[LLVMdev] RFC - Profile Guided Optimization in LLVM

[Diego Novillo]

On 2013-06-12 17:23 , Diego Novillo wrote:
>
> I have started looking at the state of PGO (Profile Guided 
> Optimization) in LLVM.**I want to discuss my high-level plan and make 
> sure I'm not missing anything interesting out.  I appreciate any 
> feedback on this, pointers to existing work, patches and anything 
> related to PGO in LLVM.

Good grief.  A whole lot of fail in my cut-n-paste job.  Apologies.
>
> I will be keeping changes to this plan in this web document
>
> *https://docs.google.com/document/d/1b2XFuOkR2K-Oao4u5fR3a9Ok83IB_W4EJWVmNak4GRE/pub
> *

You can read from the above or this:

At a high-level, I would like the PGO harness to contain the following 
modules:

Profile generators

These modules represent sources of profile.  Mostly, they work by 
instrumenting the user program to make it produce profile information.  
However, other sources of profile information (e.g., samples, hardware 
counters, static predictors) would be supported.


Profile Analysis Oracles

Profile information is loaded into the compiler and translated into 
analysis data which the optimizers can use.  These oracles become the 
one and only source of profile information used by transformations.  
Direct access to the raw profile data generated externally is not allowed.

Translation from profile information into analysis can be done by adding 
IR metadata or altering compiler internal data structures directly.  I 
prefer IR metadata because it simplifies debugging, unit testing and bug 
reproduction.

Analyses should be narrow in the specific type of information they 
provide (e.g., branch probability) and there should not be two different 
analyses that provide overlapping information.  We could later provide 
broader analyses types by aggregating the existing ones.



Transformations

Transformations should naturally take advantage of profile information 
by consulting the analyses.  The better information they get from the 
analysis oracles, the better their decisions.


My plan is to start by making sure that the infrastructure exists and 
provides the basic analyses.
I have two primary goals in this first phase:

1- Augment the PGO infrastructure where required.
2- Fix existing transformations that are not taking advantage of profile 
data.


In evaluating and triaging the existing infrastructure, I will use test 
cases taken from GCC's own testsuite, a collection of Google's internal 
applications and any other code base folks consider useful.

In using GCC's testsuite, my goal is not to mimic how GCC does its work, 
but make sure that the two compilers implement functionally equivalent 
transformations.  That is, make sure that LLVM is not leaving 
optimization opportunities behind.

This may require implementing missing profile functionality. From a 
brief inspection of the code, most of the major ones seem to be there 
(edge, path, block).  But I don't know what state they are in.

Some of the properties I would like to maintain or add to the current 
framework:

* Profile data is never accessed directly by analyses and 
transformations.  Rather, it is translated into IR metadata.

* Graceful degradation in the presence of stale profiles.  Old profile 
data should only result in degraded optimization opportunities.  It 
should neither confuse the compiler nor cause erroneous code generation.

After the basic profile-based transformations are working, I would like 
to add new sources of profile.  Mainly, I am thinking of implementing 
Auto FDO. FDO stands for Feedback Directed Optimization (both PGO and 
FDO tend to be used interchangeably in the GCC community).  In this 
scheme, the compiler does not instrument the code.  Rather, it uses an 
external sample collection tool (e.g., perf) to collect samples from the 
program's execution.  These samples are then converted to the format 
that the instrumented program would've emitted.

In terms of optimizations, our (Google) experience is that inlining is 
the key beneficiary of profile information. Particularly, in big C++ 
applications. I expect to focus most of my attention on the inliner.

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