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Dr. C.V. Suresh Babu

5/12/2013

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Dr. C.V. Suresh Babu

UNIT IV SHARED OBJECTS AND CONCURRENT OBJECTS 9
4.1. Shared Objects and Synchronization –
4.2. Properties of Mutual Exclusion4.2.1. The Moral –
4.2.2. The Producer–Consumer Problem –
4.2.3. The Readers–Writers Problem4.3. Realities of Parallelization4.3.1. Parallel Programming- Principles4.4. Mutual Exclusion4.4.1. Time4.4.2. Critical Sections—
4.5. Thread Solutions4.5.1. The Filter Lock4.5.2. Fairness4.5.2. Lamport’s Bakery Algorithm4.6. Bounded Timestamps4.6.1. Lower Bounds on the Number of Locations4.7. Concurrent Objects4.7.1. Concurrency and Correctness4.7.2. Sequential Objects4.7.3. Quiescent Consistency4.7.4. Sequential Consistency4.8. Linearizability4.8.1. Formal Definitions4.8.2. Progress Conditions4.9. The Java Memory Model

4.1. Shared Objects and Synchronization
When processes interact with one another two fundamental requirements must be satisfied:
 Synchronization
 Communication

Balancing the work load using a shared counter. Each thread gets a dynamically
determined number of numbers to test
Counter counter = new Counter(1); // shared by all threads
void primePrint {
long i = 0;
long limit = power(10, 10);
while (i< limit) { // loop until all numbers taken
i = counter.getAndIncrement(); // take next untaken number
if (isPrime(i))
print(i);
}
}

An implementation of the shared counter
public class Counter {
private long value; // counter starts at one
public Counter(inti) { // constructor initializes counter
value = i;
}
public long getAndIncrement() { // increment, returning prior value
return value++;
}
}
4.2. Properties of Mutual Exclusion
First, if one pet wants to enter the yard, then it eventually succeeds. Second, if both pets want
to enter the yard, then eventually at least one of them succeeds. We consider this deadlockfreedom property to be essential.
Another property of compelling interest is starvation-freedom (sometimes called lockoutfreedom): if a pet wants to enter the yard, will it eventually succeed?
Here, Alice and Bob‟s protocol performs poorly.
Need for Mutual Exclusion
 If there is no controlled access to shared data, processes or threads may get an
inconsistent view of this data
 The result of concurrent execution will depend on the order in which instructions are
interleaved.
 Errors are timing dependent and usually not reproducible.
 Assume P1 and P2 are executing this code and share the variable a
 Processes can be preempted at any time.
 Assume P1 is preempted after the input statement, and P2 then executes entirely
 The character echoed by P1 will be the one read by P2 !!

4.2.1. The Moral
First, we examine why shouting across the yard and placing cell phone calls did not work.
Two kinds of communication occur naturally in concurrent systems:
Transient communication requires both parties to participate at the same time.
Shouting, gestures, or cell phone calls are examples of transient communication.
Persistent communication allows the sender and receiver to participate at different
times. Posting letters, sending email, or leaving notes under rocks are all examples of
persistent communication.
2.2. PRODUCER-CONSUMER
The producer – consumer problem is a common implementation pattern for cooperating
processes or threads. Put simply, a producer produces information that is later consumed
by a consumer. Traditionally, the problem is defined as follows:
To allow the producer and consumer to run concurrently, the producer and
consumer must share a common buffer.
So that the consumer does not try to consume an item that has not yet been
produced, the two processes must be synchronized.
If the common data buffer is bounded, the consumer process must wait if the
buffer is empty, and the producer process must wait if the buffer is full.
There are many examples of this problem in “real world” applications.

