Should be more asymptotically happy.

We process each variable in turn to find all the
locations where it needs a phi (the dominance frontier
of all of its definitions).  Then we add all those phis.
This takes O(n * #variables), although hopefully much less.

Then we do a single tree walk to match all the
FwdRefs with the nearest definition or phi.
This takes O(n) time.

The one remaining inefficiency is that we might end up
introducing a bunch of dead phis in the first step.
A TODO is to introduce phis only where they might be
used by a read.

The old algorithm is still faster on small functions,
so there's a cutover size (currently 500 blocks).

This algorithm supercedes the David's sparse phi
placement algorithm for large functions.

Lowers compile time of example from #14934 from
~10 sec to ~4 sec.
Lowers compile time of example from #16361 from
~4.5 sec to ~3 sec.
Lowers #16407 from ~20 min to ~30 sec.

Update #14934
Update #16361
Fixes #16407

Change-Id: I1cff6364e1623c143190b6a924d7599e309db58f
Reviewed-on: https://go-review.googlesource.com/30163
Reviewed-by: David Chase <drchase@google.com>