[llvm-dev] Enabling scalarized conditional stores in the loop vectorizer

[Michael Kuperstein via llvm-dev]

Conceptually speaking, I think we really ought to enable this.

Practically, I'm going to test it on our benchmarks (on x86), and see if we
have any regressions - this seems like a fairly major change.
Re targets - let's see where we stand w.r.t regressions first. What kind of
performance testing have you already run on this? Do you know of specific
targets where the cost model is known to be good enough, so it's clearly
beneficial?

(+Arnold, who probably knows why this is disabled by default. :-) )

Thanks,
  Michael

On Mon, Dec 12, 2016 at 2:52 PM, Matthew Simpson <mssimpso at codeaurora.org>
wrote:

> Hi,
>
> I'd like to enable the scalarized conditional stores feature in the loop
> vectorizer (-enable-cond-stores-vec=true). The feature allows us to
> vectorize loops containing conditional stores that must be scalarized and
> predicated in the vectorized loop.
>
> Note that this flag does not affect the decision to generate masked vector
> stores. That is a separate feature and is guarded by a TTI hook. Currently,
> we give up on loops containing conditional stores that must be scalarized
> (i.e., conditional stores that can't be represented with masked vector
> stores). If the feature is enabled, we attempt to vectorize those loops if
> profitable, while scalarizing and predicating the conditional stores.
>
> I think these stores are fairly well modeled in the cost model at this
> point using the static estimates. They're modeled similar to the way we
> model other non-store conditional instructions that must be scalarized and
> predicated (e.g., instructions that may divide by zero); however, only the
> conditional stores are currently disabled by default.
>
> I'd appreciate any opinions on how/if we can enable this feature. For
> example, can we enable it for all targets or would a target-by-target
> opt-in mechanism using a TTI hook be preferable? If you'd like to test the
> feature on your target, please report any significant regressions and
> improvements you find.
>
> Thanks!
>
> -- Matt
>
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