R. Bagrodia, Parsec Workshop ‘98 Rajive Bagrodia Computer Science Department UCLA Partial support from the TRAVLER and DOMAINS contracts from DARPA ITO

R. Bagrodia, Parsec Workshop ‘98

Rajive BagrodiaComputer Science Department

UCLA

Partial support from the TRAVLER and DOMAINS contractsfrom DARPA ITO


8:00 am - 8:30 am Check-In / Breakfast

8:30 am - 10:30 am PARSEC Simulation Language

Rajive Bagrodia Introduction: Simulation of Complex Scaleable SystemsRich Meyer Tutorial: Language Constructs and Migrating from Maisie

10:30 am - 10:45 am Break

10:45 am – 12:15 am Advanced PARSEC

Xiang Zeng Case Study: Smple-Net SimulatorMineo Takai Scalable & Parallel Simulation in Parsec

12:15 - 1:00 pm Lunch

1:00 pm – 2:45 pm Modeling and Network Applications

L. Bajaj & R. Ahuja Case Study: Network Applications/GloMoSimCong Duc Pham Interoperability: PARSEC and HLA

2:45 pm - 3:00 pm Break

3:00 pm – 4:30 pm Other Applications

Jay Martin Compose/DatabaseAdam Rosenstein Web CachingGary Pei Multicast ProtocolsLan Wang TCP and RSVPRenato Lo Cigno ATM NetworkingL. Bajaj & R. Ahuja Replicated File Systems

4:30 pm - 5:30 pm Demos

Rich Meyer PAVEAdam Rosenstein Web CachingGary Pei Multicast/MultihopMineo Takai GloMoSim

9:00 am - 9:30 am Check-In / Breakfast

9:30 am - Noon Optional Session

Mineo Takai & PCL Application Design Discussion


UAV Network

How does TCP perform in multi-hop wireless networks?

OSPF, MMWN, or WRP routing?

Tactical Internet


Graphic for a TOC

Nomadic Computing Systems

Sample Query: Estimate QoS of the Situation Awareness (SA) data for a given battle scenario with hundreds of replicas


sensor

weapon

threat

c2center

dmaker

War Game Model


Distributed, multihop communications,Multiple consumers/information sinks,Dynamic circuit establishment/teardown

At what density should the sensors be spread to ensure a given probability of detection over a specified active lifetime?

Scalable Sensor Network


Desired Simulator Characteristics

Scalability: ability to simulate very large systems multi-granularity models parallel model execution

Migration of simulator to operational prototype automated code generation capability common language for simulation/operational

software Integration of operational components

multi-paradigm models Domain-specific model libraries for large scale

simulations Visual, hierarchical model design


PARSEC Simulation Environment

Scalable model design & execution Visual model design Support for hierarchical, multiple granularity models Parallel execution using diverse algorithms & architectures

Commodity Platforms Implemented on heterogeneous platforms and OS from

commodity PCs to state of the art supercomputers Interoperability

Provides HLA-compatibility for interoperability with stimulators (e.g.., MODSAF) and simulators

Next generation of the Maisie simulation language


PARSEC Simulation Environment

World Wide Web enabled users need not download PARSEC — models can be submitted via

a Java web interface, executed remotely, and results displayed at local client

Established user base widespread use at 300+ academic (e.g., Illinois, Columbia, Caltech,

Technion Israel, University College London, …) and industrial sites (e.g., IBM, BBN, Sun, Rockwell, Northrop, ... )

use referenced in publications at ICC, Mobicom, Usenix, etc.

