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Programming Environment and Performance Modeling for million-processor machines

Programming Environment and Performance Modeling for million-processor machines. Laxmikant (Sanjay) Kale Parallel Programming Laboratory Department of Computer Science University of Illinois at Urbana-Champaign Http://charm.Cs.uiuc.edu. Context: Group Mission and Approach.

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Programming Environment and Performance Modeling for million-processor machines

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  1. Programming Environment and Performance Modeling for million-processor machines Laxmikant (Sanjay) Kale Parallel Programming Laboratory Department of Computer Science University of Illinois at Urbana-Champaign Http://charm.Cs.uiuc.edu PPL-Dept of Computer Science, UIUC

  2. Context: Group Mission and Approach • To enhance Performance and Productivity in programming complex parallel applications • Performance: scalable to very large number of processors • Productivity: of human programmers • Complex: irregular structure, dynamic variations • Approach: Application Oriented yet CS centered research • Develop enabling technology, for a wide collection of apps. • Develop, use and test it in the context of real applications • Develop standard library of reusable parallel components PPL-Dept of Computer Science, UIUC

  3. Project Objective and Overview • Focus on extremely large parallel machines • Exemplified by Blue Gene/Cyclops • Issues: • Programming Environment: • Objects, threads, compiler support • Runtime performance adaptation • Performance modeling • Coarse grained models • Fine grained models • Hybrid • Applications: • Unstructured Meshes (FEM/Crack Propagation), .. David Padua Sanjay Kale Sarita Adve Phillipe Geubelle PPL-Dept of Computer Science, UIUC

  4. Project Objective and Overview • Focus on extremely large parallel machines • Exemplified by Blue Gene/Cyclops • Issues: • Programming Environment • Runtime performance adaptation • Performance modeling • Coarse grained models • Fine grained models • Hybrid • Applications: • Unstructured Meshes (FEM/Crack Propagation), .. David Padua Sanjay Kale Sarita Adve Phillipe Geubelle PPL-Dept of Computer Science, UIUC

  5. Multi-partition Decomposition • Idea: divide the computation into a large number of pieces • Independent of number of processors • Typically larger than number of processors • Let the system map entities to processors • Optimal division of labor between “system” and programmer: • Decomposition done by programmer, • Everything else automated PPL-Dept of Computer Science, UIUC

  6. Object-based Parallelization User is only concerned with interaction between objects System implementation User View PPL-Dept of Computer Science, UIUC

  7. Charm++ • Parallel C++ with Data Driven Objects • Object Arrays/ Object Collections • Object Groups: • Global object with a “representative” on each PE • Asynchronous method invocation • Prioritized scheduling • Information sharing abstractions: readonly, tables,.. • Mature, robust, portable • http://charm.cs.uiuc.edu PPL-Dept of Computer Science, UIUC

  8. Data driven execution Scheduler Scheduler Message Q Message Q PPL-Dept of Computer Science, UIUC

  9. Load Balancing Framework • Based on object migration • Partitions implemented as objects (or threads) are mapped to available processors by LB framework • Measurement based load balancers: • Principle of persistence • Computational loads and communication patterns • Runtime system measures actual computation times of every partition, as well as communication patterns • Variety of “plug-in” LB strategies available • Scalable to a few thousand processors • Including those for situations when principle of persistence does not apply PPL-Dept of Computer Science, UIUC

  10. Building on Object-based Parallelism • Application induced load imbalances • Environment induced performance issues: • Dealing with extraneous loads on shared m/cs • Vacating workstations • Heterogeneous clusters • Shrinking and Expanding jobs to available Pes • Object “migration”: novel uses • Automatic checkpointing • Automatic prefetching for out-of-core execution • Reuse: object based components PPL-Dept of Computer Science, UIUC

  11. Applications • Charm++ developed in the context of real applications • Current applications we are involved with: • Molecular dynamics • Crack propagation • Rocket simulation: fluid dynamics + structures + • QM/MM: Material properties via quantum mech • Cosmology simulations: parallel analysis+viz • Cosmology: gravitational with multiple timestepping PPL-Dept of Computer Science, UIUC

  12. Molecular Dynamics • Collection of [charged] atoms, with bonds • Newtonian mechanics • At each time-step • Calculate forces on each atom • Bonds: • Non-bonded: electrostatic and van der Waal’s • Calculate velocities and advance positions • 1 femtosecond time-step, millions needed! • Thousands of atoms (1,000 - 100,000) PPL-Dept of Computer Science, UIUC

  13. Object Based Parallelization for MD PPL-Dept of Computer Science, UIUC

  14. Performance Data: SC2000 PPL-Dept of Computer Science, UIUC

  15. Charm++ Is a Good Match for M-PIM • Encapsulation : objects • Cost model: • Object data, read-only data, remote data • Migration and resource management: automatic • One sided communication: since the beginning • Asynchronous global operations (reductions, ..) • Modularity: • see 1996 paper for why DD Objects enable modularity • Acceptability: • C++ • Now also: AMPI on top of charm++ PPL-Dept of Computer Science, UIUC

  16. Higher-level Models • Do programmers find Charm++/AMPI easy/good • We think so  • Certainly a good intermediate level model • Higher level abstractions can be built on it • But what kinds of abstractions? • We think domain-specific ones PPL-Dept of Computer Science, UIUC