e.g., a build system may run multiple processes concurrently. The compiler
process will produce assembly code to be read by the assembler process.
_
You would have had to worry about this problem in PA1 had you not used a
socket as the data buffer between your client and server processes. Essentially, the
operating system handled your synchronization issues
PRODUCER PROBLEM
Add last items to the queue. Set queue->empty = 0
i == WORK_MAX, producer exits.
CONSUMER PROBLEM
Remove an item from the queue. A check to see if the queue is empty shows that it is.
Set queue->empty = 1.
Set queue->full = 0
Start spinning.
4.2.3. Readers and Writers Problem

W
R

R
R

Motivation: Consider a shared database
– Two classes of users:
» Readers – never modify database
» Writers – read and modify database
– Is using a single lock on the whole database sufficient?
» Like to have many readers at the same time
» Only one writer at a time
Basic structure of a solution:
– Reader()
Wait until no writers
Access data base
Check out – wake up a waiting writer
– Writer()
Wait until no active readers or writers
Access database
Check out – wake up waiting readers or writer

–

State variables (Protected by a lock called “lock”):
» int AR: Number of active readers; initially = 0
» int WR: Number of waiting readers; initially = 0
» int AW: Number of active writers; initially = 0
» int WW: Number of waiting writers; initially = 0
» Condition okToRead = NIL
» ConditioinokToWrite = NIL
Transform an algorithm to a Java program
The dining philosopher‟s problem is useful for modelling processes that are competing for
exclusive access to a limited number of resources. Another famous problem is the readers
and writers problem, which models access to a data base: it is acceptable to have multiple
processes reading the data base at the same time, but if one process is writing the data
base, no other process may have access to the data base, not even readers.
A solution for this problem, as C fragment is shown below.

typedefint semaphore;

// use your imagination

semaphoremutex = 1;

// controls access to „rc‟

semaphore db = 1;

// controls access to the data base

intrc = 0;

// # of processes reading or wanting to

reader() {
while (true) {

// repeat forever

down(&mutex);

// get exclusive access to „rc‟

rc++;

// one reader more now

if (rc == 1) down(&db); // if this is the first reader …
read_data_base();

// access the data

rc--;

// one reader fewer now

up(&mutex);

// release exclusive access to „rc‟

use_data_read();

// noncritical section

}
}
writer() {
while (true) {

// repeat forever

think_up_data();
down(&db);

// get exclusive access

write_data_base()();

// update the data

up(&db);
}

// noncritical section

// releases exclusive access

}

PRODUCERS SIMPLE APPLICATION
producer()
{
while(true)
{
ifi == N //full buffer
turn = 1
while turn <> 0
{
// nothing
}
produceitem(&item)//produce the item
insertitem(item, buffer)//insert the item in the buffer
turn = 1
//process zone
}
}
consumer()
{
while(true)
{
ifi == 0 //empty buffer
turn = 0
while turn <> 1
{
// nothing
}
consumeitem(&item)
deleteitem(buffer)//erase the item from buffer
turn = 0
//process zone
}
}
Readers and Writers Application
A message distribution system is an example of Readers and Writers. We must keep track of
how many messages are in the queue. If one notebook exists where writers may write
information to, only one writer may write at a time. Confusion may arise if a reader is trying
read at the same as a writer is writing. Since readers only look at the data, but do not modify
the data, we can allow more than one reader to read at the same time. The main complexity
with this problems stems from allowing more than one reader to access the data at the same
time.

Writers have mutual exclusion, but multiple readers at the same time is allowed.
Here is a basic Python class that implements a solution to Readers and Writers using simple
locks:
Importthreading
classReader_writer(Object):
def__init__(self):
self.readers=0
self.writers=0
self.mutex1=threading.Lock()
self.mutex2=threading.Lock()
self.readPending=threading.Lock()
self.writeBlock=threading.Lock()
self.readBlock=threading.Lock()
# This is kind of complicated locking stuff. Don't worry about why there
# are so many locks, it is just part of the known solution to
# the readers and writers problem.
#
# The basic idea of the readers and writers algorithm is to use locks to
# quickly see how many other readers and writers there are. write Block
# is the only lock held while reading the data. Thus the reader only
# prevents writers from entering the critical section. Both write Block
# and read Block are held while writing to the data. Thus the writer
# blocks readers and other writers.

