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

Mon Mar 30 18:07:16 PDT 2020

1. Thanks for the clarifications. I will stick to non-containerized OS X
for now.

2. As an aside, I did try to build a Debian docker container by git cloning
into it and using the Dockerfile in LLVM/utils/docker as a starting point:
 - some changes needed to updated packages (GCC in particular needs to be
latest) and the Debian image (Debian 9 instead of Debian 8) pretty much
sets up the docker container well. But for some reason, the Ninja build
tool within the CMake Generator fails. I am looking into it. Maybe I can
produce a working docker workflow for others who want to build and work
with LLVM in a container environment.

3. I have submitted the final proposal today to GSoC 2020 today after
incorporating some comments and thoughts. When you all get a chance to
review, let me know your thoughts.

4. On GPU extension, my thoughts were around what an integrated compiler
like Nvidia's nvcc (GCC for CPU + PTX for GPU) does when GCC is substituted
with LLVM and if that arrangement can be optimized for ML passes. But I am
beginning to think that structuring this problem well and doing meaningful
work over the summer might be a bit difficult. As mentors, do you have any
thoughts on how LLVM might be integrated into a joint CPU-GPU compiler by
the likes of Nvidia, Apple etc.?

Best
Shiva

On Mon, Mar 30, 2020 at 5:30 PM Johannes Doerfert <
johannesdoerfert at gmail.com> wrote:

