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<a name="Number-of-iterations"></a>
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<a name="Number-of-iterations-analysis"></a>
<h3 class="section">16.7 Number of iterations analysis</h3>
<a name="index-Number-of-iterations-analysis"></a>

<p>Both on GIMPLE and on RTL, there are functions available to determine
the number of iterations of a loop, with a similar interface.  The
number of iterations of a loop in GCC is defined as the number of
executions of the loop latch.  In many cases, it is not possible to
determine the number of iterations unconditionally &ndash; the determined
number is correct only if some assumptions are satisfied.  The analysis
tries to verify these conditions using the information contained in the
program; if it fails, the conditions are returned together with the
result.  The following information and conditions are provided by the
analysis:
</p>
<ul>
<li> <code>assumptions</code>: If this condition is false, the rest of
the information is invalid.
</li><li> <code>noloop_assumptions</code> on RTL, <code>may_be_zero</code> on GIMPLE: If
this condition is true, the loop exits in the first iteration.
</li><li> <code>infinite</code>: If this condition is true, the loop is infinite.
This condition is only available on RTL.  On GIMPLE, conditions for
finiteness of the loop are included in <code>assumptions</code>.
</li><li> <code>niter_expr</code> on RTL, <code>niter</code> on GIMPLE: The expression
that gives number of iterations.  The number of iterations is defined as
the number of executions of the loop latch.
</li></ul>

<p>Both on GIMPLE and on RTL, it necessary for the induction variable
analysis framework to be initialized (SCEV on GIMPLE, loop-iv on RTL).
On GIMPLE, the results are stored to <code>struct tree_niter_desc</code>
structure.  Number of iterations before the loop is exited through a
given exit can be determined using <code>number_of_iterations_exit</code>
function.  On RTL, the results are returned in <code>struct niter_desc</code>
structure.  The corresponding function is named
<code>check_simple_exit</code>.  There are also functions that pass through
all the exits of a loop and try to find one with easy to determine
number of iterations &ndash; <code>find_loop_niter</code> on GIMPLE and
<code>find_simple_exit</code> on RTL.  Finally, there are functions that
provide the same information, but additionally cache it, so that
repeated calls to number of iterations are not so costly &ndash;
<code>number_of_latch_executions</code> on GIMPLE and <code>get_simple_loop_desc</code>
on RTL.
</p>
<p>Note that some of these functions may behave slightly differently than
others &ndash; some of them return only the expression for the number of
iterations, and fail if there are some assumptions.  The function
<code>number_of_latch_executions</code> works only for single-exit loops.
The function <code>number_of_cond_exit_executions</code> can be used to
determine number of executions of the exit condition of a single-exit
loop (i.e., the <code>number_of_latch_executions</code> increased by one).
</p>
<p>On GIMPLE, below constraint flags affect semantics of some APIs of number
of iterations analyzer:
</p>
<ul>
<li> <code>LOOP_C_INFINITE</code>: If this constraint flag is set, the loop
is known to be infinite.  APIs like <code>number_of_iterations_exit</code> can
return false directly without doing any analysis.
</li><li> <code>LOOP_C_FINITE</code>: If this constraint flag is set, the loop is
known to be finite, in other words, loop&rsquo;s number of iterations can be
computed with <code>assumptions</code> be true.
</li></ul>

<p>Generally, the constraint flags are set/cleared by consumers which are
loop optimizers.  It&rsquo;s also the consumers&rsquo; responsibility to set/clear
constraints correctly.  Failing to do that might result in hard to track
down bugs in scev/niter consumers.  One typical use case is vectorizer:
it drives number of iterations analyzer by setting <code>LOOP_C_FINITE</code>
and vectorizes possibly infinite loop by versioning loop with analysis
result.  In return, constraints set by consumers can also help number of
iterations analyzer in following optimizers.  For example, <code>niter</code>
of a loop versioned under <code>assumptions</code> is valid unconditionally.
</p>
<p>Other constraints may be added in the future, for example, a constraint
indicating that loops&rsquo; latch must roll thus <code>may_be_zero</code> would be
false unconditionally.
</p>
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