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Efficient Optimistic Parallel Simulations Using Reverse Computation

Efficient Optimistic Parallel Simulations Using Reverse Computation. Chris Carothers Department of Computer Science Rensselaer Polytechnic Institute Kalyan Permulla and Richard M. Fujimoto College of Computing Georgia Institute of Technology. Why Parallel/Distributed Simulation?.

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Efficient Optimistic Parallel Simulations Using Reverse Computation

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  1. Efficient Optimistic Parallel Simulations Using Reverse Computation Chris Carothers Department of Computer Science Rensselaer Polytechnic Institute Kalyan Permulla and Richard M. Fujimoto College of Computing Georgia Institute of Technology

  2. Why Parallel/Distributed Simulation? • Goal: speed up discrete-event simulation programs using multiple processors • Enabling technology for… • intractable simulation models tractable • off-line decision aides on-line aides for time critical situation analysis • DPAT: A distributed simulation success story • simulation model of the National Airspace • developed @ MITRE using Georgia Tech Time Warp (GTW) • simulates 50,000 flights in < 1 minute, which use to take 1.5 hours. • web based user-interface • to be used in the FAA Command Center for on-line “what if” planning • Parallel/distributed simulation has the potential to improve how “what if” planning strategies are evaluated

  3. How to Synchronize Distributed Simulations? 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 processed event “straggler” event

  4. 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

  5. Time Warp P P P P P P P P P Shared Memory or High Speed Network Challenge: Efficient Implementation? • Advantages: • automatically finds available parallelism • makes development easier • outperforms conservative schemes by a factor of N • Disadvantages: • Large memory requirements to support rollback operation • State-saving incurs high overheads for fine-grain event computations • Time Warp is out of “performance” envelop for many applications Our Solution: Reverse Computation

  6. Outline... • Reverse Computation • Example: ATM Multiplexor • Beneficial Application Properties • Rules for Automation • Reversible Random Number Generator • Experimental Results • Conclusions • Future Work

  7. Compiler Original Code Modified Code Reverse Code 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

  8. Forward Reverse if( qlen < B ) b1 = 1 qlen++ delays[qlen]++ else b1 = 0 lost++ if( b1 == 1 ) delays[qlen]-- qlen-- else lost-- Example: ATM Multiplexor Original N if( qlen < B ) qlen++ delays[qlen]++ else lost++ B on cell arrival...

  9. Gains…. • State size reduction • from B+2 words to 1 word • e.g. B=100 => 100x reduction! • Negligible overhead in forward computation • removed from forward computation • moved to rollback phase • Result • significant increase in speed • significant decrease in memory • How?...

  10. 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

  11. Rules for Automation... Generation rules, and upper-bounds on bit requirements for various statement types

  12. 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)

  13. Reversing an RNG? double RNGGenVal(Generator g) { long k,s; double u; u = 0.0; s = Cg [0][g]; k = s / 46693; s = 45991 * (s - k * 46693) - k * 25884; if (s < 0) s = s + 2147483647; Cg [0][g] = s; u = u + 4.65661287524579692e-10 * s; s = Cg [1][g]; k = s / 10339; s = 207707 * (s - k * 10339) - k * 870; if (s < 0) s = s + 2147483543; Cg [1][g] = s; u = u - 4.65661310075985993e-10 * s; if (u < 0) u = u + 1.0; s = Cg [2][g]; k = s / 15499; s = 138556 * (s - k * 15499) - k * 3979; if (s < 0.0) s = s + 2147483423; Cg [2][g] = s; u = u + 4.65661336096842131e-10 * s; if (u >= 1.0) u = u - 1.0; s = Cg [3][g]; k = s / 43218; s = 49689 * (s - k * 43218) - k * 24121; if (s < 0) s = s + 2147483323; Cg [3][g] = s; u = u - 4.65661357780891134e-10 * s; if (u < 0) u = u + 1.0; return (u); } Observation: k = s / 46693 is a Destructive AssignmentResult: RC degrades to classic state-saving…can we do better?

  14. RNGs: A Higher Level View The previous RNG is based on the following recurrence…. xi,n = aixi,n-1 mod mi where xi,none of the four seed values in the Nth set, miis one the four largest primes less than 231, and ai is a primitive root of mi. Now, the above recurrence is in fact reversible…. inverse of aimodulo mi is defined, bi = aimi-2 mod mi Using bi, we can generate the reverse recurrence as follows: xi,n-1 = bixi,n mod mi

  15. Reverse Code Efficiency... • Future RNGs may result in even greater savings. • Consider the MT19937 Generator... • Has a period of 219937 • Uses 2496 bytes for a single “generator” • Property... • Non-reversibility of indvidual steps DO NOT imply that the computation as a whole is not reversible. • Can we automatically find this “higher-level” reversibility? • Other Reversible Structures Include... • Circular shift operation • Insertion & deletion operations on trees (i.e., priority queues). Reverse computation is well-suited for queuing network models!

