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Synchronized has two important meanings: it ensures that only one thread executes a protected section of code at one time mutual exclusion or mutex , and it ensures that data changed by one thread is visible to other threads visibility of changes. Without synchronization, it is easy for data to be left in an inconsistent state. For example, if one thread is updating two related values say, the position and velocity of a particle , and another thread is reading those two values, it is possible that the second thread could be scheduled to run after the first thread has written one value but not the other, thus seeing one old and one new value.
Synchronization allows us to define blocks of code that must run atomically, in which they appear to execute in an all-or-nothing manner, as far as other threads can tell. The atomic execution or mutual exclusion aspect of synchronization is similar to the concept of critical sections in other operating environments. Ensuring visibility of changes to shared data Synchronization allows us to ensure that threads see consistent views of memory.
Processors can use caches to speed up access to memory or compilers may store values in registers for faster access. This means that on such systems, it is possible for two threads executing on two different processors to see two different values for the same variable! This sounds scary, but it is normal. It just means that you have to follow some rules when accessing data used or modified by other threads. Volatile is simpler than synchronization and is suitable only for controlling access to single instances of primitive variables — integers, booleans, and so on.
When a variable is declared volatile, any write to that variable will go directly to main memory, bypassing the cache, while any read of that variable will come directly from main memory, bypassing the cache. This means that all threads see the same value for a volatile variable at all times. Without proper synchronization, it is possible for threads to see stale values of variables or experience other forms of data corruption.
Atomic code blocks protected by locks Volatile is useful for ensuring that each thread sees the most recent value for a variable, but sometimes we need to protect access to larger sections of code, such as sections that involve updating multiple variables. Synchronization uses the concepts of monitors, or locks, to coordinate access to particular blocks of code.
Every Java object has an associated lock. Java locks can be held by no more than one thread at a time. When a thread enters a synchronized block of code, the thread blocks and waits until the lock is available, acquires the lock when it becomes available, and then executes the block of code.
It releases the lock when control exits the protected block of code, either by reaching the end of the block or when an exception is thrown that is not caught within the synchronized block. In this way, only one thread can execute a block protected by a given monitor at one time. The block can be considered atomic because, from the perspective of other threads, it appears to either have executed entirely or not at all.
A simple synchronization example Using synchronized blocks allows you to perform a group of related updates as a set without worrying about other threads interrupting or seeing the intermediate results of a computation.
Java locking Java locking incorporates a form of mutual exclusion. Only one thread may hold a lock at one time. Locks are used to protect blocks of code or entire methods, but it is important to remember that it is the identity of the lock that protects a block of code, not the block itself.
One lock may protect many blocks of code or methods. Conversely, just because a block of code is protected by a lock does not mean that two threads cannot execute that block at once. It only means that two threads cannot execute that block at once if they are waiting on the same lock. In the following example, the two threads are free to execute the synchronized block in setLastAccess simultaneously because each thread has a different value for thingie. Therefore, the synchronized block is protected by different locks in the two executing threads.
For static synchronized methods, this lock will be the monitor associated with the Class object in which the method is declared. Only one thread can execute setXY , or any other synchronized method of Point, on a single Point instance at one time.
Synchronized blocks The syntax for synchronized blocks is a little more complicated than for synchronized methods because you also need to explicitly specify what lock is being protected by the block. This means that the block will use the same lock as synchronized methods in that class. Because synchronization prevents multiple threads from executing a block at once, it has performance implications, even on uniprocessor systems. It is a good practice to use synchronization around the smallest possible block of code that needs to be protected.
Access to local stack-based variables never need to be protected, because they are only accessible from the owning thread. Most classes are not synchronized Because synchronization carries a small performance penalty, most general-purpose classes, like the Collection classes in java. This means that classes like HashMap cannot be used from multiple threads without additional synchronization. You can use the Collections classes in a multithreaded application by using synchronization every time you access a method in a shared collection.
For any given collection, you must synchronize on the same lock each time. A common choice of lock would be the collection object itself. The example class SimpleCache below shows how you can use a HashMap to provide caching in a thread-safe way.
