1 OMSE 510: Computing Foundations 7: More IPC & Multithreading Chris Gilmore Portland State...

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OMSE 510: Computing Foundations7: More IPC & Multithreading

Chris Gilmore <[email protected]>

Portland State University/OMSE

Material Borrowed from Jon Walpole’s lectures

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Today

Classical IPC Problems

Monitors

Message Passing

Scheduling

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Classical IPC problems

Producer Consumer (bounded buffer)

Dining philosophers

Sleeping barber

Readers and writers

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Producer consumer problem

8 Buffers

InP

OutP

Consumer

Producer

Producer and consumerare separate threads

Also known as the bounded buffer problem

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Is this a valid solution?thread producer {

while(1){ // Produce char c while (count==n) { no_op } buf[InP] = c InP = InP + 1 mod n count++}

}

thread consumer {while(1){ while (count==0) { no_op } c = buf[OutP] OutP = OutP + 1 mod n count-- // Consume char}

}

0

1

2

n-1

…

Global variables: char buf[n] int InP = 0 // place to add int OutP = 0 // place to get int count

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0 thread consumer {1 while(1) {2 while (count==0) {3 sleep(empty)4 }5 c = buf[OutP]6 OutP = OutP + 1 mod n7 count--;8 if (count == n-1)9 wakeup(full)10 // Consume char11 }12 }

How about this?0 thread producer {1 while(1) {2 // Produce char c3 if (count==n) {4 sleep(full)5 }6 buf[InP] = c;7 InP = InP + 1 mod n8 count++9 if (count == 1)10 wakeup(empty)11 }12 }

0

1

2

n-1

…

Global variables: char buf[n] int InP = 0 // place to add int OutP = 0 // place to get int count

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Does this solution work?

0 thread producer {1 while(1){2 // Produce char c...3 down(empty_buffs)4 buf[InP] = c5 InP = InP + 1 mod n6 up(full_buffs)7 }8 }

0 thread consumer {1 while(1){2 down(full_buffs)3 c = buf[OutP]4 OutP = OutP + 1 mod n5 up(empty_buffs)6 // Consume char...7 }8 }

Global variables semaphore full_buffs = 0; semaphore empty_buffs = n; char buff[n]; int InP, OutP;

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Producer consumer problem

8 Buffers

InP

OutP

Consumer

Producer

Producer and consumerare separate threads

What is the shared state in the last solution?

Does it apply mutual exclusion? If so, how?

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Definition of Deadlock

A set of processes is deadlocked if each

process in the set is waiting for an event that only

another process in the set can cause

Usually the event is the release of a currently held resource

None of the processes can … be awakened run release resources

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Deadlock conditionsA deadlock situation can occur if and only if the following conditions hold simultaneously

Mutual exclusion condition – resource assigned to one process

Hold and wait condition – processes can get more than one resource

No preemption condition

Circular wait condition – chain of two or more processes (must be waiting for resource from next one in chain)

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Resource acquisition scenarios

acquire (resource_1)use resource_1release (resource_1)

Thread A:

Example: var r1_mutex: Mutex ... r1_mutex.Lock() Use resource_1 r1_mutex.Unlock()

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Resource acquisition scenariosThread A:


Another Example: var r1_sem: Semaphore ... r1_sem.Down() Use resource_1 r1_sem.Up()

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Thread A: Thread B:


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Thread A: Thread B:

No deadlock can occur here!


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Resource acquisition scenarios: 2 resources

acquire (resource_1)acquire (resource_2)use resources 1 & 2release (resource_2)release (resource_1)


Thread A: Thread B:

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Thread A: Thread B:


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acquire (resource_1)use resources 1release (resource_1)acquire (resource_2)use resources 2release (resource_2)


Thread A: Thread B:

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Thread A: Thread B:


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Thread A: Thread B:

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Thread A: Thread B:

Deadlock is possible!

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Examples of deadlock Deadlock occurs in a single programProgrammer creates a situation that deadlocksKill the program and move onNot a big deal

Deadlock occurs in the Operating SystemSpin locks and locking mechanisms are mismanaged within the OSThreads become frozenSystem hangs or crashesMust restart the system and kill and applications

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Five philosophers sit at a tableOne fork between each philosopher

Why do they need to synchronize?How should they do it?

