On Fri, Jul 12, 2024 at 12:09 PM Ajit Agarwal <aagar...@linux.ibm.com> wrote:
>
> Hello Richard:
>
> On 11/07/24 2:21 pm, Richard Biener wrote:
> > On Thu, Jul 11, 2024 at 10:30 AM Ajit Agarwal <aagar...@linux.ibm.com>
> > wrote:
> >>
> >> Hello All:
> >>
> >> Unroll factor is determined with max distance across loop iterations.
> >> The logic for determining the loop unroll factor is based on
> >> data dependency across loop iterations.
> >>
> >> The max distance across loop iterations is the unrolling factor
> >> that helps in predictive commoning.
> >
> > The old comment in the code says
> >
> >> - /* The best unroll factor for this chain is equal to the number of
> >> - temporary variables that we create for it. */
> >
> > why is that wrong and why is the max dependence distance more correct?
> >
> > Do you have a testcase that shows how this makes a (positive) difference?
> >
>
> There is nothing wrong in the existing implementation of unroll
> factor for predictive commoning.
>
> But with max dependence distance we get performance improvement
> with spec 2017 benchmarks (INT) of 0.01% (Geomean) with and without
> changes. Improvement in benchmarks with max dependence distance
> changes.
>
> I have used the following flags:
> -O2 -funroll-loops --param max-unroll-times=8 -fpredictive-commoning
> -fno-tree-pre
>
> With above flags I ran with and without changes.
A 0.01% geomean improvement is noise. Why did you disable PRE?
> There is no degradation with spec 2017 (FP benchmarks).
>
> Because in predictive commoning we reuse values computed in
> earlier iterations of a loop in the later ones, max distance is the
> better choice.
The re-use distance is the same though. So your change merely increases
the unroll factor? Or can you explain why there is more re-use with
your change.
Richard.
> > Richard.
> >
>
> Thanks & Regards
> Ajit
>
> >> Bootstrapped and regtested on powerpc64-linux-gnu.
> >>
> >> Thanks & Regards
> >> Ajit
> >>
> >> tree-optimization, predcom: Improve unroll factor for predictive commoning
> >>
> >> Unroll factor is determined with max distance across loop iterations.
> >> The logic for determining the loop unroll factor is based on
> >> data dependency across loop iterations.
> >>
> >> The max distance across loop iterations is the unrolling factor
> >> that helps in predictive commoning.
> >>
> >> 2024-07-11 Ajit Kumar Agarwal <aagar...@linux.ibm.com>
> >>
> >> gcc/ChangeLog:
> >>
> >> * tree-predcom.cc: Change in determining unroll factor with
> >> data dependence across loop iterations.
> >> ---
> >> gcc/tree-predcom.cc | 51 ++++++++++++++++++++++++++++++++++-----------
> >> 1 file changed, 39 insertions(+), 12 deletions(-)
> >>
> >> diff --git a/gcc/tree-predcom.cc b/gcc/tree-predcom.cc
> >> index 9844fee1e97..029b02f5990 100644
> >> --- a/gcc/tree-predcom.cc
> >> +++ b/gcc/tree-predcom.cc
> >> @@ -409,6 +409,7 @@ public:
> >> /* Perform the predictive commoning optimization for chains, make this
> >> public for being called in callback execute_pred_commoning_cbck. */
> >> void execute_pred_commoning (bitmap tmp_vars);
> >> + unsigned determine_unroll_factor (const vec<chain_p> &chains);
> >>
> >> private:
> >> /* The pointer to the given loop. */
> >> @@ -2400,13 +2401,46 @@ pcom_worker::execute_pred_commoning_chain (chain_p
> >> chain,
> >> copies as possible. CHAINS is the list of chains that will be
> >> optimized. */
> >>
> >> -static unsigned
> >> -determine_unroll_factor (const vec<chain_p> &chains)
> >> +unsigned
> >> +pcom_worker::determine_unroll_factor (const vec<chain_p> &chains)
> >> {
> >> chain_p chain;
> >> - unsigned factor = 1, af, nfactor, i;
> >> + unsigned factor = 1, i;
> >> unsigned max = param_max_unroll_times;
> >> + struct data_dependence_relation *ddr;
> >> + unsigned nfactor = 0;
> >> + int nzfactor = 0;
> >> +
> >> + /* Best unroll factor is the maximum distance across loop
> >> + iterations. */
> >> + FOR_EACH_VEC_ELT (m_dependences, i, ddr)
> >> + {
> >> + for (unsigned j = 0; j < DDR_NUM_DIST_VECTS (ddr); j++)
> >> + {
> >> + lambda_vector vec = DDR_DIST_VECT (ddr, j);
> >> + widest_int distance = vec[j];
> >> + unsigned offset = distance.to_uhwi ();
> >> + if (offset == 0)
> >> + continue;
> >> +
> >> + int dist = offset - nzfactor;
> >> + if (dist == 0)
> >> + continue;
> >>
> >> + if (nfactor == 0)
> >> + {
> >> + nfactor = offset;
> >> + nzfactor = offset;
> >> + }
> >> + else if (dist <= nzfactor)
> >> + nfactor = offset;
> >> +
> >> + if (nfactor > 0 && nfactor <= max)
> >> + factor = nfactor;
> >> + }
> >> + }
> >> +
> >> + int max_use = 0;
> >> FOR_EACH_VEC_ELT (chains, i, chain)
> >> {
> >> if (chain->type == CT_INVARIANT)
> >> @@ -2427,17 +2461,10 @@ determine_unroll_factor (const vec<chain_p>
> >> &chains)
> >> continue;
> >> }
> >>
> >> - /* The best unroll factor for this chain is equal to the number of
> >> - temporary variables that we create for it. */
> >> - af = chain->length;
> >> if (chain->has_max_use_after)
> >> - af++;
> >> -
> >> - nfactor = factor * af / gcd (factor, af);
> >> - if (nfactor <= max)
> >> - factor = nfactor;
> >> + max_use++;
> >> }
> >> -
> >> + factor += max_use;
> >> return factor;
> >> }
> >>
> >> --
> >> 2.43.5
> >>
