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.
I will be keeping changes to this plan in this web document
*https://docs.google.com/document/d/1b2XFuOkR2K-Oao4u5fR3a9Ok83IB_W4EJWVmNak4GRE/pub
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 <http://gcc.gnu.org/wiki/AutoFDO>. 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 <https://perf.wiki.kernel.org/index.php/Main_Page>) 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.
*Thanks. Diego.
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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. -------------- next part -------------- An HTML attachment was scrubbed... URL: <http://lists.llvm.org/pipermail/llvm-dev/attachments/20130612/d6b185af/attachment.html>
Jakob Stoklund Olesen
2013-Jun-12 21:55 UTC
[LLVMdev] RFC - Profile Guided Optimization in LLVM
On Jun 12, 2013, at 2:23 PM, Diego Novillo <dnovillo at google.com> wrote:> 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.That sounds plausible to me. It seems like we might need a way of representing call graph profiling in addition to the existing branch probabilities? FWIW, the greedy register allocator’s live range splitting algorithm is designed to consume profile information so it can push spill code into cold blocks. The primary interface is SpillPlacement::getBlockFrequency() which currently returns an estimate based on loop depth only. /jakob
Chandler Carruth
2013-Jun-12 22:05 UTC
[LLVMdev] RFC - Profile Guided Optimization in LLVM
On Wed, Jun 12, 2013 at 2:55 PM, Jakob Stoklund Olesen <stoklund at 2pi.dk>wrote:> That sounds plausible to me. It seems like we might need a way of > representing call graph profiling in addition to the existing branch > probabilities? >Agreed. An important consideration here is WPO vs. LTO vs. TU-at-a-time call graphs.> FWIW, the greedy register allocator’s live range splitting algorithm is > designed to consume profile information so it can push spill code into cold > blocks. The primary interface is SpillPlacement::getBlockFrequency() which > currently returns an estimate based on loop depth only. >It doesn't use MachineBlockFrequency? If it does, it will get a lot more than loop depth: __builtin_expect, cold function attribute, and static branch heuristics. If it doesn't it should, and then it will immediately benefit from this. -------------- next part -------------- An HTML attachment was scrubbed... URL: <http://lists.llvm.org/pipermail/llvm-dev/attachments/20130612/a851059d/attachment.html>
Xinliang David Li
2013-Jun-12 23:26 UTC
[LLVMdev] RFC - Profile Guided Optimization in LLVM
> > 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. >For those who are not familiar with what autoFDO is -- Auto FDO is originally called Sample Based FDO. Its main author is Dehao Chen @google, and Robert Hundt is the one of the main pushers of technology in Google. The latest incarnation of this technology uses LBR events available on Nehalem and above. http://www.computer.org/csdl/trans/tc/2013/02/ttc2013020376-abs.html Cheers, David -------------- next part -------------- An HTML attachment was scrubbed... URL: <http://lists.llvm.org/pipermail/llvm-dev/attachments/20130612/bef348f4/attachment.html>
Bob and I were discussing this over lunch yesterday and he has some very good ideas, so I've CC'ed him to make sure he joins the thread. Chad On Jun 12, 2013, at 2:32 PM, Diego Novillo <dnovillo at google.com> wrote:> 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. > > _______________________________________________ > LLVM Developers mailing list > LLVMdev at cs.uiuc.edu http://llvm.cs.uiuc.edu > http://lists.cs.uiuc.edu/mailman/listinfo/llvmdev-------------- next part -------------- An HTML attachment was scrubbed... URL: <http://lists.llvm.org/pipermail/llvm-dev/attachments/20130614/e01929f9/attachment.html>
Krzysztof Parzyszek
2013-Jun-14 17:56 UTC
[LLVMdev] RFC - Profile Guided Optimization in LLVM
On 6/12/2013 4:55 PM, Jakob Stoklund Olesen wrote:> > It seems like we might need a way of representing call graph profiling in addition to the existing branch probabilities? >Metadata on function definitions/declarations perhaps? I agree---the call graph profiling is of critical importance. I guess we should first see what types of data we already know may be useful and see how we would represent them within the compiler. Here's my list (in addition to the usual data gathered in such cases): * Function call frequency: - How many times a function has been called? * Function argument value profiling: - Is the function called with a small set of values for the parameters? - Is there any value that is particularly frequent for any of the arguments? - If the function takes pointers, are the pointers usually aligned? - If the function takes pointers, are they aliased? - etc. * Branch profiling: - Is the branch executed (i.e. taken/not-taken) in the same way as the last time, or does it often change direction? * Path profiling: - Correlation between branches, function calls, etc. -Krzysztof -- Qualcomm Innovation Center, Inc. is a member of Code Aurora Forum, hosted by The Linux Foundation