defreader(self,lastread):
"Reader of readers and writers algorithm"
self.readPending.acquire()
self.readBlock.acquire()
self.mutex1.acquire()
self.readers=self.readers+1
ifself.readers==1:self.writeBlock.acquire()
self.mutex1.release()
self.readBlock.release()
self.readPending.release()
#-------------------------#
# critical section
#
#-------------------------self.mutex1.acquire()
self.readers=self.readers-1
ifself.readers==0:self.writeBlock.release()
returnretVal
defwriter(self,data):
"Writer of readers and writers algorithm"
self.writers=self.writers+1
ifself.writers==1:self.readBlock.acquire()
self.writeBlock.acquire()
#-------------------------#
# critical section
#
#-------------------------self.writeBlock.release()
self.writers=self.writers-1
ifself.writers==0:self.readBlock.release()
4.4.2. Critical Sections
For brevity, we say a thread acquires (alternately locks) a lockwhen it executes alock()
method call, and releases (alternately unlocks) the lock when it executes an
1 class Counter {
2 private intvalue;
3 public Counter(intc) { // constructor
4 value = c;
5}
6 // increment and return prior value
7 public intgetAndIncrement() {

8 inttemp = value; // start of danger zone
9 value = temp + 1; // end of danger zone
10 return temp;
11 }
12 }
Figure 2.1 The Counter class.
2.2 Critical Sections 23
1 public interface Lock {
2 public void lock(); // before entering critical section
3 public void unlock(); // before leaving critical section
4}
Figure 2.2 The Lock interface.
1 public class Counter {
2 private long value;
3 private Lock lock; // to protect critical section
45
public long getAndIncrement() {
6 lock.lock(); // enter critical section
7 try {
8 long temp = value; // in critical section
9 value = temp + 1; // in critical section
10 }finally{
11 lock.unlock(); // leave critical section
12 }
13 return temp;
14 }
15 }
Figure 2.3 Using a lock object
.
Threads using the lock() and unlock()methods must follow a specific format. A thread is well
formedif:
1. each critical section is associated with a unique Lock object,
2. the thread calls that object‟s lock() method when it is trying to enter the
critical section, and
3. the thread calls the unlock() method when it leaves the critical section.
Pragma2.2.1. In Java, these methods should be used in the following structured way.
1 mutex.lock();
2 try {
3 ... // body
4 }finally{
5 mutex.unlock();
6}
This idiom ensures that the lock is acquired before entering the try block, andthat the lock is
released when control leaves the block, even if some statementin the block throws an
unexpected exception.

Mutual Exclusion Critical sections of different threads do not overlap. ForThreadsA and B,
and integers j and k, either CSk
A →CSj
B or CSj
B →CSk
A.
Freedom from Deadlock If some thread attempts to acquire the lock, then some thread will
succeed in acquiring the lock. If thread A calls lock() but never acquires the lock, then other
threads must be completing an infinite number of critical sections.
Freedom from Starvation Every thread that attempts to acquire the lock eventually
succeeds. Every call to lock() eventually returns. This property is sometimes called lockout
freedom.
4.5. Thread Solutions
The LockOneClass
The LockOnealgorithm satisfies mutual exclusion.
Proof: Suppose not. Then there exist integers j and k such that CSj
A _→ CSk
B and
CSk
B _→ CSj
A. Consider each thread‟s last execution of the lock() method before
entering its k-th (j-th) critical section.
Inspecting the code, we see that
writeA(flag[A] = true)→readA(flag[B] == false)→CSA (2.3.1)
writeB(flag[B] = true)→readB(flag[A] == false)→CSB (2.3.2)
readA(flag[B] == false)→writeB(flag[B] = true) (2.3.3)
Note that once flag[B] is set to true it remains true. It follows that Eq. 2.3.3
holds, since otherwise thread A could not have read flag[B] as false. Eq. 2.3.4
follows from Eqs. 2.3.1–2.3.3, and the transitivity of the precedence order.
class LockOneimplementsLock {
private boolean[] flag = new boolean[2];
// thread-local index, 0 or 1
public void lock() {
inti = ThreadID.get();
intj = 1 - i;
flag[i] = true;
while (flag[j]) {} // wait
}
public void unlock() {
inti = ThreadID.get();
flag[i] = false;
}
}
Figure 2.4 TheLockOnealgorithm.
writeA(flag[A] = true)→readA(flag[B] == false)→ (2.3.4)
writeB(flag[B] = true)→readB(flag[A] == false)
It follows that writeA(flag[A] = true)→readB(flag[A] == false) without an

