Apr 21, 2023

Threads and Multithreading

2

Threads

A Thread is a single flow of control When you step through a program, you are following a Thread

A Thread is an Object you can create and control Java programs use multiple Threads

The “main Thread” is the one that starts at your program’s main method

Other Threads will start at a run() method There are other Threads running, for example, the one that

does garbage collection

3

States of a Thread

A Thread can be in one of four states: Ready: all set to run Running: actually doing something Waiting, or blocked: needs something Dead: will never do anything again

State names vary across textbooks You have some control, but the Java scheduler has more

ready

waiting

running deadstart

4

Two ways of creating Threads

You can extend the Thread class: class LongComputation extends Thread {…}

Limiting, since you can only extend one class Your class must contain a public void run()

method Or you can implement the Runnable interface:

class LongComputation implements Runnable {…}

Your class must contain a public void run() method

This approach is more generally useful

5

Extending Thread

class LongComputation extends Thread { public void run() { code for this thread } Anything else you want in this class}

LongComputation lc= new LongComputation();

A newly created Thread is in the Ready state To start the lc Thread running, call lc.start(); start() is a request to the scheduler to run the Thread—

it usually does not happen right away The Thread should eventually enter the Running state

6

Implementing Runnable

class LongComputation implements Runnable {…}

The Runnable interface requires run() This is the “main” method of your new Thread

LongComputation lc = new LongComputation ();

Thread myThread = new Thread(lc); To start the Thread running, call myThread.start();

You do not write the start() method—it’s provided by Java

As always, start() is a request to the scheduler to run the Thread--it may not happen right away

7

Starting and ending a Thread

Every Thread has a start() method Do not write or override start() You call start() to request a Thread to run The scheduler then (eventually) calls run()

Nothing will prevent you from calling run() directly If you do, it will run as an ordinary method, not in a new Thread

You must supply public void run() This is where you put the code that the Thread is going to run

When the run() method finishes (or returns), the Thread “dies”

A dead Thread is truly dead—it still exists as an object, but it cannot be used again

8

Extending Thread: summary

class LongComputation extends Thread { public void run() { while (okToRun) {...} }}

LongComputation lc = new LongComputation ();lc.start();

9

Implementing Runnable: summary

class LongComputation extends Applet implements Runnable { public void run() { while (okToRun) { ... } }}

LongComputation lc = new LongComputation ( );Thread myThread = new Thread(lc);myThread.start( );

Daemon Threads There are two kinds of Threads—user Threads and daemon

Threads A daemon Thread is a “helper Thread” A Java program continues to run until all its user Threads have

died This means it’s very easy to have forgotten Threads running in the

background, invisibly tying up machine resources A Java program will exit if the only Threads left are daemon

Threads

To make a Thread into a daemon Thread, call thread.setDaemon(true), before starting the Thread

You can ask a Thread if it is a daemon Thread with the method thread.isDaemon()

10

11

Sleeping

Thread.sleep(int milliseconds); A millisecond is 1/1000 of a second9

try { Thread.sleep(1000); }catch (InterruptedException e) {}

sleep only works for the current Thread

Under normal circumstances, nothing in the Java system will interrupt your sleeping Thread…but

Your program can call someThread.interrupt()

12

Things a Thread can do

Thread.sleep(milliseconds) yield() Thread me = currentThread(); int myPriority = me.getPriority(); me.setPriority(NORM_PRIORITY);

As long as a higher-priority Thread exists, it will always run instead of a lower-priority Thread

Don’t mess with priorities unless you are really sure of what you are doing

if (otherThread.isAlive()) {…} join(otherThread);

13

Things a Thread should NOT do The Thread controls its own destiny Original Java provided these methods, which were almost immediately:

myThread.stop() myThread.suspend() myThread.resume()

These methods literally cannot be used in a safe way! Never use these methods! If you want a Thread to do something, you should ask it

(nicely) For example, see the use of an okToRun variable in some of the

previous slides

14

Controlling another Thread boolean okToRun = true;lc.start();Do stuffokToRun = false;

public void run() { while (okToRun) {…}

This is just meant to be suggestive—typically, the okToRun variable would be in a different class than the run() method

15

Thread pools

A thread pool is a collection of reusable threads This can save a lot of the overhead of creating and disposing

of threads Very basic introduction (Java 5+):

import java.util.concurrent.*;...ExecutorService exec = Executors.newFixedThreadPool(20);

Create some Runnable objects (objects that implement public void run() )

exec.execute(Some Runnable object)

16

A problem

What gets printed as the value of k? This is a trivial example of what is, in general, a

very difficult problem

int k = 0;

