Automatic Memory Management in newLISP
Lutz Mueller, 2006-07-25 rev 14
ORO (One Reference Only) automatic memory management developed for newLISP is a fast and resources saving alternative to classic garbage collection algorithms in dynamic, interactive programming languages. This article explains how ORO memory management works.newLISP and any other interactive language system will constantly generate new memory objects during expression evaluation. The new memory objects are intermediate evaluation results, reassigned memory objects, or memory objects whose content was changed. If newLISP did not delete these objects, it would eventually run out of available memory.
In order to understand newLISP's automatic memory management, it is necessary to first review the traditional methods employed by other languages.
Traditional automatic memory management (Garbage Collection)
In most programming languages, a process registers allocated memory, and another process finds and recycles the unused parts of the allocated memory pool. The recycling process can be triggered by some memory allocation limit or can be scheduled to happen between evaluation steps. This form of automatic memory management is called Garbage Collection.
Traditional garbage collection schemes employ one of two algorithms:
(1) The mark and sweep algorithm registers each allocated memory object. A mark phase periodically flags each object in the allocated memory pool. A named object (a variable) directly or indirectly references each memory object in the system. The sweep phase frees the memory of the marked objects when they are no longer in use.
(2) A reference-counting scheme registers each allocated memory object together with a count of references to the object. This reference count gets incremented or decremented during expression evaluation. Whenever an object's reference count reaches zero, the object's allocated memory is freed.
Over time, many elaborate garbage collection schemes have been attempted using these algorithms. The first garbage collection algorithms appeared in LISP. The inventors of the Smalltalk language used more elaborate garbage collection schemes. The history of Smalltalk-80 is an exciting account of the challenges of implementing memory management in an interactive programming language; see [Glenn Krasner, 1983: Smalltalk-80, Bits of History, Words of Advice]. A more recent overview of garbage collection methods can be found in [Richard Jones, Rafael Lins, 1996: Garbage Collection, Algorithms for Automatic Dynamic Memory Management].
One reference only, (ORO) memory management
Memory management in newLISP does not rely on a garbage collection algorithm. Memory is not marked or reference-counted. Instead, a decision whether to delete a newly created memory object is made right after the memory object is created.
Empirical studies of LISP have shown that most LISP cells are not shared and so can be reclaimed during the evaluation process. Aside from some optimizations for primitives like set, define, and eval, newLISP deletes memory objects containing intermediate evaluation results once it reaches a higher evaluation level. newLISP does this by pushing a reference to each created memory object onto a result stack. When newLISP reaches a higher evaluation level, it removes the last evaluation result's reference from the result stack and deletes the evaluation result's memory object. This should not be confused with one-bit reference counting. ORO memory management does not set bits to mark objects as sticky.
newLISP follows a one reference only (ORO) rule. Every memory object not referenced by a symbol or context reference is obsolete once newLISP reaches a higher evaluation level during expression evaluation. Objects in newLISP (excluding symbols and contexts) are passed by value to other functions. As a result, each newLISP object only requires one reference.
newLISP's ORO rule has advantages. It simplifies not only memory management but also other aspects of the newLISP language. For example, while users of traditional LISP have to distinguish between equality of copied memory objects and equality of references to memory objects, newLISP users do not.
newLISP's ORO rule also has disadvantages. It forces newLISP to constantly allocate and then free LISP cells. newLISP optimizes this process by allocating large chunks of cell memory from the host operating system. newLISP will request LISP cells from a free cell list and then recycle those cells back into that list. As a result, only a few CPU instructions (pointer assignments) are needed to unlink a free cell or to re-insert a deleted cell.
The overall effect of ORO memory management is a faster evaluation time and a smaller memory and disk footprint than traditional interpreted LISP's can offer. The lack of garbage collection in newLISP more than compensates for its high frequency of cell creation/deletion. Note that under error conditions, newLISP will employ a mark and sweep algorithm to free un-referenced cells.
Performance considerations with value-passing
Passing parameters by value (memory copying) instead of by reference poses a potential disadvantage when dealing with large lists. For practical purposes, however, the overhead needed to copy a large list is negligible compared to the processing done on the list. Nevertheless, to achieve maximum performance, newLISP offers a group of destructive functions that can efficiently create and modify large lists. While
cons and set-nth return a new memory object of the changed list, push, pop and nth-set change the existing list and only return a copy of the list elements that they added or removed. In order for any function to operate destructively on a large list, the large list must be passed by reference. If a list is packaged in a context (a namespace) in newLISP, then newLISP can pass the list by reference. newLISP contexts are the best choice when passing big lists or string buffers by reference. In general, the speed of ORO memory management more than compensates for the overhead required to pass parameters by value.
Memory and datatypes in newLISP
The memory objects of newLISP strings are allocated from and freed to the host's OS whenever newLISP recycles the cells from its allocated chunks of cell memory. This means that newLISP handles cell memory more efficiently than string memory. As a result, it is often better to use symbols than strings for efficient text processing. For example, when handling natural language it is more efficient to handle natural language words as individual symbols in a separated name-space, rather than as a single string. The 'spam-filter' program in the newLISP source distribution uses this method. newLISP can handle millions of symbols without degrading performance.
Programmers coming from other programming languages frequently overlook that symbols in LISP can act as more than just variables or object references. The symbol is a useful data type in itself, which in many cases can replace the string data type.
Integer numbers and double floating-point numbers are stored directly in newLISP's LISP cells and do not need a separate memory allocation cycle.
For efficiency during matrix operations like matrix multiplication or inversion, newLISP allocates non-cell memory objects for matrices, converts the results to LISP cells, and then frees the matrix memory objects.
newLISP allocates an array as a group of LISP cells. The LISP cells are allocated linearly. As a result, array indices have faster random access to the LISP cells. Only a subset of newLISP list functions can be used on arrays. Automatic memory management in newLISP handles arrays in a manner similar to how it handles lists.
References
- Glenn Krasner, 1983: Smalltalk-80, Bits of History, Words of Advice
Addison Wesley Publishing Company
- Richard Jones, Rafael Lins, 1996: Garbage Collection, Algorithms for Automatic Dynamic Memory Management
John Wiley & Sons
Copyright © 2004-2006, Lutz Mueller http://newlisp.org. All rights reserved.