In section 5.4, we will show how to implement a Scheme evaluator as a register machine. In order to simplify the discussion, we will assume that our register machines can be equipped with a list-structured memory, in which the basic operations for manipulating list-structured data are primitive. Postulating the existence of such a memory is a useful abstraction when one is focusing on the mechanisms of control in a Scheme interpreter, but this does not reflect a realistic view of the actual primitive data operations of contemporary computers. To obtain a more complete picture of how a Lisp system operates, we must investigate how list structure can be represented in a way that is compatible with conventional computer memories.
There are 2 considerations in implementing list structure. The
first is purely an issue of representation: how to represent the
box-and-pointer
structure of Lisp pairs, using only the storage
and addressing capabilities of typical computer memories. The 2nd
issue concerns the management of memory as a computation proceeds.
The operation of a Lisp system depends crucially on the ability to
continually create new data objects. These include objects that are
explicitly created by the Lisp procedures being interpreted as well
as structures created by the interpreter itself, such as environments
and argument lists. Although the constant creation of new data
objects would pose no problem on a computer with an infinite amount of
rapidly addressable memory, computer memories are available only in
finite sizes (more's the pity). Lisp systems
thus provide an automatic storage allocation facility to
support the illusion of an infinite memory. When a data object is no
longer needed, the memory allocated to it is automatically recycled
and used to construct new data objects. There are various
techniques for providing such automatic storage allocation. The
method we shall discuss in this section is called garbage
collection.
A conventional computer memory can be thought of as an array of cubbyholes, each of which can contain a piece of information. Each cubbyhole has a unique name, called its address or location. Typical memory systems provide 2 primitive operations: one that fetches the data stored in a specified location and one that assigns new data to a specified location. Memory addresses can be incremented to support sequential access to some set of the cubbyholes. More generally, many important data operations require that memory addresses be treated as data, which can be stored in memory locations and manipulated in machine registers. The representation of list structure is one application of such address arithmetic.
To model computer memory, we use a new kind of data structure called a vector. Abstractly, a vector is a compound data object whose individual elements can be accessed by means of an integer index in an amount of time that is independent of the index.5 In order to describe memory operations, we use 2 primitive Scheme procedures for manipulating vectors:
(vector-ref <vector> <n>)
returns the nth
element of the vector.
(vector-set! <vector> <n> <value>)
sets
the nth element of the vector to the designated value.
For example, if v
is a vector, then (vector-ref v 5)
gets
the fifth entry in the vector v
and (vector-set! v 5 7)
changes the value of the fifth entry of the vector v
to 7.6
For computer memory, this access can be implemented
through the use of address arithmetic to combine a base address
that specifies the beginning location of a vector in memory with an
index that specifies the offset of a particular element of the vector.
We can use vectors to implement the basic pair structures required for
a list-structured memory. Let us imagine that computer memory is
divided into 2 vectors: the-cars
and the-cdrs
. We will
represent list structure as follows: A pointer to a pair is an index
into the 2 vectors. The car
of the pair is the entry in the-cars
with the designated index, and the cdr
of the pair is
the entry in the-cdrs
with the designated index. We also need a
representation for objects other than pairs (such as numbers and
symbols) and a way to distinguish one kind of data from another.
There are many methods of accomplishing this, but they all reduce to
using typed pointers, that is, to extending the notion of
pointer
to include information on data type.7 The data type enables the system to
distinguish a pointer to a pair (which consists of the pair
data
type and an index into the memory vectors) from pointers to other
kinds of data (which consist of some other data type and whatever is
being used to represent data of that type). Two data objects are
considered to be the same (eq?
) if their pointers are
identical.8 Figure 5.14
illustrates the use of this method to represent the list ((1 2) 3
4)
, whose box-and-pointer diagram is also shown. We use letter
prefixes to denote the data-type information. Thus, a pointer to the
pair with index 5 is denoted p5
, the empty list is denoted by
the pointer e0
, and a pointer to the number 4 is denoted n4
. In the box-and-pointer diagram, we have indicated at the lower
left of each pair the vector index that specifies where the car
and cdr
of the pair are stored. The blank locations in the-cars
and the-cdrs
may contain parts of other list
structures (not of interest here).
