For all forms of Profiling, you can gather counts for all functions on the stack, not just the currently executing function. To do so, compile your program with -profile-stack true. For example, suppose that list-rev.sml contains the following.

fun append (l1, l2) =
   case l1 of
      [] => l2
    | x :: l1 => x :: append (l1, l2)

fun rev l =
   case l of
      [] => []
    | x :: l => append (rev l, [x])

val l = List.tabulate (1000, fn i => i)
val _ = 1 + hd (rev l)

Compile with stack profiling and then run the program.

% mlton -profile alloc -profile-stack true list-rev.sml
% ./list-rev

Display the profiling data.

% mlprof -show-line true list-rev mlmon.out
6,030,136 bytes allocated (108,336 bytes by GC)
       function          cur  stack  GC 
----------------------- ----- ----- ----
append  list-rev.sml: 1 97.6% 97.6% 1.4%
<gc>                     1.8%  0.0% 1.8%
<main>                   0.4% 98.2% 1.8%
rev  list-rev.sml: 6     0.2% 97.6% 1.8%

In the above table, we see that rev, defined on line 6 of list-rev.sml, is only responsible for 0.2% of the allocation, but is on the stack while 97.6% of the allocation is done by the user program and while 1.8% of the allocation is done by the garbage collector.

The run-time performance impact of -profile-stack true can be noticeable since there is some extra bookkeeping at every nontail call and return.