  16. Platform SGI Origin 2000, 16 processors (R10000), 4GB RAM • Model • 3 levels of multiplexers, fan-in N • N^3 sources => N^3 + N^2 + N + 1entities in total • eg. N=4 => entities=85, N=64 => entities=266,305 Performance Study

  17. Why the large increase in parallel performance? million events/second

  18. Cache Performance... FaultsTLB P cache S cache • SS 12pe: 43966018 1283032615 162449694 • RC 12pe: 11595326 590555715 94771426

  19. Related Work... • Reverse computation used in • low power processors, debugging, garbage collection, database recovery, reliability, etc. • All previous work either • prohibit irreversible constructs, or • use copy-on-write implementation for every modification(correspond to incremental state saving) • Many operate at coarse, virtual page-level

  20. Contributions We identify that • RC makes Time Warp usable for fine-grain models! • disproved previous beliefthat “fine grain models can’t be optimistically simulated efficiently” • less memory consumption, more speed, without extra user effort • RC generalizes state saving • e.g., incremental state saving, copy state saving • For certain data types, RC is more memory efficient than SS • e.g., priority queues

  21. Future Work • Develop state minimization algorithms, by • State compression:bit size for reversibility < bit size of data variables • State reuse:same state bits for different statements • based on liveness, analogous to register allocation • Complete RC automation algorithm designavoiding the straightforward incremental state saving approach • Lossy integer and floating point arithmetic • Jump statements • Recursive functions

  22. Myrinet Geronimo! System Architecture High Performance Simulation Application Geronimo distributed compute server rack-mounted CPUs (not in demonstration) multiprocessor Geronimo Features: (1) “risky” or “speculative” processing of object computations, (2) reverse computation to support “undo” operation, (3) “Active Code” in a combination, heterogeneous, shared-memory, message passing environment...

  23. Error detection and Rollback V i r t u a l T i m e (1) undo state D’s (2) cancel “scheduled” tasks Object 2 Object 3 Object 1 Geronimo!: “Risky” Processing... • Execution Framework: • Objects • schedule Threads / Tasks • at some “virtual time” • Applications: • discrete-event simulations • scientific computing applications processed thread CAVEAT: Good performance relies on cost of recovery * probability of failure being less than cost of being “safe”! “straggler” thread unprocessed thread

  24. Geronimo!: Efficient “Undo” • Traditional approach: State Saving • save byte-copies of modified items • high overhead for fine-granularity computations • memory utilization is large • need alternative for large-scale, fine-grain simulations • Our approach: Reverse Computation • automatically generate reverse code from model source • utilize reverse code to do rollback • negligible overhead for forward computation • significantly lower memory utilization • joint with Kalyan Perumalla and Richard Fujimoto Observation: “reverse” computation treats “code” as“state”. This results in a code-state duality.Can we generalize notion?…..

  25. Geronimo!: Active Code • Key idea: allow object methods/code to be dynamically changed during run-time. • objects can schedule in the future a new method or re-define old methods of other objects and themselves. • objects can erase/delete methods on themselves or other objects. • new methods can contain “Active Code” which can re-specialize itself or other objects. • work in a heterogeneous environment. • How is this useful? • increase performance by allowing the program to consistently “execute the common case fast”. • adaptive, perturbation-free, monitoring of distributed systems. • potential for increasing a language’s “expressive power”. • Our approach? • Java…no, need higher performance…maybe used in the future... • special compiler…no, can’t keep up with changes to microprocessors.

  26. Geronimo!: Active Code Implementation • Runtime infrastructure • modifies source code tree • start a rebuild of the executable on a another existing machine • uses a system’s naïve compiler • Re-exec system call • reloads only the new text or code segment of new executable • fix-up old stack to reflect new code changes • fix-up pointers to functions • will run in “user-space” for portability across platforms • Language preprocessor • instruments code to support stack and function pointer fix-up • instruments code to support stack reconstruction and re-start process

  27. Research Issues • Software architecture for the heterogeneous, shared-memory, message passing environment. • Development of distributed algorithms that are fully optimized for this “combination” environment. • What language to use for development, C or C++ or both? • Geronimo! API. • Active Code Language and Systems Support. • Mapping relevant application types to this framework Homework Problem: Can you find specific applications/problems where we can apply Geronimo!?

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