![]() |
A pointer to a number, such as n4
,
might consist of a type indicating numeric data together with the
actual representation of the number 4.9
To deal with numbers that are too large to
be represented in the fixed amount of space allocated for a single
pointer, we could use a distinct bignum data type, for which the
pointer designates a list in which the parts of the number are
stored.10
A symbol might be represented as a typed pointer that designates a
sequence of the characters that form the symbol's printed representation.
This sequence is constructed by the Lisp reader when the character string
is initially encountered in input. Since we want 2 instances of a
symbol to be recognized as the same
symbol by eq?
and we
want eq?
to be a simple test for equality of pointers, we must
ensure that if the reader sees the same character string twice, it
will use the same pointer (to the same sequence of characters) to
represent both occurrences. To accomplish this, the reader maintains
a table, traditionally called the obarray, of all the symbols it
has ever encountered. When the reader encounters a character string
and is about to construct a symbol, it checks the obarray to see if it
has ever before seen the same character string. If it has not, it
uses the characters to construct a new symbol (a typed pointer to a
new character sequence) and enters this pointer in the obarray. If the
reader has seen the string before, it returns the symbol pointer
stored in the obarray. This process of replacing character strings by
unique pointers is called interning symbols.
Given the above representation scheme, we can replace each
primitive
list operation of a register machine with one or more
primitive vector operations. We will use 2 registers, the-cars
and the-cdrs
, to identify the memory vectors, and will
assume that vector-ref
and vector-set!
are available as
primitive operations. We also assume that numeric operations on
pointers (such as incrementing a pointer, using a pair pointer to
index a vector, or adding 2 numbers) use only the index portion of
the typed pointer.
For example, we can make a register machine support the instructions
(assign <reg1> (op car) (reg <reg2>))
(assign <reg1> (op cdr) (reg <reg2>))
if we implement these, respectively, as
(assign <reg1> (op vector-ref) (reg the-cars) (reg <reg2>))
(assign <reg1> (op vector-ref) (reg the-cdrs) (reg <reg2>))
The instructions
(perform (op set-car!) (reg <reg1>) (reg <reg2>))
(perform (op set-cdr!) (reg <reg1>) (reg <reg2>))
are implemented as
(perform
(op vector-set!) (reg the-cars) (reg <reg1>) (reg <reg2>))
(perform
(op vector-set!) (reg the-cdrs) (reg <reg1>) (reg <reg2>))
Cons
is performed by allocating an unused index and storing the
arguments to cons
in the-cars
and the-cdrs
at that
indexed vector position. We presume that there is a special register,
free
, that always holds a pair pointer containing the next
available index, and that we can increment the index part of that
pointer to find the next free location.11
For example, the instruction
(assign <reg1> (op cons) (reg <reg2>) (reg <reg3>))
is implemented as the following sequence of vector operations:12
(perform
(op vector-set!) (reg the-cars) (reg free) (reg <reg2>))
(perform
(op vector-set!) (reg the-cdrs) (reg free) (reg <reg3>))
(assign <reg1> (reg free))
(assign free (op +) (reg free) (const 1))
The eq?
operation
(op eq?) (reg <reg1>) (reg <reg2>)
simply tests the equality of all fields in the registers, and
predicates such as pair?
, null?
, symbol?
, and number?
need only check the type field.
Although our register machines use stacks, we need do nothing special
here, since stacks can be modeled in terms of lists. The stack can be
a list of the saved values, pointed to by a special register the-stack
. Thus, (save <reg>)
can be implemented as
(assign the-stack (op cons) (reg <reg>) (reg the-stack))
Similarly, (restore <reg>)
can be implemented as
(assign <reg> (op car) (reg the-stack))
(assign the-stack (op cdr) (reg the-stack))
and (perform (op initialize-stack))
can be implemented as
(assign the-stack (const ()))
These operations can be further expanded in terms of the vector operations given above. In conventional computer architectures, however, it is usually advantageous to allocate the stack as a separate vector. Then pushing and popping the stack can be accomplished by incrementing or decrementing an index into that vector.