Dining philosophers problem

while(TRUE) { Think(); Grab first fork; Grab second fork; Eat(); Put down first fork; Put down second fork;}

Each philosopher ismodeled with a thread

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Is this a valid solution?

#define N 5

Philosopher() { while(TRUE) { Think(); take_fork(i); take_fork((i+1)% N); Eat(); put_fork(i); put_fork((i+1)% N); }}

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Working towards a solution …

#define N 5

Philosopher() { while(TRUE) { Think(); take_fork(i); take_fork((i+1)% N); Eat(); put_fork(i); put_fork((i+1)% N); }}

take_forks(i)

put_forks(i)

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Working towards a solution …

#define N 5

Philosopher() { while(TRUE) { Think(); take_forks(i); Eat(); put_forks(i); }}

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Picking up forks// only called with mutex set!

test(int i) { if (state[i] == HUNGRY && state[LEFT] != EATING && state[RIGHT] != EATING){ state[i] = EATING; up(sem[i]); }}

int state[N]semaphore mutex = 1semaphore sem[i]

take_forks(int i) { down(mutex); state [i] = HUNGRY; test(i); up(mutex); down(sem[i]);}

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Putting down forks// only called with mutex set!

test(int i) { if (state[i] == HUNGRY && state[LEFT] != EATING && state[RIGHT] != EATING){ state[i] = EATING; up(sem[i]); }}

int state[N]semaphore mutex = 1semaphore sem[i]

put_forks(int i) { down(mutex); state [i] = THINKING; test(LEFT); test(RIGHT); up(mutex);}

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Dining philosophers

Is the previous solution correct?

What does it mean for it to be correct?

Is there an easier way?

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The sleeping barber problem

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The sleeping barber problemBarber:While there are people waiting for a hair cut, put one in

the barber chair, and cut their hairWhen done, move to the next customerElse go to sleep, until someone comes in

Customer: If barber is asleep wake him up for a haircutIf someone is getting a haircut wait for the barber to

become free by sitting in a chairIf all chairs are all full, leave the barbershop

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Designing a solutionHow will we model the barber and customers?

What state variables do we need? .. and which ones are shared?…. and how will we protect them?

How will the barber sleep?

How will the barber wake up?

How will customers wait?

What problems do we need to look out for?

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Is this a good solution?

Barber Thread: while true Down(customers) Lock(lock) numWaiting = numWaiting-1 Up(barbers) Unlock(lock) CutHair() endWhile

Customer Thread: Lock(lock) if numWaiting < CHAIRS numWaiting = numWaiting+1 Up(customers) Unlock(lock) Down(barbers) GetHaircut() else -- give up & go home Unlock(lock) endIf

const CHAIRS = 5var customers: Semaphore barbers: Semaphore lock: Mutex numWaiting: int = 0

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The readers and writers problem

Multiple readers and writers want to access a database (each one is a thread)

Multiple readers can proceed concurrently

Writers must synchronize with readers and other writers only one writer at a time ! when someone is writing, there must be no readers !

Goals:Maximize concurrency.Prevent starvation.

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Designing a solutionHow will we model the readers and writers?

What state variables do we need? .. and which ones are shared?…. and how will we protect them?

How will the writers wait?

How will the writers wake up?

How will readers wait?

How will the readers wake up?

What problems do we need to look out for?

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Is this a valid solution to readers & writers?

Reader Thread: while true Lock(mut) rc = rc + 1 if rc == 1 Down(db) endIf Unlock(mut) ... Read shared data... Lock(mut) rc = rc - 1 if rc == 0 Up(db) endIf Unlock(mut) ... Remainder Section... endWhile

var mut: Mutex = unlocked db: Semaphore = 1 rc: int = 0

Writer Thread: while true ...Remainder Section... Down(db) ...Write shared data... Up(db) endWhile

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Readers and writers solution

Does the previous solution have any problems? is it “fair”?can any threads be starved? If so, how

could this be fixed?

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MonitorsIt is difficult to produce correct programs using semaphorescorrect ordering of up and down is tricky!avoiding race conditions and deadlock is tricky!boundary conditions are tricky!

Can we get the compiler to generate the correct semaphore code for us?what are suitable higher level abstractions for

synchronization?