User Support mailing list, web bulletin board, …


Simulation Environment

Virtual Time Synchronization (Simulation) AlgorithmsVirtual Time Synchronization (Simulation) Algorithms

ConservativeConservative OptimisticOptimistic ISPISP Global Event List

Global Event List

HybridHybrid

PAVE (Visual) Front-End

PAVE (Visual) Front-End

C++ Library(COMPOSE)

C++ Library(COMPOSE)

PARSEC (C-Based) Front-End

PARSEC (C-Based) Front-End

Java-Based WWW Interface

Java-Based WWW Interface

HLA-based InteroperabilityHLA-based Interoperability

Portable Multi-threaded Communication Library (MPC)

Portable Multi-threaded Communication Library (MPC)

Uniprocessor Machine

Sun Sparc 1000/ SGI Origin 2000IBM SP

MPI/AIXMPI/AIX MPI CH/ BSD Unix

MPI CH/ BSD Unix

Pthreads on Windows NT, Linux, Solaris, IRIX

Pthreads on Windows NT, Linux, Solaris, IRIX

Linux, Windows NT,

Unix

Linux, Windows NT,

Unix

PC Network


DES Execution simulation-clock:=0; event-list:={initial set of events}; while (termination-criteria-is-false) do

{ remove event with earliest timestamp from event-list: e1::(t1,p1,...);

deliver event to LP p1;

simulation-clock:=t1;

execute code to simulate event e1;

insert new events (if any) in event-list;

}

(5,p) (8,p1)

(5,p)e1: clock (e1)=0

clock (e1)=5

(8,p1) (9,p2) (12,p3)


Parallel Simulation Execution of a discrete-event simulation on

parallel computers by distributing the global event list among processors

• SolutionsSolutions– Blocking synchronization (conservative algorithm)Blocking synchronization (conservative algorithm)– Potentially process events out of order (optimistic)Potentially process events out of order (optimistic)– Adaptive algorithmsAdaptive algorithms

15

12Network

current event

event queue

P2

P313 9 9 3

P2 P2 P3 P3

P115

12

9


Rapid Prototyping

DESIGN

IMPLEMENTATIONSIMULATION


Rapid Prototyping

DESIGN

IMPLEMENTATIONSIMULATION


Rapid Prototyping Asynchronous message passing kernel

thread create/terminate; message send & recv MPC: messages processed in physical arrival

order

P1

P2

P0

4

M1,7

M1’



thread create/terminate; message send & recv PARSEC: messages processed in global order of

message (event) timestamps

P1

P2

P0

4

M1,7



thread create/terminate; message send & recv PARSEC: messages processed in global

order of message (event) timestamps

P1

P2

P0M1,7

4M’


Autoporting Network Ctrl Algs

Maisie Network Level

Maisie Node Level

NOS (Maisie)

Commun. Hardware

Simulation

Channel Model

Net. Ctrl. Algs.

(Joel Short)

Laptop

NOS (C Code)

Commun. Hardware

Implementation

PortingNet. Ctrl. Algs.Net. Ctrl. Algs.


PPA

PPC

PPB

PPF

PPE

PPD

Physical System

LPA

LPC

LPB

LPF

LPDE

Logical Model

PPA

PPC

PPB

LPF

LPDE

Hybrid Model

Hybrid Simulation


Hybrid Simulation

More useful than pure Simulation No need to model complex operational

components Speed Accuracy

PPA

PPC

PPB

LPF

LPDE An operational process

A simulated process


Synchronization (S to O)

Real Timet1 t2 t3

e1 e2 e3

e1(t1) e2(t2) e3(t3)

In Hybrid Simulation, event e(t) may not arrive exactly at real time t

• Simulator may be faster/slower than operational comp.• Non-deterministic comm. latency between S and O

In a completely operational system e1, e2,e3 would arrive at t1, t2, t3, respectively


Design (cont’d)

toS

StoO

Simulated (S) Operational (O)


Wargame simulations Wireless and wired network protocols Replicated file systems Web caching protocol models Database models ATM, electronic LAN models, fiber optic network, ... Queueing networks Transistor level VLSI designs MEMS sensor networks Parallel program (MPI) simulations Parallel IO system simulation

Existing Applications

Documents

R. Bagrodia, Parsec Workshop ‘98 Rajive Bagrodia Computer Science Department UCLA Partial support from the TRAVLER and DOMAINS contracts from DARPA ITO