[javaone 2011] models for concurrent programming

Models for Concurrent Programming

Tobias Ivarssonhacker @ Neo Technology

tobias@neotechnology.comtwitter: @thobeweb: http://thobe.org/ http://neo4j.org/

Common misconceptions

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More threads==

More throughput

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Finite number of cores

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Other limiting factorsf.ex. I/O is only one pipe

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Locking always impedes performance

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7

Concurrency on the track, only

one in the station

Amdahl’s law

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Speedup ≤ 1

F +1-F

N

N: Number of processorsF: Serial fraction

Throughput with synchronized

regions

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Throughput: 3

Throughput with synchronized

regions

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wait...

Throughput: 6

Throughput with synchronized

regions

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wait...wait...

Throughput: 9

Throughput with synchronized

regions

9

wait...wait...

wait...

Throughput: 11

10

wait...wait...

wait...wait...

wait...wait...

wait...wait...

wait...

wait...wait...

Throughput: 11

Throughput with synchronized

regions

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wait...wait...

wait...wait...

wait...wait...

wait...

Throughput: 11

Throughput with synchronized

regions

The fundamentals

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๏Threads

๏Mutual exclusionsynchronization and locks

๏Java Memory Modelread/write barriers & publication

The Java Memory ModelWhat you need to know

๏ Visibility / Publication of objects/fields

๏ (Un)Acceptable out-of-order behaviori.e. guarantees about ordering

๏ Implementations based on the hardware MM of the target platform

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JSR 133

Concurrency abstractions

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Java as we know it

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Threads

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๏ Abstract the line of excution from the CPU(s)

๏ Provided by most (all mainstream) operating systems

๏ CPU execution can continue on another thread if one thread blocks

๏ Increases CPU utilization

Monitors

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synchronized (someMonitor){ // single threaded region}

read barrier

write barrier

Volatile read/write

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class Thing { volatile String name; String getName() { return this.name; } void setName( String newName ) { this.name = newName; }}

read barrier

write barrier

๏Guarantees visibility:always read the latest value

๏Guarantees safe publication:no re-ordering ofpre-write ops withpost-write ops

Monitors

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synchronized (someMonitor){ // single threaded region}

read barrier

write barrier

๏Writes may not be reordered across the end

๏Reads may not be reordered across the start

java.util.concurrent

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ConcurrentMap<String,Thing> map = new ConcurrentHashMap();

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๏Adds atomic operations to the Map interface:- putIfAbsent(key, value)- remove(key, value)- replace(key,[oldVal,]newVal)- but not yet

putIfAbsent(key, #{makeValue()})

๏CHM is lock striped, i.e. synchronized on regions

List<Thing> list = new CopyOnWriteArrayList();

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๏Synchronize writers,unrestricted readers

๏Good for: #reads >> #writes

๏Readers get snapshot state:gives stable iteration

๏Volatile variable for immutable state to ensure publication

public class CopyOnWriteArrayList<T>private volatile Object[] array = new Object[0];

public synchronized boolean add(T value) {Object[] newArray = Arrays.copyOf(array, array.length+1);newArray[array.length] = value;array = newArray; // write barrierreturn true;

} // write barrier

public Iterator<T> iterator() {return new ArrayIterator<T>(this.array); // read barrier

}

private static class ArrayIterator<T> implements Iterator<T> {// I’ve written too many of these to be amused any more ...

}24

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ExecutorService executor = new ThreadPoolExecutor();

executor.submit(new Runnable() { void run() { doWork(); }});

๏Focus on tasks - not threads

๏Mitigate thread start/stop overhead

๏Ensure a balanced number of threads for the target platform

java.util.concurrent.locks

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LockSupport.park(); // currentThread

LockSupport.unpark(Thread t);

๏The thread will “sleep” until unparked

๏Although unpark-before-park marks the thread as unparked, returning from park immediately

๏... and there’s still the chance of spurious wakeups ...

๏Too low level for most people

Lock lock = new ReentrantLock();

ReadWriteLock rwLock = new ReentrantReadWriteLock();

Lock read = rwLock.readLock();Lock write = rwLock.writeLock();

lock.lock();try { doWork() } finally { lock.unlock(); }

if ( lock.tryLock() )try { doWork() } finally { lock.unlock(); }

elsebackOffAndTryAgainLater();

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// Two related locks

java.util.concurrent.atomic

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AtomicReference<Thing> ref = new AtomicReference();

AtomicReferenceArray<Thing> array = new AtomicReferenceArray();

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AtomicReference<Thing> ref = new AtomicReference();

AtomicReferenceArray<Thing> array = new AtomicReferenceArray();

class MyAtomicThingReference { volatile Thing thing; static AtomicReferenceFieldUpdater

<MyAtomicThingReference,Thing> THING = newUpdater(...);

Thing swap( Thing newThing ) { return THING.getAndSet( this, newThing ); }}๏Atomic* gives you compareAndSet()

๏Atomic*Updater saves indirection:

๏One less object header - less memory

๏One read-address-get-object operation:better cache locality

Java of Tomorrow(actually Today, JDK7 is already out)

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ForkJoin

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๏More “advanced” Executor

๏Assumes/requires more of the tasks it runs

๏Tasks first split into multiple sub-tasks, push on stack

๏Idle workers “steal” work from the bottom of the stack of other workers

Models not in Java today

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ParallelArray

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ParallelArray<Student> students = ...

double highestScore = students .filter( #{ Student s -> s.gradYear == 2010 } ) .map( #{ Student s -> s.score } ) .max();

Transactional Memory

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void transfer( TransactionalLong source, TransactionalLong target, long amount ) {

try ( Transaction tx = txManager.beingTransaction() ) {

long sourceFunds = source.getValue(); if (sourceFunds < amount) { throw new InsufficientFundsException(); } source.setValue( sourceFunds - amount ); target.setValue( target.getValue() + amount );

tx.success(); }

}

Actors

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class SimpleActor extends Actor { var state def receive = { case Get => self reply state case Set(newState) => state = newState }}

val response = simple !! Set( "new state" )Th

is m

eans

“s

end

mes

sage

”

Scala Parallel Collection Framework

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Efficient Collection splitting & combining

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Efficient Collection splitting & combining

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Efficient Collection splitting & combining

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Efficient Collection splitting & combining

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Splittable maps

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15

1745

82

Splittable maps

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8 5 15

2 174

Hash tries

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๏Similar performance to hash tables

๏Splittable (like arrays)

๏Memory (re)allocation more like trees

๏No resizing races

Clojure

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Immutable by default

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Thread local(and scoped)

override

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(def x 10) ; create a root binding

(def x 42) ; redefine the root binding

(binding [x 13] ...) ; create a thread local override

Transactional memory(ref) and (dosync ...)

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(def street (ref))(def city (ref))

(defn move [new-street new-city] (dosync ; synchronize the changes (ref-set street new-street) (ref-set city new-city))); will retry if txn fails due to concurrent change

(defn print-address [] (dosync ; get a snapshot state of the refs (printf "I live on %s in %s%n" @street @city)))

(atom)easier API for AtomicReference

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(defstruct address-t :street :city)

(def address (atom))

(defn move [new-street new-city] (set address (struct address-t new-street new-city)))

(defn print-address [] (let [addr @address] ; get snapshot (printf "I live on %s in %s%n" (get addr :street) (get addr :city))))

Explicit asynchronous updates

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(agent)A simpler cousin to actors

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(def account (agent))

(defn deposit [balance amount] (+ balance amount))

(send account deposit 1000)

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