[PATCH][LoopVectorizer] Restrict the unroll factor of reductions in loops

[Hal Finkel]

----- Original Message -----
> From: "James Molloy" <james.molloy at arm.com>
> To: "Arnold Schwaighofer" <aschwaighofer at apple.com>
> Cc: "llvm-commits" <llvm-commits at cs.uiuc.edu>
> Sent: Friday, August 8, 2014 9:37:38 AM
> Subject: [PATCH][LoopVectorizer] Restrict the unroll factor of reductions in	loops
> 
> 
> 
> 
> 
> Hi Arnold,
> 
> 
> 
> Attached are two patches. The first ups the maximum unroll factor on
> AArch64 from 2 to 4, for C-A57 only at the moment as that’s all I’ve
> got data for. This gives us significant wins – ~14% on
> 462.libquantum at least.
> 
> 
> 
> However it also causes some regressions. The second patch makes the
> loop vectorizer a bit more conservative with its unroll factor. The
> problem is purely for reductions within loops. The regressions I’ve
> seen are small (but runtime-known) trip count loops within a loop
> nest. A loop unroll factor of 2 is fine, but above 2 the reduction
> variable fixup logic after the loop increases the critical path
> length and resource usage. For most loops this isn’t a problem, but
> small loops in a larger loop nest will execute this fixup code many
> times.

Can you please add a flag for this? I anticipate needing to tune it.

Also, it seems to me that this is exactly the kind of thing that would benefit from profiling information (so we can determine if the inner loop is likely to have a large trip count). Can the current infrastructure do this? Also, maybe in cases where the inner loop count is not a function of the outer loop, we might 'unswitch' it so that we get the unrolled inner loop only when actually profitable.

Thanks again,
Hal

> 
> 
> 
> The heuristic is: if this is a (scalar) reduction, and the loop is
> nested, clamp the UF to a maximum of 2. With 2, we still get wins
> but we only add one fadd/fmul to the critical path.
> 
> 
> 
> Please take a look.
> 
> 
> 
> Cheers,
> 
> 
> 
> James
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> 

-- 
Hal Finkel
Assistant Computational Scientist
Leadership Computing Facility
Argonne National Laboratory