GCC's vectorization of loop reduction for Zen3 is ~50% faster according to LLVM-MCA

[TiborGY]

I am trying to optimize a few hotspots in a larger body of code, one of which is an RMSD computation implemented as loop reduction, where the loop bounds are completely known at compile time:

double RMSD(const double* const dm1, const double* const dm2){
    const size_t distsPerGeom = (12*11)/2;
    double RMSD = 0.0;
    for (size_t i=0; i<distsPerGeom; i++){
        RMSD += std::pow(dm1[i] - dm2[i], 2);
    }
    return std::sqrt(RMSD / distsPerGeom);
}

What I have found, is that according to LLVM-MCA, GCC is far better at vectorizing this function, 2823 total cycles vs. 4455, see this link:
https://godbolt.org/z/q85rq3bW4

I have not tested this on real HW yet, but I cannot see how this is not indicative of some issue in an LLVM component. Either MCA's cycle counts are wrong for Zen3, or clang is not as good at vectorizing as GCC is.

[RKSimon]

It appears to be a reassociation issue - clang is summing RMSD into the same ymm7 accumulation result:

vfmadd231pd %ymm2, %ymm2, %ymm7
vfmadd231pd %ymm1, %ymm1, %ymm7
vfmadd231pd %ymm0, %ymm0, %ymm7

which means that all the vfmadd231pd instructions are performed serially, while gcc is splitting the FMA accumulation and then adding the sub-results together, improving IPC.

vfmadd231pd %ymm0, %ymm0, %ymm1
..
vfmadd132pd %ymm0, %ymm4, %ymm0
..
vfmadd231pd %ymm0, %ymm0, %ymm2
..
vaddpd %ymm2, %ymm0, %ymm0
vaddpd %ymm1, %ymm0, %ymm0

[llvmbot]

@llvm/issue-subscribers-tools-llvm-mca

Author: None (TiborGY)

I am trying to optimize a few hotspots in a larger body of code, one of which is an RMSD computation implemented as loop reduction, where the loop bounds are completely known at compile time: ``` double RMSD(const double* const dm1, const double* const dm2){ const size_t distsPerGeom = (12*11)/2; double RMSD = 0.0; for (size_t i=0; i<distsPerGeom; i++){ RMSD += std::pow(dm1[i] - dm2[i], 2); } return std::sqrt(RMSD / distsPerGeom); } ``` What I have found, is that according to LLVM-MCA, GCC is far better at vectorizing this function, 2823 total cycles vs. 4455, see this link: https://godbolt.org/z/ov45ebsoM

I have not tested this on real HW yet, but I cannot see how this is not indicative of some issue in an LLVM component. Either MCA's cycle counts are wrong for Zen3, or clang is not as good at vectorizing as GCC is.

[firewave]

FYI The data might be tainted. llvm-mca will default to the underlying CPU and since this is a cloud service it will obviously have various possible platforms. So it is possible to get different results for the same input. If you add -mcpu=generic (or your intended target) to the "Arguments" you will get consistent results.

See compiler-explorer/compiler-explorer#4085.

[RKSimon]

Adding an explicit -mcpu=znver3 to the llvm-mca command lines made very little difference to the estimated instruction cycles, and still shows the 2x ratio in the "Total Cycles" numbers.

[mshockwave]

FYI The data might be tainted. llvm-mca will default to the underlying CPU and since this is a cloud service it will obviously have various possible platforms. So it is possible to get different results for the same input. If you add -mcpu=generic (or your intended target) to the "Arguments" you will get consistent results.

See compiler-explorer/compiler-explorer#4085.

It is true that one almost certain wants to add an explicit -mcpu= option when using llvm-mca, but I don't think -mcpu=generic is a good one since IIRC it's effectively an alias of -mcpu=sandybridge -- a processor that is too old (of course it still depends on which kind of applications are you profiling)

[firewave]

Adding an explicit -mcpu=znver3 to the llvm-mca command lines made very little difference to the estimated instruction cycles, and still shows the 2x ratio in the "Total Cycles" numbers.

In this case it indeed did not make a different and the underlying architecture is identical. It might be possible that using a -mcpu on the compiler tab will also apply this to the llvm-mca invocation somehow (maybe what was suggested in the comment in my ticket was actually applied). I never used that flag and ended up with Zen vs. Skylake output (as outlined in the ticket).

[TiborGY]

I have looked at the MCA outputs before I have submitted this issue, and both showed that MCA used znver3 by default, so the data were valid.
But good catch, if MCA defaults to the host CPU it might drift to whatever cloud machine happens to be free at the moment, so I have updated the link with explicit znver3: https://godbolt.org/z/q85rq3bW4

[mshockwave]

It appears to be a reassociation issue - clang is summing RMSD into the same ymm7 accumulation result:

vfmadd231pd %ymm2, %ymm2, %ymm7
vfmadd231pd %ymm1, %ymm1, %ymm7
vfmadd231pd %ymm0, %ymm0, %ymm7
which means that all the vfmadd231pd instructions are performed serially, while gcc is splitting the FMA accumulation and then adding the sub-results together, improving IPC.

vfmadd231pd %ymm0, %ymm0, %ymm1
..
vfmadd132pd %ymm0, %ymm4, %ymm0
..
vfmadd231pd %ymm0, %ymm0, %ymm2
..
vaddpd %ymm2, %ymm0, %ymm0
vaddpd %ymm1, %ymm0, %ymm0

Assuming this is to be solved in MachineCombiner (though preferably it should be solved in ReassociatePass on the pre-vectorized scalar code), I think this can be solved by adding new patterns into X86InstrInfo::getMachineCombinerPatterns

[RKSimon]

I'd much prefer to see this handled in the middle end and not make it x86 specific