1. RESAM Laboratory Univ. Lyon 1, France lead by Prof. B. Tourancheau Laurent Lefèvre CongDuc Pham Pascale Primet PhD. student Patrick Geoffray Roland Westrelin

2. Research interests High-performance communication systems –Myrinet-based clusters, cluster management –BIP, MPI-BIP, BIP-SMP Distributed Shared Memory systems –DOSMOS system Network support for Multimedia and Cooperative applications –QoS, multicast –CoTool environment Parallel simulation, synchronization algorithms, communication network models –CSAM tools

3. Parallel and Distributed Simulation of Communication Networks (towards cluster-based solution) C.D. Pham RESAM laboratory Univ. Lyon 1, France cpham@lhpca.univ-lyon1.fr

4. Outline Introduction –Discrete Event Simulation (DES) –Parallel DES and the synchronization problems Conservative protocols –Architecture of a conservative LP –The Chandy-Misra-Bryant protocol –The lookahead ability Optimistic protocols –Architecture of an optimistic LP –Time Warp

5. Outline, more... CSAM, a tools for ATM network models –kernel characteristics –results Cluster-based solutions –Myrinet, BIP, BIP-SMP, MPI/BIP, MPI/BIP-SMP –Fast Ethernet, Gamma? –GigaEthernet?

6. Introduction Discrete Event Simulation (DES) Parallel DES and synchronization problems

7. Discrete Event Simulation (DES) assumption that a system changes its state at discrete points in simulation time a1a2a3a4d1d2d3 S1S3 S2 0 tt 2t2t3t3t4t4t5t5t6t6t NOT...

8. DES concepts fundamental concepts: –system state (variables) –state transitions (events) –simulation time: totally ordered set of values representing time in the system being modeled the system state can only be modified upon reception of an event modeling can be –event-oriented –process-oriented

9. Life cycle of a DES a DES system can be viewed as a collec- tion of simulated objects and a sequence of event computations each event computation contains a time stamp indicating when that event occurs in the physical system each event computation may: –modify state variables –schedule new events into the simulated future events are stored in a local event list –events are processed in time stamped order –usually, no more event = termination

10. A simple DES model local event list A B 5 link model delay = 5 send processing time = 5 receive processing time = 1 packet arrival P1 at 5, P2 at 12, P3 at 22 B receive P1 from A e4 B sends ACK(P1) to A e5 e8 B receive P2 from A A sends P1 to B e2 A receive packet P1 e1 A sends P2 to B e6 A receive packet P2 e3 A receive packet P3 e9 e7 A receive ACK(P1)

11. Why it works? events are processed in time stamp order an event at time t can only generate future events with timestamp greater or equal to t (no event in the past) generated events are put and sorted in the event list, according to their timestamp –the event with the smallest timestamp is always processed first, –causality constraints are implicitly maintained.

12. Why change? It ’s so simple! models becomes larger and larger the simulation time is overwhelming or the simulation is just untractable example: –parallel programs with millions of lines of codes, –mobile networks with millions of mobile hosts, –ATM networks with hundreds of complex switches, –multicast model with thousands of sources, –ever-growing Internet, –and much more...

13. Some figures to convince... ATM network models –Simulation at the cell-level, –200 switches –1000 traffic sources, 50Mbits/s –155Mbits/s links, –1 simulation event per cell arrival. –simulation time increases as link speed increases, –usually more than 1 event per cell arrival, –how scalable is traditional simulation? More than 26 billions events to simulate 1 second! 30 hours if 1 event is processed in 1us

14. Parallel simulation - principles execution of a discrete event simulation on a parallel or distributed system with several physical processors. the simulation model is decomposed into several sub-models that can be executed in parallel –spacial partitioning, –temporel partitioning, radically different from simple simulation replications.

15. Parallel simulation - pros & cons pros –reduction of the simulation time, –increase of the model size, cons –causality constraints are difficult to maintain, –need of special mechanisms to synchronize the different processors, –increase both the model and the simulation kernel complexity. challenges –ease of use, transparency.

