Presentation is loading. Please wait.

Presentation is loading. Please wait.

1 Parallel Simulation Made Easy With OMNeT++ Y. Ahmet Şekerciuğlu 1, András Varga 2, Gregory K. Egan 1 1 CTIE, Monash University, Melbourne, Australia.

Similar presentations

Presentation on theme: "1 Parallel Simulation Made Easy With OMNeT++ Y. Ahmet Şekerciuğlu 1, András Varga 2, Gregory K. Egan 1 1 CTIE, Monash University, Melbourne, Australia."— Presentation transcript:

1 1 Parallel Simulation Made Easy With OMNeT++ Y. Ahmet Şekerciuğlu 1, András Varga 2, Gregory K. Egan 1 1 CTIE, Monash University, Melbourne, Australia 2 Omnest Global, Inc.

2 2 What is OMNeT++? An open-source, generic simulation framework -- best suited for simulation of communication networks, multiprocessors and other complex distributed systems (further examples: queuing systems, hardware architectures, server farm, business processes, call centers) C++-based simulation kernel plus a set of libraries and tools (GUI and command-line); platform: Unix, Windows Active user community (mailing list has about 240 subscribers) Home page: Commercial version also exists:

3 3 Model Structure Component-oriented approach: The basic building block is a simple module (programmed in C++). Simple modules can be grouped to form compound modules. Modules are connected with each other.

4 4 Defining the Topology NED (Network Description Language) defines topology: what modules exist, how they are connected and assembled to form larger modules // // Host with an Ethernet interface // module EtherStation parameters:... gates:... submodules: app: EtherTrafficGen; llc: EtherLLC; mac: EtherMAC; connections: app.out --> llc.hl_in; <-- llc.hl_out; llc.ll_in <-- mac.hl_out; llc.ll_out --> mac.hl_in; mac.ll_in <-- in; mac.ll_out --> out; endmodule The graphical editor GNED operates directly on NED files

5 5 Defining the Behaviour Behaviour is encapsulated in simple modules. A simple module: sends messages, reacts to received messages collects statistics Simple modules are programmed C++ one can choose between process-oriented or event-oriented programming simulation class library covers commonly needed functionality, such as: random number generation, statistics colleection (histograms, etc), queues and other containers, support for topology discovery and routing, etc.

6 6 Running the Model Under the GUI Without extra programming, one can: run or single-step the simulation examine scheduled events explore modules and see message flow monitor state of simulation and execution speed see what’s happening inside the model examine model object tree

7 7 Exploring Model Internals examine contents of queues, messages and other objects look at state variables and statistics trace what one module is doing step to next event in a module find out pointer values for C++ debugging (gdb)

8 8 Exploring Model Internals look at results being recorded and much more… or any objects by their names find all messages, all statistics objects or all queues (NEW) and inspect them

9 9 Modular Architecture UI and simulations are separated, and interact via a well-defined API provides command-line and graphical user interface; user interfaces can be customized, or specialized ones can be created enables embedding of simulations into larger applications user interfacesimulation SIM ENVIR main() CMDENV or TKENV or... Simulation model Model Component Library

10 10 Large-Scale Network Simulations PDES: Parallel Discrete Event Simulation Motivation: speedup: make use of multiple CPUs to reduce execution time ability to run large models by distributing resource requirements We want to use clusters that can provide supercomputing power at affordable costs -- inexpensive workstations connected via a high-speed network example: VPAC Linux cluster contains 96 IBM xSeries (dual 2.8GHz Xeon) PCs running Linux 2.4 yielding Gflops; Myrinet interconnection provides 4μs end-to-end delay communication method: MPI MPI (Message Passing Interface) is a standard for high-performance computing several implementations exist: LAM/MPI, MPICH, plus vendor-specific implementations

11 11 Why do we need large-scale simulations? Research on Internet protocols and technologies extensively relies on simulation Systems are too large and too complex for analytic treatment Small experimental networks do not reflect large-scale dynamics Large-scale simulations (10,000-1,000,000 nodes) are needed to: … properly understand dynamics of routing protocols … to test various extensions proposed to improve performance of current Internet protocols … to demonstrate scalability of multicast protocols … plus more

12 12 Parallel DES Partitioning to Logical Processes (LPs): LP1 (on CPU1) LP2 (on CPU2) LP3 (on CPU3) Each partition maps to a separate LP with its own virtual time and list of scheduled events (Future Events Set) LPs are executed on different processors Synchronization mechanism (e.g. null messages; Chandy-Misra-Bryant 1979) is needed to prevent incausalities from happening

13 13 PDES Support in OMNeT++ To try running existing OMNeT++ models in parallel, you only need to: 1. enable parallel simulation parallel-simulation=true 2. specify partitioning in configuration file 3. run

14 14 PDES Support in OMNeT++ Nearly every model can be run in parallel. Constraints: modules may communicate via sending messages only (no direct method call or member access) unless mapped to the same processor no global variables limitations on direct sending (no sending to a submodule of another module, unless mapped to the same processor) lookahead must be present in the form of link delays currently we only support static topologies (this can be improved) Models run without modification (no special instrumentation needed) Partitioning is part of configuration, no model change required follows “separation of model from experiments” principle Code will be publicly released before end 2003 (available on request until then)

15 15 Extensible PDES Architecture Pluggable communication library (“transport layer”): currently implemented: MPI (Message Passing Interface), named pipes, shared directory (for demonstration and debug purposes only) Pluggable PDES algorithm: currently implemented: Null Message Algorithm, Ideal Simulation Protocol (for benchmarking), no synchronization (to demonstrate the need for synchronization)

16 16 Simulation Kernel Parallel Simulation Architecture Parallel simulation subsystem Synchronization Communication Partition (LP) Simulation Model Event scheduling, sending, receiving communications library (MPI, sockets, etc.)

