Description
Many programming languages use manual techniques to determine when to ''allocate'' a new object from the free store. C uses themalloc
C dynamic memory allocation refers to performing manual memory management for dynamic memory allocation in the C programming language via a group of functions in the C standard library, namely , , , and .
The C++ programming language includes t ...
function; C++ and Java use the new
New is an adjective referring to something recently made, discovered, or created.
New or NEW may refer to:
Music
* New, singer of K-pop group The Boyz
Albums and EPs
* ''New'' (album), by Paul McCartney, 2013
* ''New'' (EP), by Regurgitator, ...
operator; and many other languages (such as Python) allocate all objects from the free store. Determining when an object ought to be created ( object creation) is generally trivial and unproblematic, though techniques such as free()
in C, or the delete
operator in C++ – this contrasts with automatic destruction of objects held in automatic variables, notably (non-static) local variables of functions, which are destroyed at the end of their scope in C and C++.
Manual memory management techniques
For example * malloc/free * Memory arena * scratch buffer * ...Manual management and correctness
Manual memory management is known to enable several major classes of bugs into a program when used incorrectly, notably violations of memory safety or memory leaks. These are a significant source of security bugs. * When an unused object is never released back to the free store, this is known as a memory leak. In some cases, memory leaks may be tolerable, such as a program which "leaks" a bounded amount of memory over its lifetime, or a short-running program which relies on an operating system to deallocate its resources when it terminates. However, in many cases memory leaks occur in long-running programs, and in such cases an ''unbounded'' amount of memory is leaked. When this occurs, the size of the available free store continues to decrease over time; when it is finally exhausted, the program then crashes. * Catastrophic failure of the dynamic memory management system may result when an object's backing memory is deleted out from under it more than once; an object is explicitly destroyed more than once; when, while using a pointer to manipulate an object ''not'' allocated on the free store, a programmer attempts to release said pointer's target object's backing memory; or when, while manipulating an object via a pointer to another, arbitrary area of memory managed by an unknown external task, thread, or process, a programmer corrupts that object's state, possibly in such a way as to write outside of its bounds and corrupt its memory management data. The result of such actions can include heap corruption, premature destruction of a ''different'' (and newly created) object which happens to occupy the same location in memory as the multiply deleted object, program crashes due to a segmentation fault (violation of memory protection,) and other forms of undefined behavior. * Pointers to deleted objects become wild pointers if used post-deletion; attempting to use such pointers can result in difficult-to-diagnose bugs. Languages which exclusively useResource Acquisition Is Initialization
Manual memory management has one correctness advantage, which is that it allows automaticshared_ptr
In computer science, a smart pointer is an abstract data type that simulates a pointer while providing added features, such as automatic memory management or bounds checking. Such features are intended to reduce bugs caused by the misuse of poin ...
template in the language's standard library to perform memory management is a common paradigm. shared_ptr
is ''not'' suitable for all object usage patterns, however.
This approach is not usable in most garbage collected languages – notably tracing garbage collectors or more advanced reference counting – due to finalization being non-deterministic, and sometimes not occurring at all. That is, it is difficult to define (or determine) when or if a finalizer method might be called; this is commonly known as the finalizer problem. Java and other GC'd languages frequently use manual management for scarce system resources ''besides'' memory via the dispose pattern: any object which manages resources is expected to implement the dispose()
method, which releases any such resources and marks the object as inactive. Programmers are expected to invoke dispose()
manually as appropriate to prevent "leaking" of scarce graphics resources. Depending on the finalize()
method (how Java implements finalizers) to release graphics resources is widely viewed as poor programming practice among Java programmers, and similarly the analogous __del__()
method in Python cannot be relied on for releasing resources. For stack resources (resources acquired and released within a single block of code), this can be automated by various language constructs, such as Python's with
, C#'s using
or Java's try
-with-resources.
Performance
Many advocates of manual memory management argue that it affords superior performance when compared to automatic techniques such asdelete
and such incur an overhead each time they are made, this overhead can be amortized in garbage collection cycles. This is especially true of multithreaded applications, where delete calls must be synchronized.
* The allocation routine may be more complicated, and slower. Some garbage collection schemes, such as those with heap compaction, can maintain the free store as a simple array of memory (as opposed to the complicated implementations required by manual management schemes).
Latency is a debated point that has changed over time, with early garbage collectors and simple implementations performing very poorly compared to manual memory management, but sophisticated modern garbage collectors often performing as well or better than manual memory management.
Manual allocation does not suffer from the long "pause" times that occur in simple stop-the-world garbage collection, although modern garbage collectors have collection cycles which are often not noticeable.
Manual memory management and garbage collection both suffer from potentially unbounded deallocation times – manual memory management because deallocating a single object may require deallocating its members, and recursively its members' members, etc., while garbage collection may have long collection cycles. This is especially an issue in real time systems, where unbounded collection cycles are generally unacceptable; real-time garbage collection is possible by pausing the garbage collector, while real-time manual memory management requires avoiding large deallocations, or manually pausing deallocation.
References
*External links