16. Parallel simulation - example logical process (LP) packetheventt parallel

17. A simple PDES model local event list A B 5 link model delay = 5 send processing time = 5 receive processing time = 1 packet arrival P1 at 5, P2 at 12, P3 at 22 B sends ACK(P1) e5 A sends P1 to B e2 e6 A sends P2 to B A rec. packet P1 e1 A rec. packet P2 e3 B rec. P1 from A e4 B rec. P2 from A e8 e7 A rec. ACK(P1) t e9 A rec. packet P3 causality error, violation

18. Synchronization problems fundamental concepts –each Logical Process (LP) can be at a different simulation time –local causality constraints: events in each LP must be executed in time stamp order synchronization algorithms –Conservative: avoids local causality violations by waiting until it ’s safe –Optimistic: allows local causality violations but provisions are done to recover from them at runtime

19. Conservative protocols Architecture of a conservative LP The Chandy-Misra-Bryant protocol The lookahead ability

20. Architecture of a conservative LP –LPs communicate by sending non-decreasing timestamped messages –each LP keeps a static FIFO channel for each LP with incoming communication –each FIFO channel (input channel, IC) has a clock c i that ticks according to the timestamp of the topmost message, if any, otherwise it keeps the timestamp of the last message LP B LP A LP C LP D c 1 =t B 1 tB1tB1 tB2tB2 tC3tC3 tC4tC4 tC5tC5 tD4tD4 c 2 =t C 3 c 3 =t D 3

21. A simple conservative algorithm each LP has to process event in time- stamp order to avoids local causality violations The Chandy-Misra-Bryant algorithm while (simulation is not over) { determine the IC i with the smallest C i if (IC i empty) wait for a message else { remove topmost event from IC i process event } }

22. Safe but has to block LP B LP A LP C LP D 36 147 10 5 IC 1 IC 2 IC 3 min IC event 1 2 3 1 4 2 5 3 BLOCK 3 6 1 7 2 9

23. Blocks and even deadlocks! S A B M merge point BLOCKED cycle S sends all messages to B 4 4 4 4 4 6

24. How to solve deadlock: null-messages S A B M null-messages for artificial propagation of simulation time 10 4 4 4 4 5 6 7 1 2 UNBLOCKED What frequency?

25. How to solve deadlock: null-messages a null-message indicates a Lower Bound Time Stamp minimum delay between links is 4 LP C initially at simulation time 0 119 10 7 ABC 4 LP C sends a null-message with time stamp 4 LP A sends a null-message with time stamp 8 8 LP B sends a null-message with time stamp 12 12 LP C can process event with time stamp 7 12

26. The lookahead ability null-messages are sent by an LP to indicate a lower bound time stamp on the future messages that will be sent null-messages rely on the « lookahead » ability –communication link delays –server processing time (FIFO) lookahead is very application model dependant and need to be explicitly identified

27. Lookahead for concurrent processing LP B LP A LP C LP D s TATA T A +L A ss ss ss s safe event unsafe event

28. What if lookahead is small? a null-message indicates a Lower Bound Time Stamp minimum delay between links is 4 LP C initially at simulation time 0 119 10 7 AB C 1 LP C sends a null-message with time stamp 1 LP A sends a null-message with time stamp 2 2 LP B sends a null-message with time stamp 3 3 LP C can process event with time stamp 7 7 1 then 5 5 then 6 6 then 7 7

29. Conservative: pros & cons pros –simple, easy to implement –good performance when lookahead is large (communication networks, FIFO queue) cons –pessimistic in many cases –large lookahead is essential for performance –no transparent exploitation of parallelism –performances may drop even with small changes in the model (adding preemption, adding one small lookahead link…)

30. Optimistic protocols Architecture of an optimistic LP Time Warp

31. Architecture of an optimistic LP –LPs send timestamped messages, not necessarily in non-decreasing time stamp order –no static communication channels between LPs, dynamic creation of LPs is easy –each LP processes events as they are received, no need to wait for safe events –local causality violations are detected and corrected at runtime LP B LP A LP C LP D tB1tB1 tB2tB2 tC3tC3 tC4tC4 tC5tC5 tD4tD4

32. Processing events as they arrive 11 LP B 13 LP D 18 LP B 22 LP C 25 LP D 28 LP C 36 LP B 32 LP D LP B LP A LP C LP D LP A processed! what to do with late messages?

33. TimeWarp. Rollback? How? Late messages (stragglers) are handled with a rollback mechanism –undo false/uncorrect local computations, state saving: save the state variables of an LP reverse computation –undo false/uncorrect remote computations, anti-messages: anti-messages and (real) messages annihilate each other –process late messages –re-process previous messages: processed events are NOT discarded!