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MonitorsRelated shared objects are collected together

Compiler enforces encapsulation/mutual exclusionEncapsulation:

Local data variables are accessible only via the monitor’s entry procedures (like methods)

Mutual exclusionA monitor has an associated mutex lockThreads must acquire the monitor’s mutex lock before

invoking one of its procedures

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Monitors and condition variables

But we need two flavors of synchronizationMutual exclusion

Only one at a time in the critical sectionHandled by the monitor’s mutex

Condition synchronizationWait until a certain condition holdsSignal waiting threads when the condition holds

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Monitors and condition variables

Condition variables (cv) for use within monitorswait(cv)

thread blocked (queued) until condition holdsmonitor mutex released!!

signal(cv)signals the condition and unblocks (dequeues)

a thread

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Monitor structures

initializationcode

“entry” methods

y

x

shared data

condition variablesmonitor entry queue

List of threadswaiting to

enter the monitor

Can be called fromoutside the monitor.Only one active at

any moment.

Local to monitor(Each has an associatedlist of waiting threads)

local methods

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Monitor example for mutual exclusion

process Producerbegin loop <produce char “c”> BoundedBuffer.deposit(c) end loopend Producer

process Consumerbegin loop BoundedBuffer.remove(c) <consume char “c”> end loopend Consumer

monitor: BoundedBuffervar buffer : ...; nextIn, nextOut :... ;

entry deposit(c: char) begin ... end

entry remove(var c: char) begin ... end

end BoundedBuffer

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Observations

That’s much simpler than the semaphore-based solution to producer/consumer (bounded buffer)!

… but where is the mutex?

… and what do the bodies of the monitor procedures look like?

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Monitor example with condition variables

monitor : BoundedBuffervar buffer : array[0..n-1] of char nextIn,nextOut : 0..n-1 := 0 fullCount : 0..n := 0 notEmpty, notFull : condition

entry deposit(c:char) entry remove(var c: char) begin begin if (fullCount = n) then if (fullCount = n) then wait(notFull) wait(notEmpty) end if end if

buffer[nextIn] := c c := buffer[nextOut] nextIn := nextIn+1 mod n nextOut := nextOut+1 mod n fullCount := fullCount+1 fullCount := fullCount-1 signal(notEmpty) signal(notFull) end deposit end remove

end BoundedBuffer

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Condition variables

“Condition variables allow processes to synchronize based on some state of the monitor variables.”

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Condition variables in producer/consumer“NotFull” condition

“NotEmpty” condition

Operations Wait() and Signal() allow synchronization within the monitor

When a producer thread adds an element...A consumer may be sleepingNeed to wake the consumer... Signal

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Condition synchronization semantics

“Only one thread can be executing in the monitor at any one time.”

Scenario:Thread A is executing in the monitorThread A does a signal waking up thread BWhat happens now?Signaling and signaled threads can not both run!… so which one runs, which one blocks, and on what

queue?

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Monitor design choicesCondition variables introduce a problem for mutual exclusion only one process active in the monitor at a time, so what to do

when a process is unblocked on signal? must not block holding the mutex, so what to do when a

process blocks on wait?

Should signals be stored/remembered? signals are not stored if signal occurs before wait, signal is lost!

Should condition variables count?

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Monitor design choicesChoices when A signals a condition that unblocks B A waits for B to exit the monitor or blocks again B waits for A to exit the monitor or block Signal causes A to immediately exit the monitor or block (… but

awaiting what condition?)

Choices when A signals a condition that unblocks B & C B is unblocked, but C remains blocked C is unblocked, but B remains blocked Both B & C are unblocked … and compete for the mutex?

Choices when A calls wait and blocks a new external process is allowed to enter but which one?

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Option 1: Hoare semanticsWhat happens when a Signal is performed?signaling thread (A) is suspendedsignaled thread (B) wakes up and runs immediately

Result:B can assume the condition is now true/satisfiedHoare semantics give strong guaranteesEasier to prove correctness

When B leaves monitor, A can run.A might resume execution immediately ... or maybe another thread (C) will slip in!

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Option 2: MESA Semantics (Xerox PARC)

What happens when a Signal is performed? the signaling thread (A) continues. the signaled thread (B) waits.when A leaves monitor, then B runs.

Issue: What happens while B is waiting?can another thread (C) run after A signals, but before B

runs?