>
> On 3/27/20 3:46 PM, Shiva Stanford wrote:
> > Hi Johannes - great we are engaging on this.
> >
> > Some responses now and some later.
> >
> > 1. When you say setup LLVM dev environment +. clang + tools etc, do you
> > mean setup LLVM compiler code from the repo and build it locally? If so,
> > yes, this is all done from my end - that is, I have built all this on my
> > machine and compiled and run a couple of function passes. I have look at
> > some LLVM emits from clang tools but I will familiarize more. I have
> added
> > some small code segments, modified CMAKE Lists and re-built code to get a
> > feel for the packaging  structure. Btw, is there a version of Basel build
> > for this? Right now, I am using OS X as the SDK as Apple is the one that
> > has adopted LLVM the most. But I can switch to Linux containers to
> > completely wall off the LLVM build against any OS X system builds to
> > prevent path obfuscation and truly have a separate address space. Is
> there
> > a preferable environment? In any case, I am thinking of containerizing
> the
> > build, so OS X system paths don't interfere with include paths - have you
> > received feedback from other developers on whether the include paths
> > interfere with OS X LLVM system build?
>
>
> Setup sounds good.
>
> I have never used OS X but people do and I would expect it to be OK.
>
> I don't think you need to worry about this right now.
>
>
> > 2. The attributor pass refactoring gives some specific direction as a
> > startup project - so that's great. Let me study this pass and I will get
> > back to you with more questions.
>
> Sure.
>
>
> > 3. Yes, I will stick to the style guide (Baaaah...Stanford is strict on
> > code styling and so are you guys :)) for sure.
>
> For better or worse.
>
>
> Cheers,
>
>    Johannes
>
>
>
> > On Thu, Mar 26, 2020 at 9:42 AM Johannes Doerfert <
> > johannesdoerfert at gmail.com> wrote:
> >
> >> Hi Shiva,
> >>
> >> apologies for the delayed response.
> >>
> >> On 3/24/20 4:13 AM, Shiva Stanford via llvm-dev wrote:
> >>   > I am a grad CS student at Stanford and wanted to engage with EJ
> Park,
> >>   > Giorgis Georgakoudis, Johannes Doerfert to further develop the
> Machine
> >>   > Learning and Compiler Optimization concept.
> >>
> >> Cool!
> >>
> >>
> >>   > My background is in machine learning, cluster computing, distributed
> >>   > systems etc. I am a good C/C++ developer and have a strong
> background in
> >>   > algorithms and data structure.
> >>
> >> Sounds good.
> >>
> >>
> >>   > I am also taking an advanced compiler course this quarter at
> >> Stanford. So I
> >>   > would be studying several of these topics anyways - so I thought I
> >> might as
> >>   > well co-engage on the LLVM compiler infra project.
> >>
> >> Agreed ;)
> >>
> >>
> >>   > I am currently studying the background information on SCC Call
> Graphs,
> >>   > Dominator Trees and other Global and inter-procedural analysis to
> lay
> >> some
> >>   > ground work on how to tackle this optimization pass using ML models.
> >> I have
> >>   > run a couple of all program function passes and visualized call
> graphs
> >> to
> >>   > get familiarized with the LLVM optimization pass setup. I have also
> >> setup
> >>   > and learnt the use of GDB to debug function pass code.
> >>
> >> Very nice.
> >>
> >>
> >>   > I have submitted the ML and Compiler Optimization proposal to GSOC
> >> 2020. I
> >>   > have added an additional feature to enhance the ML optimization to
> >> include
> >>   > crossover code to GPU and investigate how the function call graphs
> can
> >> be
> >>   > visualized as SCCs across CPU and GPU implementations. If the
> >> extension to
> >>   > GPU is too much for a summer project, potentially we can focus on
> >>   > developing a framework for studying SCCs across a unified CPU, GPU
> setup
> >>   > and leave the coding, if feasible, to next Summer. All preliminary
> >> ideas.
> >>
> >> I haven't looked at the proposals yet (I think we can only after the
> >> deadline). TBH, I'm not sure I fully understand your extension. Also,
> >> full disclosure, the project is pretty open-ended from my side at least.
> >> I do not necessarily believe we (=llvm) is ready for a ML driven pass or
> >> even inference in practice. What I want is to explore the use of ML to
> >> improve the code we have, especially heuristics. We build analysis and
> >> transformations but it is hard to combine them in a way that balances
> >> compile-time, code-size, and performance.
> >>
> >> Some high-level statements that might help to put my view into
> >> perspective:
> >>
> >> I want to use ML to identify patterns and code features that we can
> >> check for using common techniques but when we base our decision making
> >> on these patterns or features we achieve better compile-time, code-size,
> >> and/or performance.
> >> I want to use ML to identify shortcomings in our existing heuristics,
> >> e.g. transformation cut-off values or pass schedules. This could also
> >> mean to identify alternative (combination of) values that perform
> >> substantially better (on some inputs).
> >>
> >>
> >>   > Not sure how to proceed from here. Hence my email to this list.
> >> Please let
> >>   > me know.
> >>
> >> The email to the list was a great first step. The next one usually is to
> >> setup an LLVM development and testing environment, thus LLVM + Clang +
> >> LLVM-Test Suite that you can use. It is also advised to work on a small
> >> task before the GSoC to get used to the LLVM development.
> >>
> >> I don't have a really small ML "coding" task handy right now but the
> >> project is more about experiments anyway. To get some LLVM development
> >> experience we can just take a small task in the IPO Attributor pass.
> >>
> >> One thing we need and we don't have is data. The Attributor is a
> >> fixpoint iteration framework so the number of iterations is pretty
> >> integral part. We have a statistics counter to determine if the number
> >> required was higher than the given threshold but not one to determine
> >> the maximum iteration count required during compilation. It would be
> >> great if you could add that, thus a statistics counter that shows how
> >> many iterations where required until a fixpoint was found across all
> >> invocations of the Attributor. Does this make sense? Let me know what
> >> you think and feel free to ask questions via email or on IRC.
> >>
> >> Cheers,
> >>     Johannes
> >>
> >> P.S. Check out the coding style guide and the how to contribute guide!
> >>
> >>
> >>   > Thank you
> >>   > Shiva Badruswamy
> >>   > shivastanford at gmail.com
> >>   >
> >>   >
> >>   > _______________________________________________
> >>   > LLVM Developers mailing list
> >>   > llvm-dev at lists.llvm.org
> >>   > https://lists.llvm.org/cgi-bin/mailman/listinfo/llvm-dev
> >>
> >>
>
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