  17. Decomposition Mapping Charm++ HPF Scheduling expression MPI Specialization Domain specific frameworks /AMPI PPL-Dept of Computer Science, UIUC

  18. S S Q Q Further Match With MPIM • Ability to predict: • Which data is going to be needed and • Which code will execute • Based on the ready queue of object method invocations • So, we can: • Prefetch data accurately • Prefetch code if needed PPL-Dept of Computer Science, UIUC

  19. So, What Are We Doing About It? • How to develop any programming environment for a machine that isn’t built yet • Blue Gene/C emulator using charm++ • Completed last year • Implememnts low level BG/C API • Packet sends, extract packet from comm buffers • Emulation runs on machines with hundreds of “normal” processors • Charm++ on blue Gene /C Emulator PPL-Dept of Computer Science, UIUC

  20. Charm++ Charm++ BG/C low level API Converse Structure of the Emulators Blue Gene/C Low-level API Charm++ Converse PPL-Dept of Computer Science, UIUC

  21. BG/C Nodes Hardware thread Simulating (Host) Processor Emulation on a Parallel Machine PPL-Dept of Computer Science, UIUC

  22. Extensions to Charm++ for BG/C • Microtasks: • Objects may fire microtasks that can be executed by any thread on the same node • Increases parallelism • Overhead: sub-microsecond • Issue: • Object affinity: map to thread or node? • Thread, currently. • Microtasks alleviate load balancing within a node PPL-Dept of Computer Science, UIUC

  23. Emulation efficiency • How much time does it take to run an emulation? • 8 Million processors being emulated on 100 • In addition, lower cache performance • Lots of tiny messages • On a Linux cluster: • Emulation shows good speedup PPL-Dept of Computer Science, UIUC

  24. Emulation efficiency 1000 BG/C nodes (10x10x10) Each with 200 threads (total of 200,000 user-level threads) But Data is preliminary, based on one simulation PPL-Dept of Computer Science, UIUC

  25. Emulator to Simulator • Step 1: Coarse grained simulation • Simulation: performance prediction capability • Models contention for processor/thread • Also models communication delay based on distance • Doesn’t model memory access on chip, or network • How to do this in spite of out-of-order message delivery? • Rely on determinism of Charm++ programs • Time stamped messages and threads • Parallel time-stamp correction algorithm PPL-Dept of Computer Science, UIUC

  26. Timestamp correction • Basic execution: • Timestamped messages • Correction needed when: • A message arrives with an earlier timestamp than other messages “processed” already • Cases: • Messages to Handlers or simple objects • MPI style threads, without wildcard or irecvs • Charm++ with dependence expressed via structured dagger PPL-Dept of Computer Science, UIUC

  27. RecvTime Execution TimeLine M1 M2 M3 M4 M5 M6 M7 M8 Timestamps Correction PPL-Dept of Computer Science, UIUC

  28. RecvTime Execution TimeLine M1 M2 M3 M8 M4 M5 M6 M7 Timestamps Correction PPL-Dept of Computer Science, UIUC

  29. RecvTime Execution TimeLine M1 M2 M3 M4 M5 M6 M7 M8 RecvTime Execution TimeLine M1 M2 M3 M8 M4 M5 M6 M7 Correction Message Timestamps Correction PPL-Dept of Computer Science, UIUC

  30. RecvTime Execution TimeLine M1 M2 M3 M4 M5 M6 M7 M4 M4 Correction Message (M4) RecvTime Execution TimeLine M1 M2 M4 M3 M5 M6 M7 Correction Message Correction Message (M4) RecvTime Execution TimeLine M1 M2 M3 M5 M6 M4 M7 Correction Message Timestamps Correction PPL-Dept of Computer Science, UIUC

  31. Applications on the current system • Using BG Charm++ • LeanMD: • Research quality Molecular Dyanmics • Version 0: only electrostatics + van der Vaal • Simple AMR kernel • Adaptive tree to generate millions of objects • Each holding a 3D array • Communication with “neighbors” • Tree makes it harder to find nbrs, but Charm makes it easy PPL-Dept of Computer Science, UIUC

  32. Emulator to Simulator • Step 2: Add fine grained procesor simulation • Sarita Adve: RSIM based simulation of a node • SMP node simulation: completed • Also: simulation of interconnection network • Millions of thread units/caches to simulate in detail? • Step 3: Hybrid simulation • Instead: use detailed simulation to build model • Drive coarse simulation using model behavior • Further help from compiler and RTS PPL-Dept of Computer Science, UIUC

  33. Modeling layers Applications For each: need a detailed simulation and a simpler (e.g. table-driven) “model” Libraries/RTS Chip Architecture Network model And methods for combining them PPL-Dept of Computer Science, UIUC

  34. Summary • Charm++ (data-driven migratable objects) • is a well-matched candidate programming model for M-PIMs • We have developed an Emulator/Simulator • For BG/C • Runs on parallel machines • We have Implemented multi-million object applications using Charm++ • And tested on emulated Blue Gene/C • More info: http://charm.cs.uiuc.edu • Emulator is available for download, along with Charm PPL-Dept of Computer Science, UIUC

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