Thread safety

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In multi-threaded computer programming, a function is thread-safe when it can be invoked or accessed concurrently by multiple threads without causing unexpected behavior, race conditions, or data corruption. [1] [2] As in the multi-threaded context where a program executes several threads simultaneously in a shared address space and each of those threads has access to every other thread's memory, thread-safe functions need to ensure that all those threads behave properly and fulfill their design specifications without unintended interaction. [3]

Contents

There are various strategies for making thread-safe data structures. [3]

Levels of thread safety

Different vendors use slightly different terminology for thread-safety, [4] but the most commonly use thread-safety terminology are: [2]

Thread safety guarantees usually also include design steps to prevent or limit the risk of different forms of deadlocks, as well as optimizations to maximize concurrent performance. However, deadlock-free guarantees cannot always be given, since deadlocks can be caused by callbacks and violation of architectural layering independent of the library itself.

Software libraries can provide certain thread-safety guarantees. [5] For example, concurrent reads might be guaranteed to be thread-safe, but concurrent writes might not be. Whether a program using such a library is thread-safe depends on whether it uses the library in a manner consistent with those guarantees.

Implementation approaches

Below we discuss two classes of approaches for avoiding race conditions to achieve thread-safety.

The first class of approaches focuses on avoiding shared state and includes:

Re-entrancy [6]
Writing code in such a way that it can be partially executed by a thread, executed by the same thread, or simultaneously executed by another thread and still correctly complete the original execution. This requires the saving of state information in variables local to each execution, usually on a stack, instead of in static or global variables or other non-local state. All non-local states must be accessed through atomic operations and the data-structures must also be reentrant.
Thread-local storage
Variables are localized so that each thread has its own private copy. These variables retain their values across subroutine and other code boundaries and are thread-safe since they are local to each thread, even though the code which accesses them might be executed simultaneously by another thread.
Immutable objects
The state of an object cannot be changed after construction. This implies both that only read-only data is shared and that inherent thread safety is attained. Mutable (non-const) operations can then be implemented in such a way that they create new objects instead of modifying the existing ones. This approach is characteristic of functional programming and is also used by the string implementations in Java, C#, and Python. (See Immutable object.)

The second class of approaches are synchronization-related, and are used in situations where shared state cannot be avoided:

Mutual exclusion
Access to shared data is serialized using mechanisms that ensure only one thread reads or writes to the shared data at any time. Incorporation of mutual exclusion needs to be well thought out, since improper usage can lead to side-effects like deadlocks, livelocks, and resource starvation.
Atomic operations
Shared data is accessed by using atomic operations which cannot be interrupted by other threads. This usually requires using special machine language instructions, which might be available in a runtime library. Since the operations are atomic, the shared data is always kept in a valid state, no matter how other threads access it. Atomic operations form the basis of many thread locking mechanisms, and are used to implement mutual exclusion primitives.

Examples

In the following piece of Java code, the Java keyword synchronized makes the method thread-safe:

classCounter{privateinti=0;publicsynchronizedvoidinc(){i++;}}

In the C programming language, each thread has its own stack. However, a static variable is not kept on the stack; all threads share simultaneous access to it. If multiple threads overlap while running the same function, it is possible that a static variable might be changed by one thread while another is midway through checking it. This difficult-to-diagnose logic error, which may compile and run properly most of the time, is called a race condition. One common way to avoid this is to use another shared variable as a "lock" or "mutex" (from mutual exclusion).

In the following piece of C code, the function is thread-safe, but not reentrant:

#include<pthread.h>intincrement_counter(){staticintcounter=0;staticpthread_mutex_tmutex=PTHREAD_MUTEX_INITIALIZER;// only allow one thread to increment at a timepthread_mutex_lock(&mutex);++counter;// store value before any other threads increment it furtherintresult=counter;pthread_mutex_unlock(&mutex);returnresult;}

In the above, increment_counter can be called by different threads without any problem since a mutex is used to synchronize all access to the shared counter variable. But if the function is used in a reentrant interrupt handler and a second interrupt arises while the mutex is locked, the second routine will hang forever. As interrupt servicing can disable other interrupts, the whole system could suffer.