intervening write to the flag[] array, a contradiction. _
The Lock One algorithm is inadequate because it deadlocks if thread executions
are interleaved. If writeA(flag[A] = true) and writeB(flag[B] = true) events
occur before readA(flag[B]) and readB(flag[A]) events, then both threads wait
forever. Nevertheless, LockOnehas an interesting property: if one thread runs
before the other, no deadlock occurs, and all is well.
The LockTwoClass
The LockTwoalgorithm satisfies mutual exclusion.
Proof: Suppose not. Then there exist integers j and k such that CSj
A _→ CSk
B and
CSk
B _→ CSj
A. Consider as before each thread‟s last execution of the lock() method
before entering its k-th (j-th) critical section.
Inspecting the code, we see that
writeA(victim = A)→readA(victim == B)→CSA (2.3.5)
writeB(victim = B)→readB(victim == A)→CSB (2.3.6)
Thread B must assign B to the victim field between events writeA(victim = A)
andreadA(victim = B) (see Eq. 2.3.5). Since this assignment is the last, we have
writeA(victim = A)→writeB(victim = B)→readA(victim == B). (2.3.7)
Once the victim field is set to B, it does not change, so any subsequent read
returnsB, contradicting Eq. 2.3.6. _
1 class LockTwoimplementsLock {
2 private volatile intvictim;
3 public void lock() {
4 inti = ThreadID.get();
5 victim = i; // let the other go first
6 while (victim == i) {} // wait
7}
8 public void unlock() {}
9}
Figure 2.5 TheLockTwoalgorithm.
Example of lock algorithm
The Peterson lock algorithm satisfies mutual exclusion.
Proof: Suppose not. As before, consider the last executions of the lock() method
by threads A and B. Inspecting the code, we see that
writeA(flag[A] = true)→ (2.3.8)
writeA(victim = A)→readA(flag[B])→readA(victim)→CSA
writeB(flag[B] = true)→ (2.3.9)
writeB(victim = B)→readB(flag[A])→readB(victim)→CSB
Assume, without loss of generality, that A was the last thread to write to the
victimfield.
writeB(victim = B)→writeA(victim = A) (2.3.10)
1 class Peterson implements Lock {
2 // thread-local index, 0 or 1
3 private volatile boolean[] flag = new boolean[2];

4 private volatile intvictim;
7 intj = 1 - i;
8 flag[i] = true; // I’m interested
9 victim = i; // you go first
10 while (flag[j] && victim == i) {}; // wait
11 }
12 public void unlock() {
14 flag[i] = false; // I’m not interested
15 }
16 }
Figure 2.6 The Peterson lock algorithm.
4.5.1. The Filter Lock
The levels are depicted in Fig. 2.8. Levels satisfy two important properties:
_ At least one thread trying to enter level _ succeeds.
_ If more than one thread is trying to enter level _, then at least one is blocked
(i.e., continues to wait at that level).
2.4 The Filter Lock 29
1 class Filter implements Lock {
2 int[] level;
3 int[] victim;
4 public Filter(intn) {
5 level = new int[n];
6 victim = new int[n]; // use 1..n-1
7 for (inti = 0; i< n; i++) {
8 level[i] = 0;
9}
10 }
12 intme = ThreadID.get();
13 for (inti = 1; i< n; i++) { //attempt level 1
14 level[me] = i;
15 victim[i] = me;
16 // spin while conflicts exist
17 while ((∃k != me) (level[k] >= i&& victim[i] == me)) {};
18 }
19 }
21 intme = ThreadID.get();
22 level[me] = 0;
23 }
24 }
Figure 2.7 The Filter lock algorithm.
4.5.2. Fairness
A doorwaysection, whose execution intervalDAconsists of a bounded numberof steps, and
2. awaitingsection, whose execution intervalWAmay take an unbounded numberof steps

