Sparse Conditional Constant Propagation

July 9, 2001

Daniel Berlin and Jeff Law have contributed an SSA based sparse conditional constant optimization (SSA CCP) pass to GCC.

SSA CPP is an aggressive constant propagation algorithm that performs traditional global constant propagation, but also uses the SSA graph to identify and optimize away branches which it can prove can never be taken.

Consider this function (from GCC's runtime support library):

long
__divsi3 (long a, long b)
{
  int neg = 0;
  long res;

  if (a < 0)
    {
      a = -a;
      neg = !neg;
    }

  if (b < 0)
    {
      b = -b;
      neg = !neg;
    }

  res = udivmodsi4 (a, b, 0);

 if (neg)
    res = -res;

  return res;
}

The statement neg = !neg in the true arm of the first conditional will normally create a runtime branch to assign the value 1 or 0 to neg depending on neg's previous value.

A quick analysis of the program indicates that neg will always have the value zero if we enter the true arm of the first conditional which allows us to simplify the code.

Here's a more complex example derived from Morgan's textbook:

foo()
{
  int a, b, c, d, e, f, g;

  a = 3;
  d = 2;

top:
  f = a + d;
  g = 5;
  a = g - d;
  if (f <= g)
    {
      f = g + 1;
    }
  else
    {
      if (g >= a)
        return f;
    }
  d = 2;
  goto top;
}

Compiling that code without SSA CCP on an unspecified embedded target results in code like this:

foo:
        ldi     r2, #3
.L2:
        copy    r1, r2
        addi    r1, #2
	ldi	r2, #3
	ldi	r0, #5
	cmp gt	r1, r0
	brf	.L2
	copy	r0, r1
	ret

However, a careful analysis of the code will show that the entire function should collapse into an infinite loop. With SSA CCP we get the following result:

foo:
.L2:
        bra    .L2

The SSA CCP optimizer was able to determine the outcome of the cmp gt instruction and optimize the code based on that outcome.

These are merely simple examples of a fairly complex optimization that applies surprisingly often in practice. The SSA CCP optimizer performs the analysis and optimization in linear time.

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Last modified 2005-07-11