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 two 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 second 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 two 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.290 In order to describe memory operations, we use two primitive Scheme procedures for manipulating vectors:
(vector-ref ⟨vector⟩ ⟨n⟩)
returns the
element of the vector.
(vector-set! ⟨vector⟩ ⟨n⟩ ⟨value⟩)
sets the 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 7.291 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
two vectors: the-cars
and the-cdrs
. We will represent list
structure as follows: A pointer to a pair is an index into the two 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.292 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.293
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
4.294 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.295
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 two 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 two 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 two 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.296 For example, the instruction
(assign ⟨reg₁⟩ (op cons) (reg ⟨reg₂⟩) (reg ⟨reg₃⟩))
is implemented as the following sequence of vector operations:297
(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 initiallyp1
. What is the final value offree
? What pointers represent the values ofx
andy
?
Exercise 5.21: Implement register machines for the following procedures. Assume that the list-structure memory operations are available as machine primitives.
- Recursive
count-leaves
:(define (count-leaves tree) (cond ((null? tree) 0) ((not (pair? tree)) 1) (else (+ (count-leaves (car tree)) (count-leaves (cdr tree))))))- 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 3.3.1 presented an
append
procedure that appends two lists to form a new list and anappend!
procedure that splices two 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 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.298 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 two 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.299
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 two 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).300
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.301 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 two
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.)302 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 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
.303
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 ;; Updatefree
pointer. (assign free (op +) (reg free) (const 1)) ;; Copy thecar
andcdr
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))
290
We could represent memory as
lists of items. However, the access time would then not be independent of the
index, since accessing the element of a list requires
cdr
operations.
291
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.
292 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.
293 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.
294
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.
295 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.
296 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 5.3.2.
297
This is
essentially the implementation of cons
in terms of set-car!
and
set-cdr!
, as described in 3.3.1. The operation
get-new-pair
used in that implementation is realized here by the
free
pointer.
298
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
microseconds in a year, so if we were to cons
once per
microsecond we would need about 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.
299 We assume here that the stack is represented as a list as described in 5.3.1, so that items on the stack are accessible via the pointer in the stack register.
300 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 second 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.
301
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.
302 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.
303
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?
.