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In computer science, a lock is a synchronization mechanism for enforcing limits on access to a resource in an environment where there are many threads of execution. Locks are one way of enforcing concurrency control policies.
Types Generally, locks are advisory locks, where each thread cooperates by acquiring the lock before accessing the corresponding data. Some systems also implement mandatory locks, where attempting unauthorized access to a locked resource will force an exception in the entity attempting to make the access. A (binary) semaphore is the simplest type of lock. No distinction is made between shared (read only) or exclusive (read and write) modes. Other schemes provide for a shared mode, where several threads can acquire a shared lock for read-only access to the data. Other modes such as shared, exclusive, intend-to-exclude and intend-to-upgrade are also widely implemented. Independent of the type of lock chosen above, locks can be classified by what happens when the lock strategy prevents progress of a thread. Most locking designs block the execution of the process requesting the lock until it is allowed to access the locked resource. A spinlock is a lock where the thread simply waits ("spins") until the lock becomes available. It is very efficient if threads are only likely to be blocked for a short period of time, as it avoids the overhead of operating system process re-scheduling. It is wasteful if the lock is held for a long period of time. Implementation Locks typically require hardware support for efficient implementation. This usually takes the form of one or more atomic instructions such as "test-and-set", "fetch-and-add" or "compare-and-swap". These instructions allow a single process to test if the lock is free, and if free, acquire the lock in a single atomic operation. Uniprocessor architectures have the option of using uninterruptable sequences of instructions, using special instructions or instruction prefixes to disable interrupts temporarily, but this technique does not work for multiprocessor shared-memory machines. Proper support for locks in a multiprocessor environment can require quite complex hardware and/or software support, with substantial synchronization issues. The reason an atomic operation is required is because of concurrency, where more than one task executes the same logic. For example, consider the following C code: if (lock == 0) lock = myPID; / The above example does not guarantee that the task has the lock, since more than one task can be testing the lock at the same time. Since both tasks will detect that the lock is free, both tasks will attempt to set the lock, not knowing that the other task is also setting the lock. Dekker's or Peterson's algorithm are possible substitutes if atomic locking operations are not available. Careless use of locks can result in deadlock. This occurs when a process holds a lock and then attempts to acquire a second lock. If the second lock is already held by another process, the first process will be blocked. If the second process then attempts to acquire the lock held by the first process, the system has "deadlocked": no progress will ever be made. A number of strategies can be used to avoid or recover from deadlocks, both at design-time and at run-time. Granularity Before introducing lock granularity, one needs to understand three concepts about locks. So there is a tradeoff between decreasing lock overhead and decreasing lock contention when choosing the number of locks in synchronization. An important property of a lock is its granularity. The granularity is a measure of the amount of data the lock is protecting. In general, choosing a coarse granularity (a small number of locks, each protecting a large segment of data) results in less lock overhead when a single process is accessing the protected data, but worse performance when multiple processes are running concurrently. This is because of increased lock contention the more coarse the lock, the higher the likelihood that the lock will stop an unrelated process from proceeding. Conversely, using a fine granularity (a larger number of locks, each protecting a fairly small amount of data) increases the overhead of the locks themselves but reduces lock contention. More locks also increase the risk of deadlock. In a database management system, for example, a lock could protect, in order of increasing granularity, a record, a data page, or an entire table. Coarse granularity, such as using table locks, tends to give the best performance for a single user, whereas fine granularity, such as record locks, tends to give the best performance for multiple users. Database locks In databases, locks can be used as a means of ensuring transaction synchronicity. i.e. when making transaction processing concurrent (interleaving transactions), using 2-phased locks ensures that the concurrent execution of the transaction turns out equivalent to some serial ordering of the transaction. However, deadlocks become an unfortunate side-effect of locking in databases. Deadlocks are either prevented by pre-determining the locking order between transactions or are detected using waits-for graphs. An alternate to locking for database synchronicity while avoiding deadlocks involves the use of totally ordered global timestamps. The problems with locks Lock-based resource protection and thread/process synchronization has many disadvantages: One strategy is to avoid locks entirely by using non-blocking synchronization methods, like lock-free programming techniques and transactional memory. The lock keyword In the C# programming language, the lock keyword can be used to ensure that a block of code runs to completion without interruption by other threads, similar to the Java keyword synchronized. For more information, see Barrier (computer science). See also | ||||||||
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