[llvm-dev] PGO is ineffective for Rust - but why?

[Michael Woerister via llvm-dev]

> Are the 'function_entry_count' and the 'ProfileSummary' metadata included in the IR? I think some PGO passes may expect them to trigger.

Yes, they are present in the IR generate during the `profile-use`
phase. For my test case you can take a look at the IR generated here:

generate-phase:
https://github.com/michaelwoerister/rust-pgo-test-programs/blob/master/branch_weights/outputs/opt_lib_gen.lld.ll
use-phase: https://github.com/michaelwoerister/rust-pgo-test-programs/blob/master/branch_weights/outputs/opt_lib_use.lld.ll
source code: https://github.com/michaelwoerister/rust-pgo-test-programs/blob/master/branch_weights/opt_lib.rs

However, when using the right linker and thus not running into the GNU
ld bug mentioned earlier, I'm seeing proper speedups with PGO for this
test case. For other (larger) test cases I still don't see speedups so
I'll need to take a closer look at those.

On Tue, Sep 17, 2019 at 8:23 PM Hiroshi Yamauchi <yamauchi at google.com> wrote:
>
>
>
> On Thu, Sep 12, 2019 at 2:18 AM Michael Woerister via llvm-dev <llvm-dev at lists.llvm.org> wrote:
>>
>> Hi everyone,
>>
>> As part of my work for Mozilla's Low Level Tools team I've
>> implemented PGO in the Rust compiler. The feature is
>> available since Rust 1.37 [1]. However, so far we have not
>> seen any actual performance gains from enabling PGO for
>> Rust code. Performance even seems to drop 1-3% with PGO
>> enabled. I wonder why that is and I'm hoping that someone
>> here might have experience debugging PGO effectiveness.
>>
>>
>> PGO in the Rust compiler
>> ------------------------
>>
>> The Rust compiler uses IR-level instrumentation (the
>> equivalent of Clang's `-fprofile-generate`/`-fprofile-use`).
>> This has worked pretty well and even enables doing PGO for
>> mixed Rust/C++ codebases when also using Clang.
>>
>> The Rust compiler has regression tests that make sure that:
>>
>> - instrumentation shows up in LLVM IR for the `generate` phase,
>>   and that
>>
>> - profiling data is actually used during the `use` phase, i.e.
>>   that cold functions get marked with `cold` and hot functions
>>   get marked with `inline`.
>>
>> I also verified manually that `branch_weights` are being set
>> in IR. So, from my perspective, the PGO implementation does
>> what it is supposed to do.
>
>
> Are the 'function_entry_count' and the 'ProfileSummary' metadata included in the IR? I think some PGO passes may expect them to trigger.
>
>>
>> However, as already mentioned, in all benchmarks I've seen so
>> far performance seems to stay the same at best and often even
>> suffers slightly. Which is suprising because for C++ code
>> using Clang's version of IR-level instrumentation & PGO brings
>> signifcant gains (up to 5-10% from what I've seen in
>> benchmarks for Firefox).
>>
>> One thing we noticed early on is that disabling the
>> pre-inlining pass (`-disable-preinline`) seems to consistently
>> improve the situation for Rust code. Doing that we sometimes
>> see performance wins of almost 1% over not using PGO. This
>> again is very different to C++ where disabling this pass
>> causes dramatic performance loses for the Firefox benchmarks.
>> And 1% performance improvement is still well below
>> expectations, I think.
>>
>> So my questions to you are:
>>
>> - Has anybody here observed something similar while
>>   wokring on or with PGO?
>>
>> - Are there certain known characteristics of LLVM IR code
>>   that inhibit PGO's effectiveness and that IR produced by
>>   `rustc` might exhibit?
>>
>> - Does anybody know of a good source that describes how to
>>   effectively debug a problem like this?
>>
>> - Does anybody know of a small example program in C/C++
>>   that is known to profit from PGO and that could be
>>   re-implemented in Rust for comparison?
>>
>> Thanks a lot for reading! Any help is appreciated.
>>
>> -Michael
>>
>> [1] https://blog.rust-lang.org/2019/08/15/Rust-1.37.0.html#profile-guided-optimization
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