in FLAME CLIMACE Meeting - 14 May 2009

  11. Issues with HPC and FLAME • Parallelism is hidden in the XML model and the C-code – this is in term of agent locality or groupings • Communications captured in XML • In agent function descriptions • In message descriptions • The States are the computational load • weight not known until run time • could be fine or course grained • Initial distribution based on a static analysis • Final distributions method be based on dynamic behaviour CLIMACE Meeting - 14 May 2009

  12. Parallelism in FLAME Parallel agents grouped on parallel nodes. Messages synchronised Message board library allows both serial and parallel versions to work Implementation details hidden from modellers System automatically manages the simulation CLIMACE Meeting - 14 May 2009

  13. Message Boards • Decoupled from the FLAME framework • Well defined Application Program Interface (API) • Includes functions for creating, deleting, managing and accessing information on the Message Boards • Details such as internal data representations, memory management and communication strategies are hidden • Uses multi-threading for work and communications CLIMACE Meeting - 14 May 2009

  14. FLAME & the Message Boards CLIMACE Meeting - 14 May 2009

  15. Message Board API • MB Management • create, delete, add message, clear board • Access to message information (iterators) • plain, filtered, sorted, randomise • MB Synchronisation • moving information between nodes • full data replication – very expensive • filtered information using tagging • overlapped with computation CLIMACE Meeting - 14 May 2009

  16. The MB Environment • Message Board Management • MB_Env_Init - Initialises MB environment • MB_Env_Finalise - Finalises the MB environment • MB_Create - Creates a new Message Board object • MB_AddMessage - Adds a message to a Message Board • MB_Clear - Clears a Message Board • MB_Delete - Deletes a Message Board CLIMACE Meeting - 14 May 2009

  17. The Message Board API (2) • Message Selection & Reading - Iterators • MB_Iterator_Create - Creates an iterator • MB_Iterator_CreateSorted - Create a sorted iterator • MB_Iterator_CreateFiltered - Create a filtered iterator • MB_Iterator_Delete - Deletes an iterator • MB_Iterator_Rewind - Rewinds an iterator • MB_Iterator_Randomise - Randomises an Iterator • MB_Iterator_GetMessage - Returns next message CLIMACE Meeting - 14 May 2009

  18. The Message Board API (3) • Message Synchronisation: Synchronisation of boards involves the propagation of message data out across the processing nodes as required by the agents on each node • MB_SyncStart - Synchronises a message board • MB_SyncTest - Tests for synchronisation completion • MB_SyncComplete - Completes the synchronisation CLIMACE Meeting - 14 May 2009

  19. The Message Board API (4) • MB Sychronisation: • The simplest form is full replication of message data - very expensive in communication and memory • The MB uses message tagging to reduce the volume of data being transferred and stored • Tagging uses message FILTERs to select message information to be transferred • FILTERs are specified in the Model File XMML CLIMACE Meeting - 14 May 2009

  20. The Message Board API (5) • Selection based on filters • Filters defined in XMML • Filters can be used: • in creating iterators to reduce local message list • during synchronisation to minimise cross-node communications CLIMACE Meeting - 14 May 2009

  21. MB Iterators (1) • Iterators objects used for traversing Message Board content. They provide users access to messages while isolating them from the internal data representation of Boards. • Creating an Iterator generates a list of the available messages within the Board against a specific criteria. This is a snapshot of the content of a local Board. CLIMACE Meeting - 14 May 2009

  22. MB Iterators (2) CLIMACE Meeting - 14 May 2009

  23. Porting to Parallel Platforms • FLAME has been successfully ported to the to various HPC systems: • SCARF – 360x2.2 GHz AMD Opteron cores, 1.3TB total memory • HAPU – 128x2.4 GHz Opteron cores, 2GB memory / core • NW-Grid – 384x2.4 GHz Opteron cores, 2 or 4 GB memory/core • HPCx – 2560x1.5GHz Power5 cores, 2GB memory / core • Legion (Blue Gene/P) – 1026xPowerPC 850 MHz; 4096 cores • Leviathan (UNIBI) – 3xIntel Xeon E5355 (Quad Core), 24 cores CLIMACE Meeting - 14 May 2009

  24. Test Models • Circles Model • Very simple agents • all have position data • x,y,fx,fy,radius in memory • Repulsion from neighbours • 1message type • Domain decomposition • C@S Model • Mix of agents: Malls, Firms, People • A mixture of state complexities • All have position data • Agents have range of influence • 9 message types • Domain decomposition CLIMACE Meeting - 14 May 2009

  25. Circles Model CLIMACE Meeting - 14 May 2009

  26. C@S Model CLIMACE Meeting - 14 May 2009

  27. Bielefeld Model CLIMACE Meeting - 14 May 2009

  28. Dynamic Load Balancing • Work only just started • Goal to move agents between compute nodes: • reduce overall elapsed time • increase parallel efficiency • There is an interaction between computational efficiency and overall elapsed time • The requirements of communications and load may conflict! CLIMACE Meeting - 14 May 2009

  29. Balance - Load vs. Communication P1 P2 • Distribution 1 • P1: 13 agents • P2: 3 agents • P2 <--> P1: 1 channel • Distribution 2 • P1: 9 agents • P2: 7 agents • P1 <--> P2: 6 channels Distribution A Frequent Occasional Distribution B CLIMACE Meeting - 14 May 2009

  30. Moving Wrong Agents Moving wrong agents could increase elapsed time CLIMACE Meeting - 14 May 2009

  31. HPC Issues in CLIMACE • Size of agent population • Granularity of agents • is there are large computational load • How often do they communicate • Inherent parallelism (locality) in model • Are the agents in groups • Do they have short range communication • Size of initial data • Size of outputs CLIMACE Meeting - 14 May 2009

  32. HCP Challenges for ABM • Effect initial static distributions • Effect dynamic agent migration algorithms • Sophisticated communication strategies • To reduce the number of communications • To reduce synchronisations • To reduce communication volumes • Pre-tagging information to allow pre-fetching • Overlapping of computation with communications • Efficient use of multi-code nodes on large systems • Efficient use of attached processors CLIMACE Meeting - 14 May 2009

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