Thread #1: k = k + 1;

Thread #2: System.out.print(k);

Concurrency vs. parallelism

Briefly, “parallel” is a hardware term, while “concurrent” is a software term

Parallel: Different computations are actually happening at the same time

Concurrent: Different computations might be happening at the same time (if you have the hardware for it) Or one might happen before another Or their execution may be interleaved in any arbitrary order

Concurrent programming can be done on: a single computer with a single processor a single computer with multiple processors multiple computers networked together

17

Shared state

Processes run in completely different parts of memory, and cannot modify one another’s data

Threads run in the same part of memory and share access to data

If the data is immutable, reading it from different Threads does not cause any problems

If the data is mutable, then it describes the state of the computation (it changes as the computation proceeds), and we have shared state

Shared state makes concurrent programming difficult Concurrent programs are nondeterministic—things could happen

in many different orders Every possible ordering, no matter how unlikely, must produce

correct results

18

19

Problems

Concurrency can lead to data corruption: Race conditions—if two or more processes try to write to the same data

space, or one tries to write and one tries to read, it is indeterminate which happens first

Concurrency can lead to “freezing up” and other flow problems: Deadlock—two or more processes are each waiting for data from the

other, or are waiting for the other to finish

Livelock—two or more processes each repeatedly change state in an attempt to avoid deadlock, but in so doing continue to block one another

Starvation—a process never gets an opportunity to run, possibly because other processes have higher priority

19

20

Why bother with concurrency? We use concurrency to make programs “faster”

“Faster” may mean more responsive We need threads, even on single core machines, to move slow operations out

of the GUI “Faster” may mean the computation completes sooner

We can: Break a computation into separate parts Distribute these partial computations to several cores Collect the partial results into a single result

Thread creation, communication between threads, and thread disposal constitutes overhead, which is not present in the sequential version

Due to overhead costs, it is not unusual for first attempts at using concurrency to result in a slower program

Really getting much speedup requires lots of experimentation, timing tests, and tuning the code

Good performance is not platform independent

Atomicity

An operation is atomic if it appears to happen “all at once” Suppose one Thread is copying an array, while a concurrent

Thread is sorting it The first Thread might get the sorted or the unsorted array Much worse, the first Thread might get a mixture of the two, with

some elements duplicated and others lost We don’t want the operations to overlap; we want one to

finish before the other one starts In other words, we want these operations to be “atomic” We can do this by “locking” the array, so that only one

process at a time can access it, and others have to wait

21

Synchronization

Synchronization provides a “locking” mechanism Here’s how it works:

Pick an object, any object Method A synchronizes on that object Method B synchronizes on the same object Whichever method, A or B, happens to synchronize first, the

other method has to wait until it is done In the case of sorting vs. copying an array, one obvious

object to synchronize on is the array itself Or, you might want to synchronize on whatever object

contains the array But you could literally synchronize on any object, as long as

the two methods synchronize on the same object22

23

Ways to synchronize

You can explicitly say which object to synchronize on: synchronized (obj) { code } Notice that synchronized is being used as a statement

You can synchronize an instance method: synchronized void sort(array) { code } The object used for synchronization is the object (instance)

executing this method Thus, other synchronized instance methods cannot run at the same

time, for this instance You can synchronize a static method:

synchronized static void sort(array) { code } The object used for synchronization is the class object, which is an

object representing this entire class

Synchronization is re-entrant

Suppose a Thread obtains a lock on some object:synchronized (thing) { code }

Then the code calls another method that tries to synchronize on the same object:synchronized (thing) { some other code }

This works! If a Thread has a lock on some object, it is okay for that same Thread to ask for the lock again

Synchronization prevents some other Thread from getting the same lock

24

25

Data invariants

Any publicly available method that modifies an object should take it from one valid state to another valid state A data invariant is a logical condition (possibly quite complex)

that describes what it means for an object to be valid Any method that “partially” updates an object must be private

This is a fundamental rule of all object-oriented programming

Any method that modifies a shared object must be atomic Example:

Suppose you have a Fraction object with value 10/15 You want to reduce this Fraction to lowest terms: 2/3 It is unsafe to modify the numerator atomically and the denominator

atomically; they must both be changed in a single atomic operation

When to synchronize

A method that mutates the state of an object should move it from one valid state to another

In the process, the data may become temporarily invalid Invalid data should never be visible to another Thread

Suppose mutable data items A, B, and C are in some sort of relationship to one another

Trivial example: A + B = C Often, these data items would be fields in a single object

Fundamental rule: If mutable data can be accessed by more than one thread, then every access to it, everywhere, must be synchronized. No exceptions!