The same function can be implemented to be both thread-safe and reentrant using the lock-free atomics in C++11:

#include<atomic>intincrement_counter(){staticstd::atomic<int>counter(0);// increment is guaranteed to be done atomicallyintresult=++counter;returnresult;}

See also

Related Research Articles

<span class="mw-page-title-main">Mutual exclusion</span> In computing, restricting data to be accessible by one thread at a time

In computer science, mutual exclusion is a property of concurrency control, which is instituted for the purpose of preventing race conditions. It is the requirement that one thread of execution never enters a critical section while a concurrent thread of execution is already accessing said critical section, which refers to an interval of time during which a thread of execution accesses a shared resource or shared memory.

Reentrancy is a programming concept where a function or subroutine can be interrupted and then resumed before it finishes executing. This means that the function can be called again before it completes its previous execution. Reentrant code is designed to be safe and predictable when multiple instances of the same function are called simultaneously or in quick succession. A computer program or subroutine is called reentrant if multiple invocations can safely run concurrently on multiple processors, or if on a single-processor system its execution can be interrupted and a new execution of it can be safely started. The interruption could be caused by an internal action such as a jump or call, or by an external action such as an interrupt or signal, unlike recursion where new invocations can only be caused by internal call.

In computer science, a semaphore is a variable or abstract data type used to control access to a common resource by multiple threads and avoid critical section problems in a concurrent system such as a multitasking operating system. Semaphores are a type of synchronization primitive. A trivial semaphore is a plain variable that is changed depending on programmer-defined conditions.

In software engineering, double-checked locking is a software design pattern used to reduce the overhead of acquiring a lock by testing the locking criterion before acquiring the lock. Locking occurs only if the locking criterion check indicates that locking is required.

In computer science, a lock or mutex is a synchronization primitive that prevents state from being modified or accessed by multiple threads of execution at once. Locks enforce mutual exclusion concurrency control policies, and with a variety of possible methods there exist multiple unique implementations for different applications.

In software engineering, a spinlock is a lock that causes a thread trying to acquire it to simply wait in a loop ("spin") while repeatedly checking whether the lock is available. Since the thread remains active but is not performing a useful task, the use of such a lock is a kind of busy waiting. Once acquired, spinlocks will usually be held until they are explicitly released, although in some implementations they may be automatically released if the thread being waited on blocks or "goes to sleep".

<span class="mw-page-title-main">Dining philosophers problem</span> Problem used to illustrate synchronization issues and techniques for resolving them

In computer science, the dining philosophers problem is an example problem often used in concurrent algorithm design to illustrate synchronization issues and techniques for resolving them.

<span class="mw-page-title-main">Race condition</span> When a systems behavior depends on timing of uncontrollable events

A race condition or race hazard is the condition of an electronics, software, or other system where the system's substantive behavior is dependent on the sequence or timing of other uncontrollable events, leading to unexpected or inconsistent results. It becomes a bug when one or more of the possible behaviors is undesirable.

In computer science, an algorithm is called non-blocking if failure or suspension of any thread cannot cause failure or suspension of another thread; for some operations, these algorithms provide a useful alternative to traditional blocking implementations. A non-blocking algorithm is lock-free if there is guaranteed system-wide progress, and wait-free if there is also guaranteed per-thread progress. "Non-blocking" was used as a synonym for "lock-free" in the literature until the introduction of obstruction-freedom in 2003.

In concurrent programming, concurrent accesses to shared resources can lead to unexpected or erroneous behavior. Thus, the parts of the program where the shared resource is accessed need to be protected in ways that avoid the concurrent access. One way to do so is known as a critical section or critical region. This protected section cannot be entered by more than one process or thread at a time; others are suspended until the first leaves the critical section. Typically, the critical section accesses a shared resource, such as a data structure, peripheral device, or network connection, that would not operate correctly in the context of multiple concurrent accesses.