Exercise 5.20. Draw the box-and-pointer representation and the memory-vector representation (as in figure 5.14) of the list structure produced by
(define x (cons 1 2))
(define y (list x x))
with the free
pointer initially p1
. What is the final
value of free
? What pointers represent the values of x
and y
?
Exercise 5.21. Implement register machines for the following procedures. Assume that the list-structure memory operations are available as machine primitives.
a. Recursive count-leaves
:
(define (count-leaves tree)
(cond ((null? tree) 0)
((not (pair? tree)) 1)
(else (+ (count-leaves (car tree))
(count-leaves (cdr tree))))))
b. Recursive count-leaves
with explicit counter:
(define (count-leaves tree)
(define (count-iter tree n)
(cond ((null? tree) n)
((not (pair? tree)) (+ n 1))
(else (count-iter (cdr tree)
(count-iter (car tree) n)))))
(count-iter tree 0))
Exercise 5.22. Exercise 3.12 of section 3.3.1
presented an append
procedure that appends 2 lists to form a
new list and an append!
procedure that splices 2 lists
together. Design a register machine to implement each of these
procedures. Assume that the list-structure memory operations are
available as primitive operations.
The representation method outlined in section 5.3.1 solves the problem of implementing list structure, provided that we have an infinite amount of memory. With a real computer we will eventually run out of free space in which to construct new pairs.13 However, most of the pairs generated in a typical computation are used only to hold intermediate results. After these results are accessed, the pairs are no longer needed -- they are garbage. For instance, the computation
(accumulate + 0 (filter odd? (enumerate-interval 0 n)))
constructs 2 lists: the enumeration and the result of filtering the enumeration. When the accumulation is complete, these lists are no longer needed, and the allocated memory can be reclaimed. If we can arrange to collect all the garbage periodically, and if this turns out to recycle memory at about the same rate at which we construct new pairs, we will have preserved the illusion that there is an infinite amount of memory.
In order to recycle pairs, we must have a way to determine which
allocated pairs are not needed (in the sense that their contents can
no longer influence the future of the computation). The method we
shall examine for accomplishing this is known as garbage
collection. Garbage collection is based on the observation that, at
any moment in a Lisp interpretation, the only objects that can
affect the future of the computation are those that can be reached by
some succession of car
and cdr
operations starting from
the pointers that are currently in the machine registers.14 Any memory cell
that is not so accessible may be recycled.
There are many ways to perform garbage collection. The method we
shall examine here is called stop-and-copy. The basic idea is
to divide memory into 2 halves: working memory
and free
memory.
When cons
constructs pairs, it allocates these in
working memory. When working memory is full, we perform garbage
collection by locating all the useful pairs in working memory and
copying these into consecutive locations in free memory. (The useful
pairs are located by tracing all the car
and cdr
pointers,
starting with the machine registers.) Since we do not copy the
garbage, there will presumably be additional free memory that we can
use to allocate new pairs. In addition, nothing in the working memory
is needed, since all the useful pairs in it have been copied. Thus,
if we interchange the roles of working memory and free memory, we can
continue processing; new pairs will be allocated in the new working
memory (which was the old free memory). When this is full, we can
copy the useful pairs into the new free memory (which was the old
working memory).15
We now use our register-machine language to describe the stop-and-copy
algorithm in more detail. We will assume that there is a register
called root
that contains a pointer to a structure that
eventually points at all accessible data. This can be arranged by
storing the contents of all the machine registers in a
pre-allocated list pointed at by root
just before starting
garbage collection.16 We also assume that, in addition to the
current working memory, there is free memory available into which we
can copy the useful data. The current working memory consists of
vectors whose base addresses are in registers called the-cars
and the-cdrs
, and the free memory is in registers called new-cars
and new-cdrs
.