In MESA semantics a signal is more like a hintRequires B to recheck the state of the monitor variables

(the invariant) to see if it can proceed or must wait some more

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Code for the “deposit” entry routine

monitor BoundedBuffer var buffer: array[n] of char nextIn, nextOut: int = 0 cntFull: int = 0 notEmpty: Condition notFull: Condition

entry deposit(c: char) if cntFull == N notFull.Wait() endIf buffer[nextIn] = c nextIn = (nextIn+1) mod N cntFull = cntFull + 1 notEmpty.Signal() endEntry

entry remove() ...

endMonitor

Hoare Semantics

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Code for the “deposit” entry routine


entry deposit(c: char) while cntFull == N notFull.Wait() endWhile buffer[nextIn] = c nextIn = (nextIn+1) mod N cntFull = cntFull + 1 notEmpty.Signal() endEntry

entry remove() ...

endMonitor

MESA Semantics

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Code for the “remove” entry routine


entry deposit(c: char) ...

entry remove() if cntFull == 0 notEmpty.Wait() endIf c = buffer[nextOut] nextOut = (nextOut+1) mod N cntFull = cntFull - 1 notFull.Signal() endEntry

endMonitor

Hoare Semantics

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Code for the “remove” entry routine


entry deposit(c: char) ...

entry remove() while cntFull == 0 notEmpty.Wait() endWhile c = buffer[nextOut] nextOut = (nextOut+1) mod N cntFull = cntFull - 1 notFull.Signal() endEntry

endMonitor

MESA Semantics

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“Hoare Semantics”What happens when a Signal is performed?

The signaling thread (A) is suspended.The signaled thread (B) wakes up and runs immediately.

B can assume the condition is now true/satisfied

From the original Hoare Paper:

“No other thread can intervene [and enter the monitor] between the signal and the continuation of exactly one waiting thread.”

“If more than one thread is waiting on a condition, we postulate that the signal operation will reactivate the longest waiting thread. This gives a simple neutral queuing discipline which ensures that every waiting thread will eventually get its turn.”

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Implementing Hoare SemanticsThread A holds the monitor lock

Thread A signals a condition that thread B was waiting on

Thread B is moved back to the ready queue?B should run immediatelyThread A must be suspended... the monitor lock must be passed from A to B

When B finishes it releases the monitor lock

Thread A must re-aquire the lockPerhaps A is blocked, waiting to re-aquire the lock

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Implementing Hoare Semantics

Problem:Possession of the monitor lock must be

passed directly from A to B and then back to A

Simply ending monitor entry methods with

monLock.Unlock()

… will not workA’s request for the monitor lock must be

expedited somehow

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Implementing Hoare Semantics

Implementation Ideas:Consider a thread like A that hands off the

mutex lock to a signaled thread, to be “urgent”.Thread C is not “urgent”

Consider two wait lists associated with each MonitorLock

UrgentlyWaitingThreadsNonurgentlyWaitingThreads

Want to wake up urgent threads first, if any

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Brinch-Hansen SemanticsHoare SemanticsOn signal, allow signaled process to runUpon its exit from the monitor, signaler process continues.

Brinch-Hansen SemanticsSignaler must immediately exit following any invocation of

signalRestricts the kind of solutions that can be written… but monitor implementation is easier

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Reentrant codeA function/method is said to be reentrant if...

A function that has been invoked may be invoked again before the first invocation has returned, and will still work correctly

Recursive routines are reentrant

In the context of concurrent programming...

A reentrant function can be executed simultaneously by more than one thread, with no ill effects

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Reentrant CodeConsider this function...

integer seed;

integer rand() {

seed = seed * 8151 + 3423

return seed;

}

What if it is executed by different threads concurrently?

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Reentrant CodeConsider this function...

integer seed;

integer rand() {

seed = seed * 8151 + 3423

return seed;

}

What if it is executed by different threads concurrently?The results may be “random”This routine is not reentrant!

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When is code reentrant?Some variables are “local” -- to the function/method/routine “global” -- sometimes called “static”

Access to local variables?A new stack frame is created for each invocationEach thread has its own stack

What about access to global variables?Must use synchronization!

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Making this function threadsafeinteger seed;

semaphore m = 1;

integer rand() {

down(m);

seed = seed * 8151 + 3423

tmp = seed;

up(m);

return tmp;

}

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Making this function reentrant

integer seed;

integer rand( *seed ) {

*seed = *seed * 8151 + 3423

return *seed;

}

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Message PassingInterprocess Communicationvia shared memoryacross machine boundaries

Message passing can be used for synchronization or general communication

Processes use send and receive primitives receive can block (like waiting on a Semaphore)send unblocks a process blocked on receive (just as a signal

unblocks a waiting process)

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Producer-consumer with message passing

The basic idea:After producing, the producer sends the data to consumer in

a messageThe system buffers messages

The producer can out-run the consumerThe messages will be kept in order

But how does the producer avoid overflowing the buffer?After consuming the data, the consumer sends back an “empty”

messageA fixed number of messages (N=100)The messages circulate back and forth.

