Using a Stack to Implement Recursion

With the ideas illustrated so far, we can implement any iterative process by specifying a register machine that has a register corresponding to each state variable of the process. The machine repeatedly executes a controller loop, changing the contents of the registers, until some termination condition is satisfied. At each point in the controller sequence, the state of the machine (representing the state of the iterative process) is completely determined by the contents of the registers (the values of the state variables).

Implementing recursive processes, however, requires an additional mechanism. Consider the following recursive method for computing factorials, which we first examined in section :

(define (factorial n) (if (= n 1) 1 (* (factorial (- n 1)) n)))As we see from the procedure, computing

(define (gcd a b) (if (= b 0) a (gcd b (remainder a b))))similarly had to compute another GCD. But there is an important difference between the

In the case of factorial (or any recursive process) the answer to the
new factorial subproblem is not the answer to the original problem.
The value obtained for (*n*-1)! must be multiplied by *n* to get the
final answer. If we try to imitate the GCD design, and solve
the factorial subproblem by decrementing the `n` register and
rerunning the factorial machine, we will no longer have available the
old value of `n` by which to multiply the result. We thus need a
second factorial machine to work on the subproblem. This second
factorial computation itself has a factorial subproblem, which
requires a third factorial machine, and so on. Since each factorial
machine contains another factorial machine within it, the total
machine contains an infinite nest of similar machines and hence cannot
be constructed from a fixed, finite number of parts.

Nevertheless, we can implement the factorial process as a register
machine if we can arrange to use the same components for each nested
instance of the machine. Specifically, the machine that computes *n*!
should use the same components to work on the subproblem of computing
(*n*-1)!, on the subproblem for (*n*-2)!, and so on. This is
plausible because, although the factorial process dictates that an
unbounded number of copies of the same machine are needed to perform a
computation, only one of these copies needs to be active at any given
time. When the machine encounters a recursive subproblem, it can
suspend work on the main problem, reuse the same physical parts to
work on the subproblem, then continue the suspended computation.

In the subproblem, the contents of the registers will be different
than they were in the main problem. (In this case the `n` register
is decremented.) In order to be able to continue the suspended
computation, the machine must save the contents of any registers that
will be needed after the subproblem is solved so that these can be
restored to continue the suspended computation. In the case of
factorial, we will save the old value of `n`, to be restored when
we are finished computing the factorial of the decremented `n`
register.^{}

Since there is no *a priori* limit on the depth of nested
recursive calls, we may need to save an arbitrary number of register
values. These values must be restored in the reverse of the order in
which they were saved, since in a nest of recursions the last
subproblem to be entered is the first to be finished. This dictates
the use of a *stack*, or ``last in, first out'' data structure, to
save register values. We can extend the register-machine language to
include a stack by adding two kinds of instructions: Values are placed
on the stack using a `save` instruction and restored from the
stack using a `restore` instruction. After a sequence of values
has been `save`d on the stack, a sequence of `restore`s will
retrieve these values in reverse order.^{}

With the aid of the stack, we can reuse a single copy of the factorial
machine's data paths for each factorial subproblem. There is a
similar design issue in reusing the controller sequence that operates
the data paths. To reexecute the factorial computation, the
controller cannot simply loop back to the beginning, as with
an iterative process, because after solving the (*n*-1)! subproblem
the machine must still multiply the result by *n*. The controller
must suspend its computation of *n*!, solve the (*n*-1)! subproblem,
then continue its computation of *n*!. This view of the factorial
computation suggests the use of the subroutine mechanism described in
section , which has the controller use a
`continue` register to transfer to the part of the sequence that
solves a subproblem and then continue where it left off on the main
problem. We can thus make a factorial subroutine that returns to the
entry point stored in the `continue` register. Around each subroutine
call, we save and restore `continue` just as we do the `n`
register, since each ``level'' of the factorial computation will use
the same `continue` register. That is, the factorial subroutine
must put a new value in `continue` when it calls itself for a
subproblem, but it will need the old value in order to return to the
place that called it to solve a subproblem.

Figure shows the data paths and controller for
a machine that implements the recursive `factorial` procedure.
The machine has a stack and three registers, called `n`, `
val`, and `continue`. To simplify the data-path diagram, we have
not named the register-assignment buttons, only the stack-operation
buttons (`sc` and `sn` to save registers, `rc` and `
rn` to restore registers). To operate the machine, we put in register
`n` the number whose factorial we wish to compute and start the
machine. When the machine reaches `fact-done`, the computation is
finished and the answer will be found in the `val` register. In
the controller sequence, `n` and `continue` are saved before
each recursive call and restored upon return from the call. Returning
from a call is accomplished by branching to the location stored in
`continue`. `Continue` is initialized when the machine starts
so that the last return will go to `fact-done`. The `val`
register, which holds the result of the factorial computation, is not
saved before the recursive call, because the old contents of `val`
is not useful after the subroutine returns. Only the new value, which
is the value produced by the subcomputation, is needed.

Although in principle the factorial computation requires an infinite
machine, the machine in figure is actually
finite except for the stack, which is potentially unbounded. Any
particular physical implementation of a stack, however, will be of
finite size, and this will limit the depth of recursive calls that can
be handled by the machine. This implementation of factorial
illustrates the general strategy for realizing recursive algorithms as
ordinary register machines augmented by stacks. When a recursive
subproblem is encountered, we save on the stack the registers whose
current values will be required after the subproblem is solved, solve
the recursive subproblem, then restore the saved registers and
continue execution on the main problem. The `continue` register
must always be saved. Whether there are other registers that need to
be saved depends on the particular machine, since not all recursive
computations need the original values of registers that are modified
during solution of the subproblem (see exercise ).

A double recursion

Let us examine a more complex recursive process, the tree-recursive computation of the Fibonacci numbers, which we introduced in section :

(define (fib n) (if (< n 2) n (+ (fib (- n 1)) (fib (- n 2)))))Just as with factorial, we can implement the recursive Fibonacci computation as a register machine with registers

**Exercise.** Specify register machines that implement each of the following
procedures. For each machine, write a controller instruction sequence
and draw a diagram showing the data paths.

a. Recursive exponentiation:

(define (expt b n) (if (= n 0) 1 (* b (expt b (- n 1)))))

b. Iterative exponentiation:

(define (expt b n) (define (expt-iter counter product) (if (= counter 0) product (expt-iter (- counter 1) (* b product)))) (expt-iter n 1))

**Exercise.**
Hand-simulate the factorial and Fibonacci machines, using some
nontrivial input (requiring execution of at least one recursive call).
Show the contents of the stack at each significant point in the
execution.

**Exercise.**
Ben Bitdiddle observes that the Fibonacci machine's controller sequence
has an extra `save` and an extra `restore`, which can be
removed to make a faster machine. Where are these instructions?