Garbage collection is triggered when we exhaust the free cells in the
current working memory, that is, when a cons
operation attempts
to increment the free
pointer beyond the end of the memory
vector. When the garbage-collection process is complete, the root
pointer will point into the new memory, all objects accessible
from the root
will have been moved to the new memory, and the
free
pointer will indicate the next place in the new memory
where a new pair can be allocated. In addition, the roles of working
memory and new memory will have been interchanged -- new pairs will be
constructed in the new memory, beginning at the place indicated by
free
, and the (previous) working memory will be available as the
new memory for the next garbage collection.
Figure 5.15 shows the arrangement of memory just
before and just after garbage collection.
![]() |
The state of the garbage-collection process is controlled by
maintaining 2 pointers: free
and scan
. These are
initialized to point to the beginning of the new memory. The
algorithm begins by relocating the pair pointed at by root
to
the beginning of the new memory. The pair is copied, the root
pointer is adjusted to point to the new location, and the free
pointer is incremented. In addition, the old location of the pair is
marked to show that its contents have been moved. This marking is
done as follows: In the car
position, we place a special tag
that signals that this is an already-moved object. (Such an object is
traditionally called a broken heart.)17 In the cdr
position we place a forwarding
address that points at the location to which the object has been
moved.
After relocating the root, the garbage collector enters its basic
cycle. At each step in the algorithm, the scan
pointer
(initially pointing at the relocated root) points at a pair that has
been moved to the new memory but whose car
and cdr
pointers still refer to objects in the old memory. These objects are
each relocated, and the scan
pointer is incremented. To
relocate an object (for example, the object indicated by the car
pointer of the pair we are scanning) we check to see if the object has
already been moved (as indicated by the presence of a broken-heart tag
in the car
position of the object). If the object has not
already been moved, we copy it to the place indicated by free
,
update free
, set up a broken heart at the object's old location,
and update the pointer to the object (in this
example, the car
pointer of the pair we are scanning) to point
to the new location. If the object has already been moved, its
forwarding address (found in the cdr
position of the broken
heart) is substituted for the pointer in the pair being scanned.
Eventually, all accessible objects will have been moved and scanned,
at which point the scan
pointer will overtake the free
pointer and the process will terminate.
We can specify the stop-and-copy algorithm as a sequence of
instructions for a register
machine. The basic step of relocating an object is accomplished by a
subroutine called relocate-old-result-in-new
. This
subroutine gets its argument, a pointer to the object to be relocated,
from a register named old
. It relocates the designated object
(incrementing free
in the process),
puts a pointer to the relocated object into a register called new
, and returns by branching to the entry point stored in the register
relocate-continue
. To begin garbage collection, we invoke this
subroutine to relocate the root
pointer, after initializing free
and scan
. When the relocation of root
has been
accomplished, we install the new pointer as the new root
and
enter the main loop of the garbage collector.
begin-garbage-collection
(assign free (const 0))
(assign scan (const 0))
(assign old (reg root))
(assign relocate-continue (label reassign-root))
(goto (label relocate-old-result-in-new))
reassign-root
(assign root (reg new))
(goto (label gc-loop))
In the main loop of the garbage collector we must determine whether
there are any more objects to be scanned. We do this by testing
whether the scan
pointer is coincident with the free
pointer. If the pointers are equal, then all accessible objects have
been relocated, and we branch to gc-flip
, which cleans things up
so that we can continue the interrupted computation. If there are
still pairs to be scanned, we call the relocate subroutine to relocate
the car
of the next pair (by placing the car
pointer in old
). The relocate-continue
register is set up so that the
subroutine will return to update the car
pointer.
gc-loop
(test (op =) (reg scan) (reg free))
(branch (label gc-flip))
(assign old (op vector-ref) (reg new-cars) (reg scan))
(assign relocate-continue (label update-car))
(goto (label relocate-old-result-in-new))
At update-car
, we modify the car
pointer of the pair being
scanned, then proceed to relocate the cdr
of the pair. We
return to update-cdr
when that relocation has been accomplished.