In computer science and software engineering, busy-waiting, busy-looping or spinning is a technique in which a process repeatedly checks to see if a condition is true, such as whether keyboard input or a lock is available. Spinning can also be used to generate an arbitrary time delay, a technique that was necessary on systems that lacked a method of waiting a specific length of time. Processor speeds vary greatly from computer to computer, especially as some processors are designed to dynamically adjust speed based on current workload. Consequently, spinning as a time-delay technique can produce inconsistent or even unpredictable results on different systems unless code is included to determine the time a processor takes to execute a "do nothing" loop, or the looping code explicitly checks a real-time clock.

In concurrent programming, a monitor is a synchronization construct that prevents threads from concurrently accessing a shared object's state and allows them to wait for the state to change. They provide a mechanism for threads to temporarily give up exclusive access in order to wait for some condition to be met, before regaining exclusive access and resuming their task. A monitor consists of a mutex (lock) and at least one condition variable. A condition variable is explicitly 'signalled' when the object's state is modified, temporarily passing the mutex to another thread 'waiting' on the condition variable.

In computer science, the reentrant mutex is a particular type of mutual exclusion (mutex) device that may be locked multiple times by the same process/thread, without causing a deadlock.

In computer programming, thread-local storage (TLS) is a memory management method that uses static or global memory local to a thread. The concept allows storage of data that appears to be global in a system with separate threads.

In computer science, the fetch-and-add (FAA) CPU instruction atomically increments the contents of a memory location by a specified value.

In computer science, a readers–writer is a synchronization primitive that solves one of the readers–writers problems. An RW lock allows concurrent access for read-only operations, whereas write operations require exclusive access. This means that multiple threads can read the data in parallel but an exclusive lock is needed for writing or modifying data. When a writer is writing the data, all other writers and readers will be blocked until the writer is finished writing. A common use might be to control access to a data structure in memory that cannot be updated atomically and is invalid until the update is complete.

In computer science, synchronization is the task of coordinating multiple processes to join up or handshake at a certain point, in order to reach an agreement or commit to a certain sequence of action.

The Java programming language and the Java virtual machine (JVM) is designed to support concurrent programming. All execution takes place in the context of threads. Objects and resources can be accessed by many separate threads. Each thread has its own path of execution, but can potentially access any object in the program. The programmer must ensure read and write access to objects is properly coordinated between threads. Thread synchronization ensures that objects are modified by only one thread at a time and prevents threads from accessing partially updated objects during modification by another thread. The Java language has built-in constructs to support this coordination.

In computer science, a concurrent data structure is a data structure designed for access and modification by multiple computing threads on a computer, for example concurrent queues, concurrent stacks etc. The concurrent data structure is typically considered to reside in an abstract storage environment known as shared memory, which may be physically implemented as either a tightly coupled or a distributed collection of storage modules.

The Java programming language's Java Collections Framework version 1.5 and later defines and implements the original regular single-threaded Maps, and also new thread-safe Maps implementing the java.util.concurrent.ConcurrentMap interface among other concurrent interfaces. In Java 1.6, the java.util.NavigableMap interface was added, extending java.util.SortedMap, and the java.util.concurrent.ConcurrentNavigableMap interface was added as a subinterface combination.

References

  1. ↑ Kerrisk, Michael (2010). The Linux Programing Interface. No Starch Press. p. 699, "Chapter 31: THREADS: THREAD SAFETY AND PER-THREAD STORAGE"{{cite book}}: CS1 maint: postscript (link)
  2. 1 2 Oracle (2010-11-01). "Oracle: Thread safety". Docs.oracle.com. Retrieved 2013-10-16"A procedure is thread safe when the procedure is logically correct when executed simultaneously by several threads"; "3 level of thread-safe"{{cite web}}: CS1 maint: postscript (link)
  3. 1 2 "Multithreaded Programming Guide: Chapter 7 Safe and Unsafe Interfaces". Docs Oracle. Oracle. November 2020. Retrieved 2024-04-30; "Thread Safety"{{cite web}}: CS1 maint: postscript (link)
  4. ↑ "API thread safety classifications". IBM. 2023-04-11. Retrieved 2023-10-09.
  5. ↑ "MT Safety Levels for Libraries". Docs Oracle. Retrieved 2024-05-17.
  6. ↑ "Reentrancy and Thread-Safety | Qt 5.6". Qt Project. Retrieved 2016-04-20.
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