[llvm-dev] Machine learning and compiler optimizations: using inter-procedural analysis to select optimizations
Shiva Stanford via llvm-dev
llvm-dev at lists.llvm.org
Fri Mar 27 13:46:41 PDT 2020
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?
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.
3. Yes, I will stick to the style guide (Baaaah...Stanford is strict on
code styling and so are you guys :)) for sure.
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.
> > 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
> > 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
> > get familiarized with the LLVM optimization pass setup. I have also
> > 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
> > crossover code to GPU and investigate how the function call graphs can
> > 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
> 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
> 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.
> 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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