The online bibliographic database includes nearly 3,000 garbage collection-related publications. It contains abstracts for some entries and URLs or DOIs for most of the electronically available ones, and is continually being updated. The database can be searched online or downloaded as BibTeX, PostScript, or PDF.
In computer science, garbage collection (GC) is a form of automatic memory management. The garbage collector attempts to reclaim memory which was allocated by the program, but is no longer referenced; such memory is called garbage. Garbage collection was invented by American computer scientist John McCarthy around 1959 to simplify manual memory management in Lisp.[2]
Garbage collection relieves the programmer from doing manual memory management, where the programmer specifies what objects to de-allocate and return to the memory system and when to do so.[3] Other, similar techniques include stack allocation, region inference, and memory ownership, and combinations thereof. Garbage collection may take a significant proportion of a program's total processing time, and affect performance as a result.
Resources other than memory, such as network sockets, database handles, windows, file descriptors, and device descriptors, are not typically handled by garbage collection, but rather by other methods (e.g. destructors). Some such methods de-allocate memory as well.
Many programming languages require garbage collection, either as part of the language specification (e.g., RPL, Java, C#, D,[4] Go, and most scripting languages) or effectively for practical implementation (e.g., formal languages like lambda calculus). These are said to be garbage-collected languages. Other languages, such as C and C++, were designed for use with manual memory management, but have garbage-collected implementations available. Some languages, like Ada, Modula-3, and C++/CLI, allow both garbage collection and manual memory management to co-exist in the same application by using separate heaps for collected and manually managed objects. Still others, like D, are garbage-collected but allow the user to manually delete objects or even disable garbage collection entirely when speed is required.
GC uses computing resources to decide which memory to free. Therefore, the penalty for the convenience of not annotating object lifetime manually in the source code is overhead, which can impair program performance.[6] A peer-reviewed paper from 2005 concluded that GC needs five times the memory to compensate for this overhead and to perform as fast as the same program using idealised explicit memory management. The comparison however is made to a program generated by inserting deallocation calls using an oracle, implemented by collecting traces from programs run under a profiler, and the program is only correct for one particular execution of the program.[7] Interaction with memory hierarchy effects can make this overhead intolerable in circumstances that are hard to predict or to detect in routine testing. The impact on performance was given by Apple as a reason for not adopting garbage collection in iOS, despite it being the most desired feature.[8]
The moment when the garbage is actually collected can be unpredictable, resulting in stalls (pauses to shift/free memory) scattered throughout a session. Unpredictable stalls can be unacceptable in real-time environments, in transaction processing, or in interactive programs. Incremental, concurrent, and real-time garbage collectors address these problems, with varying trade-offs.
Tracing garbage collection is the most common type of garbage collection, so much so that "garbage collection" often refers to tracing garbage collection, rather than other methods such as reference counting. The overall strategy consists of determining which objects should be garbage collected by tracing which objects are reachable by a chain of references from certain root objects, and considering the rest as garbage and collecting them. However, there are a large number of algorithms used in implementation, with widely varying complexity and performance characteristics.
Reference counting garbage collection is where each object has a count of the number of references to it. Garbage is identified by having a reference count of zero. An object's reference count is incremented when a reference to it is created, and decremented when a reference is destroyed. When the count reaches zero, the object's memory is reclaimed.[9]
As with manual memory management, and unlike tracing garbage collection, reference counting guarantees that objects are destroyed as soon as their last reference is destroyed, and usually only accesses memory which is either in CPU caches, in objects to be freed, or directly pointed to by those, and thus tends to not have significant negative side effects on CPU cache and virtual memory operation.
Escape analysis is a compile-time technique that can convert heap allocations to stack allocations, thereby reducing the amount of garbage collection to be done. This analysis determines whether an object allocated inside a function is accessible outside of it. If a function-local allocation is found to be accessible to another function or thread, the allocation is said to "escape" and cannot be done on the stack. Otherwise, the object may be allocated directly on the stack and released when the function returns, bypassing the heap and associated memory management costs.[16]
Generally speaking, higher-level programming languages are more likely to have garbage collection as a standard feature. In some languages that do not have built in garbage collection, it can be added through a library, as with the Boehm garbage collector for C and C++.
Most functional programming languages, such as ML, Haskell, and APL, have garbage collection built in. Lisp is especially notable as both the first functional programming language and the first language to introduce garbage collection.[17]
Other dynamic languages, such as Ruby and Julia (but not Perl 5 or PHP before version 5.3,[18] which both use reference counting), JavaScript and ECMAScript also tend to use GC. Object-oriented programming languages such as Smalltalk, RPL and Java usually provide integrated garbage collection. Notable exceptions are C++ and Delphi, which have destructors.
BASIC and Logo have often used garbage collection for variable-length data types, such as strings and lists, so as not to burden programmers with memory management details. On the Altair 8800, programs with many string variables and little string space could cause long pauses due to garbage collection.[19] Similarly the Applesoft BASIC interpreter's garbage collection algorithm repeatedly scans the string descriptors for the string having the highest address in order to compact it toward high memory, resulting in O ( n 2 ) \displaystyle O(n^2) performance[20] and pauses anywhere from a few seconds to a few minutes.[21] A replacement garbage collector for Applesoft BASIC by Randy Wigginton identifies a group of strings in every pass over the heap, reducing collection time dramatically.[22] BASIC.System, released with ProDOS in 1983, provides a windowing garbage collector for BASIC that is many times faster.[23]
While the Objective-C traditionally had no garbage collection, with the release of OS X 10.5 in 2007 Apple introduced garbage collection for Objective-C 2.0, using an in-house developed runtime collector.[24]However, with the 2012 release of OS X 10.8, garbage collection was deprecated in favor of LLVM's automatic reference counter (ARC) that was introduced with OS X 10.7.[25] Furthermore, since May 2015 Apple even forbids the usage of garbage collection for new OS X applications in the App Store.[26][27] For iOS, garbage collection has never been introduced due to problems in application responsivity and performance;[8][28] instead, iOS uses ARC.[29][30]
Garbage collection is rarely used on embedded or real-time systems because of the usual need for very tight control over the use of limited resources. However, garbage collectors compatible with many limited environments have been developed.[31] The Microsoft .NET Micro Framework, .NET nanoFramework[32] and Java Platform, Micro Edition are embedded software platforms that, like their larger cousins, include garbage collection.
This form of garbage collection has been studied in the Mercury programming language,[34] and it saw greater usage with the introduction of LLVM's automatic reference counter (ARC) into Apple's ecosystem (iOS and OS X) in 2011.[29][30][26]
In Baker's algorithm, the allocation is done in either half of a single region of memory. When it becomes half full, a garbage collection is performed which moves the live objects into the other half and the remaining objects are implicitly deallocated. The running program (the 'mutator') has to check that any object it references is in the correct half, and if not move it across, while a background task is finding all of the objects.[38]
Generational garbage collection schemes are based on the empirical observation that most objects die young. In generational garbage collection two or more allocation regions (generations) are kept, which are kept separate based on object's age. New objects are created in the "young" generation that is regularly collected, and when a generation is full, the objects that are still referenced from older regions are copied into the next oldest generation. Occasionally a full scan is performed.
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