The Collections class provides us with a set of convenience wrappers for the List, Map, and Set interfaces. You can wrap a Map with Collections. If the documentation for a class does not say that it is thread-safe, then you must assume that it is not. Example: A simple thread-safe cache As shown in the following code sample, SimpleCache. The load method knows how to load an object by its key. After an object is loaded once, it is stored in the cache so subsequent accesses will retrieve it from the cache instead of loading it all over each time.
Each access to the shared cache is protected by a synchronized block. Because it is properly synchronized, multiple threads can call the getObject and clearCache methods simultaneously without risk of data corruption. By using synchronization to protect access to shared variables, we can ensure that threads interact with program variables in predictable ways. Every Java object can act as a lock, and synchronized blocks can ensure that only one thread executes synchronized code protected by a given lock at one time.
Synchronization details Mutual exclusion In Sharing access to data , we discussed the characteristics of synchronized blocks and described them as implementing a classic mutex that is, mutual exclusion or critical section , in which only one thread can execute a block protected by a given lock at one time.
Mutual exclusion is an important part of what synchronization does, but synchronization also has several other characteristics important to achieve correct results on multiprocessor systems.
Visibility In addition to mutual exclusion, synchronization, like volatile, enforces certain visibility constraints. When an object acquires a lock, it first invalidates its cache, so that it is guaranteed to load variables directly from main memory. Similarly, before an object releases a lock, it flushes its cache, forcing any changes made to appear in main memory.
In this way, two threads that synchronize on the same lock are guaranteed to see the same values in variables modified inside a synchronized block. When do you have to synchronize? To maintain proper visibility across threads, you have to use synchronized or volatile to ensure that changes made by one thread are visible by another whenever a non-final variable is shared among several threads.
The basic rule for synchronizing for visibility is that you must synchronize whenever you are: Reading a variable that may have been last written by another thread Writing a variable that may be read next by another thread Synchronization for consistency In addition to synchronizing for visibility, you must also synchronize to ensure that consistency is maintained from an application perspective.
When modifying multiple related values, you want other threads to see that set of changes atomically — either all of the changes or none of them. This applies to related data items such as the position and velocity of a particle and metadata items such as the data values contained in a linked list and the chain of entries in the list itself.
It could be a disaster. What if one thread calls push and another thread calls push right between the time top is incremented and it is used as an index into values?
Then both threads would store their new value in the same location! Note that using volatile would not have been enough — you need to use synchronized to ensure that the relationship between top and values remains consistent. However, if new values of the variable are derived from previous values, you will have to use synchronization. Look at the code for increment.
It is clear, but it is not thread-safe. What happens if two threads try to execute increment at the same time?
The counter might be incremented by 1 or by 2. Imagine that the counter is zero and two threads execute the increment code at exactly the same time. This can create problems if a cooperatively multitasked thread blocks by waiting on a resource or if it starves other threads by not yielding control of execution during intensive computation. Until the early s, most desktop computers had only one single-core CPU, with no support for hardware threads , although threads were still used on such computers because switching between threads was generally still quicker than full-process context switches.
Processes, kernel threads, user threads, and fibers[ edit ] Main articles: Process computing and Fiber computer science Scheduling can be done at the kernel level or user level, and multitasking can be done preemptively or cooperatively.
This yields a variety of related concepts. At the kernel level, a process contains one or more kernel threads, which share the process's resources, such as memory and file handles — a process is a unit of resources, while a thread is a unit of scheduling and execution. Kernel scheduling is typically uniformly done preemptively or, less commonly, cooperatively.
At the user level a process such as a runtime system can itself schedule multiple threads of execution. If these do not share data, as in Erlang, they are usually analogously called processes,  while if they share data they are usually called user threads, particularly if preemptively scheduled. Cooperatively scheduled user threads are known as fibers; different processes may schedule user threads differently.
User threads may be executed by kernel threads in various ways one-to-one, many-to-one, many-to-many. The term " light-weight process " variously refers to user threads or to kernel mechanisms for scheduling user threads onto kernel threads.
