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

[Сидоров , Константин Сергеевич via llvm-dev]

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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