[llvm-dev] [GSoC] Machine learning and compiler optimizations: using inter-procedural analysis to select optimizations

[Сидоров , Константин Сергеевич via llvm-dev]

Dear all,

I would like to continue the discussion of the GSoC project I mentioned in
the previous email. Now, when I know my way around the LLVM codebase, I
would like to propose the first draft of the plan:

* Improving heuristics for existing passes – to start the discussion, I
propose to start the project by working on `MLInlineAdvisor` (as far as I
understand, in this class the ML infrastructure is already developed, and
thus it seems to be a good idea to start there) and after that switching to
the other passes (e.g., `LoopVectorizationPlanner` seems to be a good
candidate for such an approach, and `LoopRotate` class contains a
profitability heuristic which could also be studied deeper).
* Machine learning models to select the optimizations – to the best of my
understanding, the key concept here is the pass manager, but here I don't
quite understand the technical details of deciding which optimization to
select. For this reason, I would like to discuss this part more thoroughly.

If the project mentors are reading this mailing list and are interested in
the discussion, can we start the discussion here?

By the way – I would like to thank Stefanos for the comprehensive response
to my previous questions that helped me to get started :)

Looking forward to a further discussion,
Konstantin Sidorov

вт, 19 янв. 2021 г. в 07:04, Сидоров , Константин Сергеевич <
sidorov.ks at phystech.edu>:

> Dear all,
>
> My name is Konstantin Sidorov, and I am a graduate student in Mathematics
> at Moscow Institute of Physics and Technology.
>
> I would like to work on a project "Machine learning and compiler
> optimizations: using inter-procedural analysis to select optimizations"
> during the Google Summer of Code 2021.
>
> I have an extensive background relevant to this project - in particular:
>
> * I have already participated in GSoC before in 2017 with mlpack
> organization on the project "Augmented RNNs":
> https://summerofcode.withgoogle.com/archive/2017/projects/4583913502539776/
> * In 2019 I have graduated from the Yandex School of Data Analysis — a
> two-year program in Data Analysis by Yandex (the leading Russian search
> engine); more info on the curriculum could be also found at
> https://yandexdataschool.com/.
> * I have also been working as a software engineer at Adeptik from July
> 2018 to date, where I have predominantly worked on projects on applied
> combinatorial optimization problems, such as vehicle-routing problems or
> supply chain modeling. In particular, I have had experience with both
> metaheuristic algorithms (e.g., local search or genetic algorithms) and
> more "traditional" mathematical modeling (e.g., linear programming or
> constraint programming).
>
> I would like to discuss this project in more detail. While it is hard to
> discuss any kind of exact plan at this stage, I already have two questions
> concerning this project:
>
> (1) I have set up an LLVM dev environment, but I am unsure what to do
> next. Could you advise me on any simple (and, preferably, relevant) tasks
> to work on?
> (2) Could you suggest any learning materials to improve the understanding
> of "low-level" concepts? (E.g., CPU concepts such as caching and SIMD)
>
> Best regards,
> Konstantin Sidorov
>
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