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thread producer var c, em: char while true // Produce char c... Receive(consumer, &em) -- Wait for an empty msg Send(consumer, &c) -- Send c to consumer endWhileend

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thread consumer var c, em: char while true Receive(producer, &c) -- Wait for a char Send(producer, &em) -- Send empty message back // Consume char... endWhileend

const N = 100 -- Size of message buffervar em: charfor i = 1 to N -- Get things started by Send (producer, &em) -- sending N empty messagesendFor

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Design choices for message passing

Option 1: MailboxesSystem maintains a buffer of sent, but not

yet received, messagesMust specify the size of the mailbox ahead

of timeSender will be blocked if the buffer is fullReceiver will be blocked if the buffer is

empty

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Design choices for message passing

Option 2: No buffering If Send happens first, the sending thread blocks If Receiver happens first, the receiving thread

blocksSender and receiver must Rendezvous (ie. meet)Both threads are ready for the transferThe data is copied / transmittedBoth threads are then allowed to proceed

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Barriers

(a) Processes approaching a barrier (b) All processes but one blocked at barrier (c) Last process arrives; all are let through

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QuizWhat is the difference between a monitor and a semaphore?Why might you prefer one over the other?

How do the wait/signal methods of a condition variable differ from the up/down methods of a semaphore?

What is the difference between Hoare and Mesa semantics for condition variables?What implications does this difference have for code

surrounding a wait() call?

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Scheduling

New ProcessReady

Blocked

RunningTermination

Process state model

Responsibility of the Operating System

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CPU scheduling criteriaCPU Utilization – how busy is the CPU?

Throughput – how many jobs finished/unit time?

Turnaround Time – how long from job submission to job termination?

Response Time – how long (on average) does it take to get a “response” from a “stimulus”?

Missed deadlines – were any deadlines missed?

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Scheduler optionsPrioritiesMay use priorities to determine who runs next

amount of memory, order of arrival, etc..

Dynamic vs. Static algorithmsDynamically alter the priority of the tasks while they are in the system

(possibly with feedback)Static algorithms typically assign a fixed priority when the job is initially

started.

Preemptive vs. NonpreemptivePreemptive systems allow the task to be interrupted at any

time so that the O.S. can take over again.

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Scheduling policies

First-Come, First Served (FIFO)

Shortest Job First (non-preemeptive)

Shortest Job First (with preemption)

Round-Robin Scheduling

Priority Scheduling

Real-Time Scheduling

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First-Come, First-Served (FIFO)Start jobs in the order they arrive (FIFO queue)Run each job until completion

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First-Come, First-Served (FIFO)

Arrival Processing Process Time Time 1 0 3 2 2 6 3 4 4 4 6 5 5 8 2

Start jobs in the order they arrive (FIFO queue)Run each job until completion

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Arrival Processing Turnaround Process Time Time Delay Time 1 0 3 2 2 6 3 4 4 4 6 5 5 8 2

Total time taken,from submission to

completion


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Arrival Processing Turnaround Process Time Time Delay Time 1 0 3 0 2 2 6 1 3 4 4 5 4 6 5 7 5 8 2 10


completion


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Arrival Processing Turnaround Process Time Time Delay Time 1 0 3 0 3 2 2 6 1 7 3 4 4 5 9 4 6 5 7 12 5 8 2 10 12


completion


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Shortest Job FirstSelect the job with the shortest (expected) running timeNon-Preemptive

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Shortest Job First

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Same Job Mix

Select the job with the shortest (expected) running timeNon-Preemptive

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Seslect the job with the shortest (expected) running timeNon-Preemptive

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Shortest Remaining Time

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Same Job Mix

Preemptive version of SJF

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• Preemptive version of SJF

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Goal: Enable interactivity Limit the amount of CPU that a process

can have at one time.

Time quantum Amount of time the OS gives a process

before interventionThe “time slice”Typically: 1 to 100ms

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Round-Robin SchedulingEffectiveness of round-robin depends on The number of jobs, and The size of the time quantum.