4.5.2. Lamport’s Bakery Algorithm
1 class Bakery implements Lock {
2 boolean[] flag;
3 Label[] label;
4 public Bakery (intn) {
5 flag = new boolean[n];
6 label = new Label[n];
7 for (inti = 0; i< n; i++) {
8 flag[i] = false; label[i] = 0;
9}
10 }
13 flag[i] = true;
14 label[i] = max(label[0], ...,label[n-1]) + 1;
15 while ((∃k != i)(flag[k] && (label[k],k) << (label[i],i))) {};
16 }
18 flag[ThreadID.get()] = false;
19 }
20 }
Figure 2.9 The Bakery lock algorithm.
4.6. Bounded Timestamps
we see that a thread needs two abilities:
_ to read the other threads‟ timestamps (scan), and
_ to assign itself a later timestamp (label).
1 public interface Timestamp {
2 booleancompare(Timestamp);
3}
4 public interface TimestampSystem {
5 public Timestamp[] scan();
6 public void label(Timestamp timestamp, inti);
7}
Figure 2.10 Atimestamping system interface.
4.6.1. Lower Bounds on the Number of Locations
Definition: A Lock object state sis inconsistent in any global state where some thread is in
the critical section, but the lock state is compatible with a global state in which no thread is in
the critical section or is trying to enter the critical section.
Lemma2.8.1. No deadlock-free Lock algorithm can enter an inconsistent state.
Proof: Suppose the Lock object is in an inconsistent state s, where no thread is in the critical
section or trying to enter. If thread B tries to enter the critical section, it must eventually
succeed, because the algorithm is deadlock-free. Suppose the Lock object is in an

inconsistent state s, where A is in the critical section. If thread B tries to enter the critical
section, it must block until A leaves.
We have a contradiction, because B cannot determine whether A is in the critical
section. _
Any Lock algorithm that solves deadlock-free mutual exclusion must have
Ndistinct locations. Here, we consider only the 3-thread case.Ndeadlock-freeLock algorithm
accessed by three threads must use three distinctlocations.
Definition 2.8.2.A covering state for a Lock object is one in which there is at leastone thread
about to write to each shared location, but the Lock object‟s locations“look” like the critical
section is empty (i.e., the locations‟ states appear as if thereis no thread either in the critical
section or trying to enter the critical section).
4.7.1. Concurrency and Correctness
Concurrent Objects
1 class LockBasedQueue<T> {
2 inthead, tail;
3 T[] items;
4 Lock lock;
5 public LockBasedQueue(intcapacity) {
6 head = 0; tail = 0;
7 lock = new ReentrantLock();
8 items = (T[])new Object[capacity];
9}
10 public void enq(T x) throws FullException {
11 lock.lock();
12 try {
13 if (tail - head == items.length)
14 throw new FullException();
15 items[tail % items.length] = x;
16 tail++;
17 }finally{
18 lock.unlock();
19 }
20 }
21 public T deq() throws EmptyException {
22 lock.lock();
23 try {
24 if (tail == head)
25 throw new EmptyException();
26 T x = items[head % items.length];
27 head++;
28 return x;
29 }finally{
30 lock.unlock();
31 }
32 }
33 }
Figure 3.1 A lock-based FIFO queue. The queue‟s items are kept in an array items, where
headis the index of the next item to dequeue, and tail is the index of the first open array