34. A pictured-view of a rollback 11131822252836 32 4345 2513 state points anti-msg 131520242730381118222832 343830 36 –The real rollback distance depends on the state saving period: short period reduces rollback overhead but increases state saving overhead 11131822252832364345 283236 unprocessed processed

35. Reception of an anti-message –may initiate a rollback if the corresponding positive message has already been processed, –may annihilate the corresponding positive message if it is still unprocessed, –may wait in the input queue if the corresponding positive message has not been received yet. 222528364345 43 222528364345 48 22252836434525 rollback 48

36. Need for a Global Virtual Time Motivations –an indicator that the simulation time advances –reclaim memory (fossil collection) Basically, GVT is the minimum of –all LPs ’ logical simulation time –timestamp of messages in transit GVT garantees that –events below GVT are definitive events (I/O) –no rollback can occur before the GVT –state points before GVT can be reclaimed –anti-messages before GVT can be reclaimed

37. A pictured-view of the GVT LP B LP A LP C LP D c c old GVT ccc cccc cccc new GVT cccc c D conditional event definitive event cc c cc c c c c c cc DD D D D D D D D WANTED

38. Optimistic overheads Periodic state savings –states may be large, very large! –copies are very costly Periodic GVT computations –costly in a distributed architecture, –may block computations, Rollback thrashing –cascaded rollback, no advancement! Memory! –memory is THE limitation

39. Optimistic: pros & cons pros –exploits all the parallelism in the model, lookahead is less important, –transparent to the end-user –interactive simulations can be enabled –can be hopefully general-purpose cons –very complex, needs lots of memory, –large overheads (state saving, GVT, rollbacks…)

40. Optimizations, variations Conservative Optimistic Mixed approaches, adaptive approaches,

41. Conservative: outline Add more information to reduce the number of null-messages –special msg: carrier null-messages [Cai90] –topology information: [DeVries90] –time/delay information: Bounded Lag [Lubuchewsky89] –time window: CTW [Ayani92] In general, one tries to add additional knowledge of the model in the simulator, may not be general-purpose

42. Optimistic: outline Reduce rollback-related overhead –lazy-cancellation, lazy re-evaluation –limit optimism (time window, blocking) Reduce memory comsumtion –fast GVT algorithms, hardware support –incremental state saving, reverse computation –cancelback, artificial rollback In general, one tries to reduce the optimism to avoid to many computation speculations

43. Mixed/adaptive approaches General framework that (automatically) switches to conservative or optimistic Adaptive approaches may determine at runtime the amount of conservatism or optimism conservativeoptimistic mixed messages performance optimistic conservative

44. Parallel simulation today Lots of algorithms have been proposed –variations on conservative and optimistic –adaptives approaches Few end-users –impossible to compete with sequential simulators in terms of user interface, generability, ease of use etc. Research mainly focus on –applications, ultra-large scale simulations –tools and execution environments (clusters) –composability issues

45. CSAM (Pham, UCBL) CSAM: Conservative Simulator for ATM network Model Simulation at the cell-level Conservative and/or sequential C++ programming-style, predefined generic model of sources, switches, links… New models can be easily created by deriving from base classes Configuration file that describes the topology

46. CSAM - Kernel characteristics Exploits the lookahead of communication links: transparent for the user Virtual Input Channels –reduces overhead for event manipulation, –reduces overhead for null-messages handling. Cyclic event execution Message aggregation –static aggregation size, –asymmetric aggregation size on CLUMPS, –sender-initiated, –receiver-initiated.

47. CSAM - Life cycle

48. Test case: 78-switch ATM network Distance-Vector Routing with dynamic link cost functions Connection setup, admission control protocols

49. CSAM - Some results... Routing protocol’s reconfiguration time

50. CSAM - Some results... End-to-end delays

51. Cluster-based solution Myrinet-based cluster of 12 Pentium Pro at 200MHz, 64 MBytes, Linux Myrinet-based cluster of 4 dual Pentium Pro 450MHz, 128 Mbytes, Linux Myrinet board with LANai 4.1, 256KB BIP, BIP-SMP, MPI/BIP, MPI/BIP-SMP communication libraries

52. CSAM - speedup on a myrinet cluster Pentium Pro 200MHz More than 53 millions events to simulate 0.31s

53. CSAM - speedup with CLUMPS Dual Pentium Pro 450MHz

54. Increasing the model size (CLUMPS) Dual Pentium Pro 450MHz, 4x2 int

55. Conclusions Parallel simulation techniques can be successfully be applied to communication network models To enable the PSTTL approach (parallel simulation to the labs), we need –powerful software, –powerful BUT, cheap and accessible, execution environment –Myrinet? Fast Ethernet? Giga Ethernet? We will always take the cheapest if performance are good

56. References Parallel simulation –K. M. Chandy and J. Misra, Distributed Simulation: A Case Study in Design and Verification of Distributed Programs, IEEE Trans. on Soft. Eng., 1979, pp440-452 –R. Fujimoto, Parallel Discrete Event Simulation, Comm. of the ACM, Vol. 33(10), Oct. 90, pp31-53 –http://