A process is a "heavyweight" unit of kernel scheduling, as creating, destroying, and switching processes is relatively expensive. Processes own resources allocated by the operating system.
Resources include memory for both code and data , file handles , sockets, device handles, windows, and a process control block. Processes are isolated by process isolation , and do not share address spaces or file resources except through explicit methods such as inheriting file handles or shared memory segments, or mapping the same file in a shared way — see interprocess communication.
Creating or destroying a process is relatively expensive, as resources must be acquired or released. Processes are typically preemptively multitasked, and process switching is relatively expensive, beyond basic cost of context switching , due to issues such as cache flushing. At least one kernel thread exists within each process. If multiple kernel threads exist within a process, then they share the same memory and file resources.
Kernel threads are preemptively multitasked if the operating system's process scheduler is preemptive. Kernel threads do not own resources except for a stack , a copy of the registers including the program counter , and thread-local storage if any , and are thus relatively cheap to create and destroy. Thread switching is also relatively cheap: it requires a context switch saving and restoring registers and stack pointer , but does not change virtual memory and is thus cache-friendly leaving TLB valid.
The kernel can assign one thread to each logical core in a system because each processor splits itself up into multiple logical cores if it supports multithreading, or only supports one logical core per physical core if it does not , and can swap out threads that get blocked. However, kernel threads take much longer than user threads to be swapped.
Threads are sometimes implemented in userspace libraries, thus called user threads. The kernel is unaware of them, so they are managed and scheduled in userspace. Some implementations base their user threads on top of several kernel threads, to benefit from multi-processor machines M:N model.
In this article the term "thread" without kernel or user qualifier defaults to referring to kernel threads.
User threads as implemented by virtual machines are also called green threads. User threads are generally fast to create and manage, but cannot take advantage of multithreading or multiprocessing, and will get blocked if all of their associated kernel threads get blocked even if there are some user threads that are ready to run. Fibers are an even lighter unit of scheduling which are cooperatively scheduled : a running fiber must explicitly " yield " to allow another fiber to run, which makes their implementation much easier than kernel or user threads.
A fiber can be scheduled to run in any thread in the same process. This permits applications to gain performance improvements by managing scheduling themselves, instead of relying on the kernel scheduler which may not be tuned for the application. Parallel programming environments such as OpenMP typically implement their tasks through fibers. Closely related to fibers are coroutines , with the distinction being that coroutines are a language-level construct, while fibers are a system-level construct.
Thread and fiber issues[ edit ] Concurrency and data structures[ edit ] Threads in the same process share the same address space. This allows concurrently running code to couple tightly and conveniently exchange data without the overhead or complexity of an IPC. When shared between threads, however, even simple data structures become prone to race conditions if they require more than one CPU instruction to update: two threads may end up attempting to update the data structure at the same time and find it unexpectedly changing underfoot.
Bugs caused by race conditions can be very difficult to reproduce and isolate. To prevent this, threading application programming interfaces APIs offer synchronization primitives such as mutexes to lock data structures against concurrent access. On uniprocessor systems, a thread running into a locked mutex must sleep and hence trigger a context switch. On multi-processor systems, the thread may instead poll the mutex in a spinlock. Both of these may sap performance and force processors in symmetric multiprocessing SMP systems to contend for the memory bus, especially if the granularity of the locking is fine.
Although threads seem to be a small step from sequential computation, in fact, they represent a huge step. They discard the most essential and appealing properties of sequential computation: understandability, predictability, and determinism. Threads, as a model of computation, are wildly non-deterministic, and the job of the programmer becomes one of pruning that nondeterminism.
As a result, context switching between user threads or fibers within the same process is extremely efficient because it does not require any interaction with the kernel at all: a context switch can be performed by locally saving the CPU registers used by the currently executing user thread or fiber and then loading the registers required by the user thread or fiber to be executed.
Since scheduling occurs in userspace, the scheduling policy can be more easily tailored to the requirements of the program's workload.
However, the use of blocking system calls in user threads as opposed to kernel threads or fibers can be problematic. If a user thread or a fiber performs a system call that blocks, the other user threads and fibers in the process are unable to run until the system call returns.