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ROSS: Parallel Discrete-Event Simulations on Near Petascale Supercomputers

Christopher D. Carothers Department of Computer Science Rensselaer Polytechnic Institute chrisc@cs.rpi.edu. ROSS: Parallel Discrete-Event Simulations on Near Petascale Supercomputers. Outline. Motivation for PDES Overview of HPC Platforms ROSS Implementation Performance Results

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ROSS: Parallel Discrete-Event Simulations on Near Petascale Supercomputers

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  1. Christopher D. Carothers Department of Computer Science Rensselaer Polytechnic Institute chrisc@cs.rpi.edu ROSS: Parallel Discrete-Event Simulations on Near Petascale Supercomputers

  2. Outline • Motivation for PDES • Overview of HPC Platforms • ROSS Implementation • Performance Results • Summary

  3. Motivation Why Parallel Discrete-Event Simulation (DES)? • Large-scale systems are difficult to understand • Analytical models are often constrained Parallel DES simulation offers: • Dramatically shrinks model’s execution-time • Prediction of future “what-if” systems performance • Potential for real-time decision support • Minutes instead of days • Analysis can be done right away • Example models: national air space (NAS), ISP backbone(s), distributed content caches, next generation supercomputer systems.

  4. Model a 10 PF Supercomputer • Suppose we want to model a 10 PF supercomputer at the MPI message level • How long excute DES model? • 10% flop rate  1 PF sustained • @ .2 bytes/sec per flop @ 1% usage  2 TB/sec • @ 1K size MPI msgs  2 billion msgs per simulated second • @ 8 hops per msg  16 billion “events” per simulated second • @ 1000 simulated seconds  16 trillion events for DES model • No I/O included !! • Nominal seq. DES simulator  100K events/sec • 16 trillion events @ 100K ev/sec 5+ years!!! Need massively parallel simulation to make tractable

  5. Blue Gene /L Layout • CCNI “fen” • 32K cores/ 16 racks • 12 TB / 8 TB usable RAM • ~1 PB of disk over GPFS • Custom OS kernel

  6. Blue Gene /P Layout • ALCF/ANL “Intrepid” • 163K cores/ 40 racks • ~80TB RAM • ~8 PB of disk over GPFS • Custom OS kernel

  7. Blue Gene: L vs. P

  8. parallel time-stepped simulation: lock-step execution parallel discrete-event simulation: must allow for sparse, irregular event computations barrier Problem: events arriving in the past Solution: Time Warp Virtual Time Virtual Time PE 2 PE 3 PE 1 PE 2 PE 3 PE 1 How to Synchronize Parallel Simulations? processed event “straggler” event

  9. Massively Parallel Discrete-Event Simulation Via Time Warp Local Control Mechanism: error detection and rollback Global Control Mechanism: compute Global Virtual Time (GVT) V i r t u a l T i m e V i r t u a l T i m e collect versions of state / events & perform I/O operations that are < GVT (1) undo state D’s (2) cancel “sent” events GVT LP 2 LP 3 LP 1 LP 2 LP 3 LP 1 unprocessed event processed event “straggler” event “committed” event

  10. Our Solution: Reverse Computation... • Use Reverse Computation (RC) • automatically generate reverse code from model source • undo by executing reverse code • Delivers better performance • negligible overhead for forward computation • significantly lower memory utilization Original Code Compiler Modified Code Reverse Code

  11. Forward Reverse if( qlen < B ) b1 = 1 qlen++ delays[qlen]++ else b1 = 0 lost++ if( b1 == 1 ) delays[qlen]-- qlen-- else lost-- Ex: Simple Network Switch Original N if( qlen < B ) qlen++ delays[qlen]++ else lost++ B on packet arrival...

  12. Beneficial Application Properties 1. Majority of operations are constructive • e.g., ++, --, etc. 2. Size of control state< size of data state • e.g., size of b1 < size of qlen, sent, lost, etc. 3. Perfectly reversible high-level operations gleaned from irreversible smaller operations • e.g., random number generation

  13. Destructive Assignment... • Destructive assignment (DA): • examples: x = y; x %= y; • requires all modified bytes to be saved • Caveat: • reversing technique forDA’s can degenerate to traditional incremental state saving • Good news: • certain collections of DA’s are perfectly reversible! • queueing network models contain collections of easily/perfectly reversible DA’s • queue handling (swap, shift, tree insert/delete, … ) • statistics collection (increment, decrement, …) • random number generation (reversible RNGs)

  14. Original if( qlen < B ) qlen++ delays[qlen]++ else lost++ B packet arrival... Forward Reverse if( qlen < B ) b1 = 1 qlen++ delays[qlen]++ else b1 = 0 lost++ if( b1 == 1 ) delays[qlen]-- qlen-- else lost-- RC Applications • PDES applications include: • Wireless telephone networks • Distributed content caches • Large-scale Internet models – • TCP over AT&T backbone • Leverges RC “swaps” • Hodgkin-Huxley neuron models • Plasma physics models using PIC • Pose -- UIUC • Non-DES include: • Debugging • PISA – Reversible instruction set architecture for low power computing • Quantum computing