Large # of jobs means that the time between scheduling of a single job increases Slow responses

Larger time quantum means that the time between the scheduling of a single job also increases Slow responses

Smaller time quantum means higher processing rates but also more overhead!

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Scheduling in general purpose systems

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Priority schedulingAssign a priority (number) to each processSchedule processes based on their priorityHigher priority jobs processes get more CPU time

Managing priorities Can use “nice” to reduce your priority Can periodically adjust a process’ priority

Prevents starvation of a lower priority process Can improve performance of I/O-bound processes by

basing priority on fraction of last quantum used

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Multi-Level Queue Scheduling

CPU

High priority

Low priority

Multiple queues, each with its own priority. Equivalently: each priority has its own ready

queue

Within each queue...Round-robin scheduling.

Simplist Approach: A Process’s priority is fixed & unchanging

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Multi-Level Feedback Queue Scheduling

Problem: Fixed priorities are too restrictive Processes exhibit varying ratios of CPU to I/O times.

Dynamic Priorities Priorities are altered over time, as process behavior

changes!

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Dynamic Priorities Priorities are altered over time, as process behavior

changes!

Issue: When do you change the priority of a process and how often?

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Dynamic Priorities Priorities are altered over time, as process behavior changes!

Issue: When do you change the priority of a process and how often?

Solution: Let the amount of CPU used be an indication of how a process is to be handled Expired time quantum more processing needed Unexpired time quantum less processing needed

Adjusting quantum and frequency vs. adjusting priority?

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CPU

High priority

Low priority

??

n priority levels, round-robin scheduling within a level

Quanta increase as priority decreases

Jobs are demoted to lower priorities if they do not complete within the current quantum

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Details, details, details... Starting priority?

High priority vs. low priority

Moving between priorities: How long should the time quantum be?

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Lottery SchedulingScheduler gives each thread some lottery tickets

To select the next process to run... The scheduler randomly selects a lottery number The winning process gets to run

Example Thread A gets 50 tickets

Thread B gets 15 tickets

Thread C gets 35 tickets

There are 100 tickets outstanding.

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Lottery Scheduling

Scheduler gives each thread some lottery tickets.

To select the next process to run... The scheduler randomly selects a lottery number The winning process gets to run

Example Thread A gets 50 tickets 50% of CPU

Thread B gets 15 tickets 15% of CPU

Thread C gets 35 tickets 35% of CPU


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Lottery SchedulingScheduler gives each thread some lottery tickets.

To select the next process to run...

The scheduler randomly selects a lottery number

The winning process gets to run

Example Thread A gets 50 tickets 50% of CPU

Thread B gets 15 tickets 15% of CPU

Thread C gets 35 tickets 35% of CPU


• Flexible

• Fair

• Responsive

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A Brief Look at Real-Time Systems

Assume processes are relatively periodic Fixed amount of work per period (e.g. sensor

systems or multimedia data)

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Assume processes are relatively periodic Fixed amount of work per period (e.g. sensor

systems or multimedia data)

Two “main” types of schedulers...

Rate-Monotonic Schedulers

Earliest-Deadline-First Schedulers

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Assume processes are relatively periodic Fixed amount of work per period (e.g. sensor systems or

multimedia data)

Two “main” types of schedulers...

Rate-Monotonic Schedulers Assign a fixed, unchanging priority to each process No dynamic adjustment of priorities

Less aggressive allocation of processor

Earliest-Deadline-First Schedulers Assign dynamic priorities based upon deadlines

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Typically real-time systems involve several steps (that aren’t in traditional systems)

Admission control All processes must ask for resources ahead of time. If sufficient resources exist,the job is “admitted” into the

system.

Resource allocation Upon admission... the appropriate resources need to be reserved for the

task.

Resource enforcement Carry out the resource allocations properly

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Rate Monotonic Schedulers

Process P1

T = 1 secondC = 1/2 second / period

For preemptable, periodic processes (tasks)

Assigns a fixed priority to each task T = The period of the task C = The amount of processing per task period

In RMS scheduling, the question to answer is... What priority should be assigned to a given task?

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P1

P2

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P1PRI > P2PRI

P2PRI > P1PRI

P1

P2

1 OMSE 510: Computing Foundations 7: More IPC & Multithreading Chris Gilmore Portland State...

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