[llvm-dev] [RFC] design doc for straight-line scalar optimizations

[escha via llvm-dev]

It’s not a big issue since they’re opt-in passes, naturally, but if we did want to use them, the main problem is that our addressing modes are of the form:

X + sext(Y) * scale

for i64 X and i32 Y and various (power of 2) scales.

A lot of these passes tend to move around the sexts in ways that prevent them from being folded into addressing modes. Another thing I’ve noticed (in separateGEPFromConstantOffset) ends up turning (X + sext(Y + C)) into (X + sext(Y)) + C. Unfortunately, unless this saves operations due to CSE, it ends up with us having:

tmp = X + sext(Y)
load with base = X and offset = C

instead of

tmp = Y + C
load with base = X and offset = tmp

Since 64-bit adds are more expensive than 32-bit adds on our GPU, this ends up being a pessimization.

These are just a few examples I spotted; it’s probably a more general LLVM problem as a whole that passes which manipulate GEPs and induction variables tend to be oblivious of addressing modes, or tuned towards a particular sort of addressing mode.

—escha

> On Aug 24, 2015, at 6:52 PM, Jingyue Wu <jingyue at google.com> wrote:
> 
> Hi Escha, 
> 
> We certainly would love to generalize them as long as the performance doesn't suffer in general. If you have specific use cases that are regressed due to these optimizations, I am more than happy to take a look. 
> 
> On Mon, Aug 24, 2015 at 6:43 PM, escha <escha at apple.com <mailto:escha at apple.com>> wrote:
> 
>> On Aug 24, 2015, at 11:10 AM, Jingyue Wu via llvm-dev <llvm-dev at lists.llvm.org <mailto:llvm-dev at lists.llvm.org>> wrote:
>> 
>> Hi, 
>> 
>> As you may have noticed, since last year, we (Google's CUDA compiler team) have contributed quite a lot to the effort of optimizing LLVM for CUDA programs. I think it's worthwhile to write some docs to wrap them up for two reasons. 
>> 1) Whoever wants to understand or work on these optimizations has some detailed docs instead of just source code to refer to. 
>> 2) RFC on how to improve these optimizations so that other targets can benefit from them as well. They are currently mostly restricted to the NVPTX backend, but I see many potentials to generalize them. 
>> 
>> So, I started from this overdue design doc <https://urldefense.proofpoint.com/v2/url?u=https-3A__docs.google.com_document_d_1momWzKFf4D6h8H3YlfgKQ3qeZy5ayvMRh6yR-2DXn2hUE_edit-3Fusp-3Dsharing&d=BQMFaQ&c=eEvniauFctOgLOKGJOplqw&r=szS1_DDBoKCtS8B5df7mJg&m=TggebUNOWYFU5W3tKpC_z1CkNT9MN05aBwWloSru2NI&s=vmPxp-RDJuf_ZN5X7LNlV10JwuHK5Pt1ljn96IenW-o&e=> on the straight-line scalar optimizations. I will send out more docs on other optimizations later. Please feel free to comment. 
>> 
>> Thanks, 
>> Jingyue
> 
> Out of curiosity, is there any plan to make the NVPTX-originated passes (separateconstantoffsetfromgep, slsr, naryreassociate) more generic? They seem very specialized for the nVidia GPU addressing modes despite the generic names, and in my tests tend to pessimize our target more often than not for that reason.
> 
> It’d be really nice to have something more generic, and I might look into helping with that sort of thing in the future if it becomes important for us.
> 
> —escha
> 

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