  15. Local Control Implementation Local Control Mechanism: error detection and rollback V i r t u a l T i m e • MPI_ISend/MPI_Irecv used to send/recv off core events • Event & Network memory is managed directly. • Pool is allocated @ startup • Event list keep sorted using a Splay Tree (logN) • LP-2-Core mapping tables are computed and not stored to avoid the need for large global LP maps. (1) undo state D’s (2) cancel “sent” events LP 2 LP 3 LP 1

  16. Global Control Implementation Global Control Mechanism: compute Global Virtual Time (GVT) V i r t u a l T i m e GVT (kicks off when memory is low): • Each core counts #sent, #recv • Recv all pending MPI msgs. • MPI_Allreduce Sum on (#sent - #recv) • If #sent - #recv != 0 goto 2 • Compute local core’s lower bound time-stamp (LVT). • GVT = MPI_Allreduce Min on LVTs Algorithms needs efficient MPI collective LC/GC can be very sensitive to OS jitter collect versions of state / events & perform I/O operations that are < GVT GVT LP 2 LP 3 LP 1 So, how does this translate into Time Warp performance on BG/L & BG/P?

  17. Performance Results: Setup • PHOLD • Synthetic benchmark model • 1024x1024 grid of LPs • Each LP has 10 initial events • Event routed randomly among all LPs based on a configurable “percent remote” parameter • Time stamps are exponentially distributed with a mean of 1.0 (i.e., lookahead is 0). • TLM – Tranmission Line Matrix • Discrete electromagnetic propagation wave model • Used model the physical layer of MANETs • As accurate as previous “ray tracing” models, but dramatically faster… • Considers wave attenuation effects • Event populations grows cubically outward from the single “radio” source. • ROSS parameters • GVT_Interval number of times thru “scheduler” loop before computing GVT. • Batch number of local events to process before “check” network for new events. • Batch X GVT_Interval events processed per GVT epoch • KPs kernel processes that hold the aggregated processed event lists for LPs to lower search overheads for fossil collection of “old” events. • Send/Recv Buffers – number of network events for “sending” or “recv’ing”. Used as a flow control mechanism.

  18. 7.5 billion ev/sec for 10% remote on 32,768 cores!! 2.7 billion ev/sec for 100% remote on 32,768 cores!! Stable performance across processor configurations attributed to near noiseless OS…

  19. Performance falls off after just 100 processors on a PS3 cluster w/ Gigabit Eithernet

  20. 12.27 billion ev/sec for 10% remote on 65,536 cores!! 4 billion ev/sec for 100% remote on 65,536 cores!!

  21. Rollback Efficiency = 1 - Erb /Enet

  22. Model a 10 PF Supercomputer (revisited) • Suppose we want to model a 10 PF supercomputer at the MPI message level • How long excute parallel DES model? • 16 trillion events @ 10 billion ev/sec ~27 mins

  23. Observations… • ROSS on Blue Gene indicates billion-events per second model are feasible today! • Yields significant TIME COMPRESSION of current models.. • LP to PE mapping less of a concern… • Past systems where very sensitive to this • ~90 TF systems can yield “Giga-scale” event rates. • Tera-event models require teraflop systems. • Assumes most of event processing time is spent in event-list management (splay tree enqueue/dequeue). • Potential: 10 PF supercomputers will be able to model near peta-event systems • 100 trillion to 1 quadrillion events in less than 1.4 to 14 hours • Current “testbed” emulators don’t come close to this for Network Modeling and Simulation..

  24. Future Models Enabled by X-Scale Computing • Discrete “transistor” level models for whole multi-core architectures… • Potential for more rapid improvements in processor technology… • Model nearly whole U.S. Internet at packet level… • Potential to radically improve overall QoS for all • Model all C4I network/systems for a whole theatre of war faster than real-time many time over.. • Enables the real-time“active” network control..

  25. Future Models Enabled by X-Scale Computing • Realistic discrete model the human brain • 100 billion neurons w/ 100 trillion synapes (e.g. connections – huge fan-out) • Potential for several exa-events per run • Detailed “discrete” agent-based model for every human on the earth for.. • Global economic modeling • pandemic flu/disease modeling • food / water / energy usage modeling… But to get there investments must be made in code that are COMPLETELY parallel from start to finish!!

  26. Thank you!! • Additional Acknowledgments • David Bauer – HPTi • David Jefferson – LLNL for helping us get discretionary access to “Intrepid” @ ALCF • Sysadmins: Ray Loy (ANL), Tisha Stacey (ANL) and Adam Todorski (CCNI) • ROSS Sponsers • NSF PetaApps, NeTS & CAREER programs • ALFC/ANL

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