[llvm-dev] [RFC] Goals for VPlan-based cost modelling
Sjoerd Meijer via llvm-dev
llvm-dev at lists.llvm.org
Fri Oct 30 04:39:55 PDT 2020
Hello Anna,
There was a VPlan round-table at the US LLVM dev conference just a few weeks ago, and I have linked in the people who were involved in this as cost-modeling was one of the topics that was discussed. You may have seen this already, but Dave started doing some work in this area recently:
https://reviews.llvm.org/D89322
https://reviews.llvm.org/D89323
Just some first high-level remarks on your proposal: I think work in this area would be very valuable and solve a real problem that we currently have. If you plan to choose this as a subject for a master's thesis, you probably need to think about how big of a task this is and having some motivating examples would be good too (but you mentioned some already). To me it sounds like a big task that needs a lot of plumbing in VPlan, but that's why having this discussion here is interesting and hopefully people with more VPlan experience can provide insights.
Cheers,
Sjoerd.
________________________________
From: llvm-dev <llvm-dev-bounces at lists.llvm.org> on behalf of Anna Sophia Welker via llvm-dev <llvm-dev at lists.llvm.org>
Sent: 30 October 2020 10:08
To: llvm-dev at lists.llvm.org <llvm-dev at lists.llvm.org>
Subject: [llvm-dev] [RFC] Goals for VPlan-based cost modelling
Hi all,
I am looking into the benefits of a VPlan-based cost model, and into how such a cost model should be implemented to make the most out of these benefits.
Over the last year I have been working with LLVM, mainly focused on the ARM backend, in the course of a one-year internship at Arm Ltd. My main project from December 2019 to August 2020 was to introduce gather/scatters for MVE auto-vectorization. One of the recurring challenges during this work was to get things right in the cost model.
For example, gathers can extend the data they load, while scatters can truncate their input, meaning that an extend following the load, or a truncate preceding the store, is for free if it meets certain type conditions. As the current cost model is not designed for context-based analysis, this was a pain to model.
I have done some research and found out that one of the proposed benefits of VPlan is that a new cost model making use of it would be able to better support context-dependent decisions like this.
However, there does not exist much specification about what such a cost model should look like.
Also, I have read through the respective code to understand how loop vectorization is currently done and how far the introduction of VPlan has progressed and have realised that the only recipe that actually handles more than one instruction from the input IR is the one for interleaved groups. When the VPlan is generated on the VPlan-native path, every IR instruction is considered and transformed into a recipe separately, ignoring its context (to give a code location, I am looking at VPlanTransforms::VPInstructionsToVPRecipes).
And maybe there are architectures that for some cases do not have the same vector instructions, so a pattern that works great for one could be useless for others. So I am wondering: Is there any plan to have target-dependent flavours of recipes, or how will those things be handled?
Right now it makes sense that nothing like this has been implemented yet, as there is no cost model that could guide the transformation. But if recipes are a general thing, should the cost model be the component actually providing the target-specific pattern for a recipe, together with its cost?
I am considering choosing a topic related to VPlan, possibly cost modelling, for my Master thesis, with the goal to present a solution and implement a prototype.
What I would like to ask the community is
(1) what goals there are or should be for VPlan-based cost modelling,
(2) whether there have been any discussions on target-dependent patterns yet, and
(3) which examples of inefficiencies and shortcomings of the current cost model they have come across.
I am looking forward to your feedback.
Many thanks,
Anna Welker
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