slot (modulo the capacity). The lock field is a lock that ensures that methods are mutually
exclusive. Initially head and tail are zero, and the queue is empty. If enq() finds the queue
is full, i.e., head and tail differ by the queue size, then it throws an exception. Otherwise,
there is room, so enq() stores the item at array entry tail, and then increments tail. The
deq() method works in a symmetric way.
A single-enqueuer/single-dequeuer FIFO queue.
1 class WaitFreeQueue<T> {
2 volatile inthead = 0, tail = 0;
3 T[] items;
4 public WaitFreeQueue(intcapacity) {
5 items = (T[])new Object[capacity];
6 head = 0; tail = 0;
7}
8 public void enq(T x) throws FullException {
9 if (tail - head == items.length)
10 throw new FullException();
11 items[tail % items.length] = x;
12 tail++;
13 }
14 public T deq() throws EmptyException {
15 if (tail - head == 0)
16 throw new EmptyException();
17 T x = items[head % items.length];
18 head++;
19 return x;
20 }
21 }

4.7.4. Sequential Consistency
An execution is sequentially consistent if the method calls can be correctly arranged
retaining the mutual order of method calls in eachthread.The calls in different threads can be
re-arranged however you wish, regardless of when they start and when they end.
An example of a sequentially consistent system is a naive notion of RAM in multi-core
processor. When different threads read from the memory, they actually read from a cache or a
register, which is synchronized with a common RAM at writes. While values read and written
on different cores may not be immediately synchronized (cores may diverge in the notion of
what value a cell contains), the values read and written on the same core are always
consistent with each other. This is naive, though: even this guarantee is relaxed by modern
compilers, and accesses to different variables within one thread can be reordered.
You might notice that the definition of sequential consistency does not directly prevents
methods to return values from the future (i.e. to read what will be written later in a different
thread). Yet I claim that RAM is sequentially consistent; how's that? The thing is that each
execution should be sequentially consistent. If read and write do not overlap, and write

follows the read, we may just stop the program between them. Since each execution should
be consistent, the value read should be written before in some thread. Therefore, this "future
prediction" is not possible in consistent data structures (not only those that are "sequentially"
consistent) even that the definition doesn't directly prevent it.
However, if we combine two sequentially consistent executions, the result won't be
sequentially consistent. But there is another model, weaker than linearizability, that makes it
possible.
4.7.3. Quiescent Consistency
An execution is quiescently consistent if the method calls can be correctly arranged
retaining the mutual order of calls separated by quiescence, a period of time where no method
is being called in any thread.
This consistency condition is stronger than sequential consistency, but is still not strong
enough that we usually expect from a multithreaded system. The reason to use this model is
to attain a higher degree of concurrency and better response time in return of weakening
consistency a bit.
Quiescent consistency is not as esoteric as you might think, especially since you barely
heard about it. Here's an easy way to imagine a quiescently consistent system: assume that all
writes are cached, and the cache is flushed to a persistent storage when there are no ongoing
writes (or after it reaches certain size). There are specialdatastructures that are quiescently
consistent.Quiescent consistency components can be combined to form quiescently consistent
components again. Note also, that strong consistency implies quiescent consistency, hence
you can combine these two kinds of systems, and the result will be quiescently consistent.
If you're a system developer, you should note that you are not bound to choose between
linearizability and "thread-unsafely" when designing a library. The models presented above
may be viewed as patterns that enumerate what behaviors in multithreaded context are
considered universally useful. Just consider carefully what your users need, and do not forget
to specify which model your system follows. It will also help to link to something that
explains what lies behind the words "something consistent".
4.8. LINEARIZABILITY
A concurrent object is a data object shared by concurrent processes. Linearizability is a
correctness condition for concurrent objects that exploits the semantics of abstract data types.
It permits a high degree of concurrency, yet it permits programmers to specify and reason
about concurrent objects using known techniques from the sequential domain. Linearizability
provides the illusion that each operation applied by concurrent processes takes effect
instantaneously at some point between its invocation and its response, implying that the
meaning of a concurrent object‟s operations can be given by pre- and post-conditions.
4.9. JAVA MEMORY MODEL:
The Java memory model describes how threads in the Java programming language interact
through memory. Together with the description of single-threaded execution of code,