26

27

If you don’t always synchronize

28

Atomic actions An operation, or block of code, is atomic if it happens “all at once,” that is, no other

Thread can access the same data while the operation is being performed x++; looks atomic, but at the machine level, it’s actually three separate operations:

1. load x into a register

2. add 1 to the register

3. store the register back in x Suppose you are maintaining a stack as an array:

void push(Object item) { this.top = this.top + 1; this.array[this.top] = item; }

You need to synchronize this method, and every other access to the stack, to make the push operation atomic

Atomic actions that maintain data invariants are thread-safe; compound (non-atomic) actions are not

This is another good reason for encapsulating your objects

29

Check-then-act A Vector is like an ArrayList, but is synchronized Hence, the following code looks reasonable:

if (!myVector.contains(someObject)) { // check myVector.add(someObject); // act}

But there is a “gap” between checking the Vector and adding to it During this gap, some other Thread may have added the object to the array Check-then-act code, as in this example, is unsafe

You must ensure that no other Thread executes during the gap synchronized(myVector) {

if (!myVector.contains(someObject)) { myVector.add(someObject); }}

So, what good is it that Vector is synchronized? It means that each call to a Vector operation is atomic

29

30

Synchronization is on an object Synchronization can be done on any object Synchronization is on objects, not on variables Suppose you have

synchronized(myVector) { … } Then it is okay to modify myVector—that is, change the values of its fields It is not okay to say myVector = new Vector();

Synchronization is expensive Synchronization entails a certain amount of overhead Synchronization limits parallelism (obviously, since it keeps other Threads from

executing) Synchronization can lead to deadlock

Moral: Don’t synchronize everything!

30

31

Local variables

A variable that is strictly local to a method is thread-safe This is because every entry to a method gets a new copy of that variable

If a variable is a primitive type, it is thread-safe Except for long and double!

If a variable holds an immutable object (such as a String) it is thread-safe, because all immutable objects are thread-safe

If a variable holds a mutable object, and there is no way to access that variable from outside the method, then it can be made thread-safe

An Object passed in as a parameter is not thread-safe (unless immutable) An Object returned as a value is not thread-safe (unless immutable) An Object that has references to data outside the method is not thread-safe

32

Thread deaths

A Thread “dies” (finishes) when its run method finishes There are two kinds of Threads: daemon Threads and non-daemon

Threads When all non-daemon Threads die, the daemon Threads are automatically

terminated If the main Thread quits, the program will appear to quit, but other non-

daemon Threads may continue to run These Threads will persist until you reboot your computer

A Thread is by default the same type (daemon or non-daemon as the Thread that creates it

There is a method: void setDaemon(boolean on)

The join(someOtherThread) allows “this” Thread to wait for some other thread to finish

32

33

Communication between Threads

Threads can communicate via shared, mutable data Since the data is mutable, all accesses to it must be synchronized Example:

synchronized(someObj) { flag = !flag; } synchronized(someObj) { if (flag) doSomething(); }

The first version of Java provided methods to allow one thread to control another thread: suspend, resume, stop, destroy

These methods were not safe and were deprecated almost immediately—never use them!

They are still there because Java never throws anything away If you want one Thread to control another Thread, do so via shared data

33

34

Use existing tools There’s no point in trying to make something thread-safe if a

carefully crafted thread-safe version exists in the Java libraries java.util.concurrent has (among other goodies):

ConcurrentHashMap ConcurrentLinkedQueue ThreadPoolExecutor FutureTask

And java.util.concurrent.atomic has thread-safe methods on single variables, such as these in AtomicInteger:

int addAndGet(int delta) int getAndAdd(int delta) boolean compareAndSet(int expect, int update) void lazySet(int newValue)

35

Advice Any data that can be made immutable, should be made immutable

This applies especially to input data--make sure it’s completely read in before you work with it, then don’t allow changes

All mutable data should be carefully encapsulated (confined to the class in which it occurs)

All access to mutable data (writing and reading it) must be synchronized

All operations that modify the state of data, such that validity conditions may be temporarily violated during the operation, must be made atomic (so that the data is valid both before and after the operation)

Be careful not to leave Threads running after the program finishes

36

Debugging

“Debugging can show the presence of errors, but never their absence.” -- Edgser Dijkstra

Concurrent programs are nondeterministic: Given exactly the same data and the same starting conditions, they may or may not do the same thing

It is virtually impossible to completely test concurrent programs; therefore: Test the non-concurrent parts as thoroughly as you can Be extremely careful with concurrency; you have to depend

much more on programming discipline, much less on testing Document your concurrency policy carefully, in order to

make the program more maintainable in the future

37

The End