After relocating and updating the cdr
, we are finished scanning
that pair, so we continue with the main loop.
update-car
(perform
(op vector-set!) (reg new-cars) (reg scan) (reg new))
(assign old (op vector-ref) (reg new-cdrs) (reg scan))
(assign relocate-continue (label update-cdr))
(goto (label relocate-old-result-in-new))
update-cdr
(perform
(op vector-set!) (reg new-cdrs) (reg scan) (reg new))
(assign scan (op +) (reg scan) (const 1))
(goto (label gc-loop))
The subroutine relocate-old-result-in-new
relocates objects as
follows: If the object to be relocated (pointed at by old
) is
not a pair, then we return the same pointer to the object unchanged
(in new
). (For example, we may be scanning a pair whose car
is the number 4. If we represent the car
by n4
, as
described in section 5.3.1, then we want the
relocated
car
pointer to still be n4
.) Otherwise, we
must perform the relocation. If the car
position of the pair to
be relocated contains a broken-heart tag, then the pair has in fact
already been moved, so we retrieve the forwarding address (from the
cdr
position of the broken heart) and return this in new
.
If the pointer in old
points at a yet-unmoved pair, then we move
the pair to the first free cell in new memory (pointed at by free
) and set up the broken heart by storing a broken-heart tag and
forwarding address at the old location.
Relocate-old-result-in-new
uses a register oldcr
to hold the car
or the cdr
of the object pointed at by
old
.18
relocate-old-result-in-new
(test (op pointer-to-pair?) (reg old))
(branch (label pair))
(assign new (reg old))
(goto (reg relocate-continue))
pair
(assign oldcr (op vector-ref) (reg the-cars) (reg old))
(test (op broken-heart?) (reg oldcr))
(branch (label already-moved))
(assign new (reg free)) ; new location for pair
;; Update
free
pointer.
(assign free (op +) (reg free) (const 1))
;; Copy the car
and cdr
to new memory.
(perform (op vector-set!)
(reg new-cars) (reg new) (reg oldcr))
(assign oldcr (op vector-ref) (reg the-cdrs) (reg old))
(perform (op vector-set!)
(reg new-cdrs) (reg new) (reg oldcr))
;; Construct the broken heart.
(perform (op vector-set!)
(reg the-cars) (reg old) (const broken-heart))
(perform
(op vector-set!) (reg the-cdrs) (reg old) (reg new))
(goto (reg relocate-continue))
already-moved
(assign new (op vector-ref) (reg the-cdrs) (reg old))
(goto (reg relocate-continue))
At the very end of the garbage-collection process, we interchange the
role of old and new memories by interchanging pointers: interchanging
the-cars
with new-cars
, and the-cdrs
with new-cdrs
. We will then be ready to perform another garbage
collection the next time memory runs out.
gc-flip
(assign temp (reg the-cdrs))
(assign the-cdrs (reg new-cdrs))
(assign new-cdrs (reg temp))
(assign temp (reg the-cars))
(assign the-cars (reg new-cars))
(assign new-cars (reg temp))
5 We could represent memory as lists of items.
However, the access time would then not be independent of the index,
since accessing the nth element of a list requires n - 1 cdr
operations.
6 For completeness, we should specify a make-vector
operation that constructs vectors. However, in the present
application we will use vectors only to model fixed divisions of the
computer memory.
7 This is
precisely the same tagged data
idea we introduced in chapter 2 for
dealing with generic operations. Here, however, the data types are
included at the primitive machine level rather than constructed
through the use of lists.