the memory model provides the semantics of the Java programming language.
The Java programming language and platform provide thread capabilities. Synchronization
between threads is notoriously difficult for developers; this difficulty is compounded because
Java applications can run on a wide range of processors and operating systems. To be able to
draw conclusions about a program's behavior, Java's designers decided they had to clearly
define possible behaviors of all Java programs.
On modern platforms, code is frequently not executed in the order it was written. It is
reordered by the compiler, the processor and the memory subsystem to achieve maximum
performance. On multiprocessor architectures, individual processors may have their own
local caches that are out of sync with main memory. It is generally undesirable to require
threads to remain perfectly in sync with one another because this would be too costly from a
performance point of view. This means that at any given time, different threads may see
different values for the same shared data.
In a single-threaded environment, it is easy to reason about code execution. The typical
approach requires the system to implement as-if-serial semantics for individual threads in
isolation. When an individual thread executes, it will appear as if all of the actions taken by
that thread occur in the order they appear in the program, even if the actions themselves occur
out of order.
If one thread executes its instructions out of order, then another thread might see the fact that
those instructions were executed out of order, even if that did not affect the semantics of the
first thread. For example, consider two threads with the following instructions, executing
concurrently, where the variables x and y are both initialized to 0:
Thread 1 Thread 2
x = 1;
int r1 = y;
y = 2;
int r2 = x;
If no reorderings are performed, and the read of y in Thread 2 returns the value 2, then the
subsequent read of x should return the value 1, because the write to x was performed before
the write to y. However, if the two writes are reordered, then the read of y can return the
value 2, and the read of x can return the value 0.
The Java Memory Model (JMM) defines the allowable behavior of multithreaded programs,
and therefore describes when such reorderings are possible. It places execution-time
constraints on the relationship between threads and main memory in order to achieve
consistent and reliable Java applications. By doing this, it makes it possible to reason about
code execution in a multithreaded environment, even in the face of optimizations performed
by the dynamic compiler, the processor(s) and the caches.
For execution of a single thread, the rules are simple. The Java Language Specification
requires a Java Virtual Machine to observe within-thread as-if-serial semantics. The runtime
(which, in this case, usually refers to the dynamic compiler, the processor and the memory
subsystem) is free to introduce any useful execution optimizations as long as the result of the
thread in isolation is guaranteed to be exactly the same as it would have been had all the

statements been executed in the order the statements occurred in the program (also called
program order).
The major caveat of this is that as-if-serial semantics do not prevent different threads from
having different views of the data. The memory model provides clear guidance about what
values are allowed to be returned when the data are read. The basic rules imply that
individual actions can be reordered, as long as the as-if-serial semantics of the thread are not
violated, and actions that imply communication between threads, such as the acquisition or
release of a lock, ensure that actions that happen prior to them are seen by other threads that
see their effects. For example, everything that happens before the release of a lock will be
seen to be ordered before and visible to everything that happens after a subsequent
acquisition of that same lock.
Mathematically, there is a partial order called the happens-before order over all actions
performed by the program. The happens-before order subsumes the program order; if one
action occurs before another in the program order, it will occur before the other in the
happens-before order. In addition, releases and subsequent acquires of locks form edges in
the happens-before graph. A read is allowed to return the value of a write if that write is the
last write to that variable before the read along some path in the happens-before order, or if
the write is not ordered with respect to that read in the happens-before order.
The Java memory model was the first attempt to provide a comprehensive memory model for
a popular programming language.

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