17 17 Communication Layer Must implement the following abstract interface: /** * Provides an abstraction layer above MPI, * PVM, shared-memory communications, etc… */ class cParsimCommunications { virtual void init() = 0; virtual void shutdown() = 0; virtual cCommBuffer *createCommBuffer() = 0; virtual void recycleCommBuffer(cCommBuffer *buffer) = 0; virtual void send(cCommBuffer *buffer, int tag, int destination) = 0; virtual void boadcast(cCommBuffer *buffer, int tag) = 0; virtual void receiveBlocking(cCommBuffer *buffer, int& rcvdTag,int& srcProcId) = 0; virtual bool receiveNonblocking(cCommBuffer *buffer, int& rcvdTag, int& srcProcId) = 0; virtual void synchronize() = 0; }; class cMPICommunications : public cParsimCommunications { … }; class cNamedPipeCommunications : public cParsimCommunications { … }; class cFileCommunications : public cParsimCommunications { … }; Communication buffers encapsulate pack/unpack operations. The cCommBuffer interface (abstract class) has multiple implementations for MPI, etc. Simulation objects are able to pack/unpack themselves to/from communication buffers, using methods from the cCommBuffer interface.

18 18 Model Partitioning OMNeT++ uses placeholder modules and proxy gates: nodeB (placeholder) nodeA CPU0 nodeB nodeA (placeholder) CPU1 communication (MPI, pipe, etc.)

19 19 (placeholder for compound module) Model Partitioning, cont’d If compound modules themselves are distributed across LPs, the solution is slightly more complicated: simple module CPU0 simple module (placeholder) CPU1 (placeholder) CPU2 simple module

20 20 Placeholder Approach Advantage of placeholder approach: when simulating telecommunication networks, all nodes (routers, ASes, hosts, etc) are present (at least as placeholders) in all LPs, so algorithms such as topology discovery for routing can proceed unhindered. LP1 (on CPU1) placeholders

21 21 Synchronization Layer /** * Abstract base class for parallel simulation algorithms... */ class cParsimSynchronizer : public cScheduler { virtual void startRun() = 0; virtual void endRun() = 0; /** * Scheduler function -- it comes from cScheduler interface... */ virtual cMessage *getNextEvent() =0; /** * Hook, called when a cMessage is sent out of the segment... */ virtual void processOutgoingMessage(cMessage *msg, int procId, int moduleId, int gateId, void *data) = 0; }; Parallel simulation protocols must implement the following abstract interface:

22 22 Synchronization Layer Currently implemented parallel simulation algorithms:

23 23 Example: Distributed CQN Closed Queuing Network (CQN) described in the “Performance Evaluation of Conservative Algorithms”, R. Bagrodia et al., 2000 N tandem queues (switch+queues); exponential service times; propagation delay all links link Lookahead: propagation delay on links CPU2 CPU1 CPU0

24 24 Example: Distributed CQN OMNeT++ model for CQN wraps tandems into compound modules

25 25 [General] parallel-simulation=true #parsim-communications-class=“cFileCommunications" parsim-communications-class="cMPICommunications" parsim-synchronization-class= "cNullMessageProtocol" [Partitioning] *.tandemQueue[0]*.segment-id=0 *.tandemQueue[1]*.segment-id=1 *.tandemQueue[2]*.segment-id=2 Configuring for Parallel Execution Configuration file: enable parallel simulation select communication library and parallel simulation protocol assign modules to processors Each partition is simulated in its own process.

26 26 CQN Partitioning in Tkenv If simulation executes under the GUI, placeholder modules and proxy gates are shown

27 27 Running Parallel Simulation If GUI is used, operation of the Null Message Algorithm can be followed in trace windows

28 28 Experimental Results Present simulation framework was used to verify the efficiency criterion for the Null Message Algorithm: LE >> 1 and λ=LE/  P >> 1 are necessary for efficient PDES execution see paper “A Practical Efficiency Criterion For The Null Message Algorithm”, András Varga, Y. Ahmet Şekerciuğlu, Gregory K. Egan in the Proceedings

29 29 Ongoing Work Optimisations on the parallel simulation kernel Create support for node mobility across LPs Test large-scale IPv6 simulations (using the IPv6Suite for OMNeT++, developed at CTIE, Monash University, Australia) Further verification and refinement of the efficiency criteria for the Null-Message Algorithm

Download ppt "1 Parallel Simulation Made Easy With OMNeT++ Y. Ahmet Şekerciuğlu 1, András Varga 2, Gregory K. Egan 1 1 CTIE, Monash University, Melbourne, Australia."

Similar presentations

Ads by Google