8 Type information may be encoded in a variety of ways, depending on the details of the machine on which the Lisp system is to be implemented. The execution efficiency of Lisp programs will be strongly dependent on how cleverly this choice is made, but it is difficult to formulate general design rules for good choices. The most straightforward way to implement typed pointers is to allocate a fixed set of bits in each pointer to be a type field that encodes the data type. Important questions to be addressed in designing such a representation include the following: How many type bits are required? How large must the vector indices be? How efficiently can the primitive machine instructions be used to manipulate the type fields of pointers? Machines that include special hardware for the efficient handling of type fields are said to have tagged architectures.
9 This decision on the
representation of numbers determines whether eq?
, which tests
equality of pointers, can be used to test for equality of numbers. If
the pointer contains the number itself, then equal numbers will have
the same pointer. But if the pointer contains the index of a location
where the number is stored, equal numbers will be guaranteed to have
equal pointers only if we are careful never to store the same number
in more than one location.
10 This is just like writing a number as a sequence of
digits, except that each digit
is a number between 0 and the
largest number that can be stored in a single pointer.
11 There are other ways of finding free storage. For example, we could link together all the unused pairs into a free list. Our free locations are consecutive (and hence can be accessed by incrementing a pointer) because we are using a compacting garbage collector, as we will see in section 5.3.2.
12 This is essentially the implementation of cons
in terms of set-car!
and set-cdr!
, as described in
section 3.3.1. The operation get-new-pair
used in that implementation is realized here by the free
pointer.
13 This may not be true eventually,
because memories may get large enough so that it would be impossible
to run out of free memory in the lifetime of the computer. For
example, there are about 3× 1013, microseconds in a year, so
if we were to cons
once per microsecond we would need about
1015 cells of memory to build a machine that could operate for 30
years without running out of memory. That much memory seems absurdly
large by today's standards, but it is not physically impossible. On
the other hand, processors are getting faster and a future computer
may have large numbers of processors operating in parallel on a single
memory, so it may be possible to use up memory much faster than we
have postulated.
14 We assume here that the stack is represented as a list as described in section 5.3.1, so that items on the stack are accessible via the pointer in the stack register.
15 This idea was invented and first implemented
by Minsky, as part of the implementation of Lisp for the PDP-1 at the
MIT Research Laboratory of Electronics. It was further developed by
Fenichel and Yochelson (1969) for use in the Lisp implementation for
the Multics time-sharing system. Later, Baker (1978) developed a
real-time
version of the method, which does not require the
computation to stop during garbage collection. Baker's idea was
extended by Hewitt, Lieberman, and Moon (see Lieberman and Hewitt
1983) to take advantage of the fact that some structure is more volatile
and other structure is more permanent.
An alternative commonly used garbage-collection technique is the mark-sweep method. This consists of tracing all the structure
accessible from the machine registers and marking each pair we reach.
We then scan all of memory, and any location that is unmarked is
swept up
as garbage and made available for reuse. A full
discussion of the mark-sweep method can be found in Allen 1978.
The Minsky-Fenichel-Yochelson algorithm is the dominant algorithm in use for large-memory systems because it examines only the useful part of memory. This is in contrast to mark-sweep, in which the sweep phase must check all of memory. A 2nd advantage of stop-and-copy is that it is a compacting garbage collector. That is, at the end of the garbage-collection phase the useful data will have been moved to consecutive memory locations, with all garbage pairs compressed out. This can be an extremely important performance consideration in machines with virtual memory, in which accesses to widely separated memory addresses may require extra paging operations.
16 This list of registers does not include
the registers used by the storage-allocation system -- root
, the-cars
, the-cdrs
, and the other registers that will be
introduced in this section.
17 The term broken heart was coined by David Cressey, who wrote a garbage collector for MDL, a dialect of Lisp developed at MIT during the early 1970s.
18 The garbage collector uses the low-level predicate
pointer-to-pair?
instead of the list-structure pair?
operation because in a real system there might be various things
that are treated as pairs for garbage-collection purposes.
For example, in a Scheme system that conforms to the IEEE standard
a procedure object may be implemented as a special kind of pair
that doesn't satisfy the pair?
predicate.
For simulation purposes, pointer-to-pair?
can be implemented as
pair?
.