[llvm-dev] RFC: PGO Late instrumentation for LLVM

Justin Bogner via llvm-dev llvm-dev at lists.llvm.org
Tue Aug 11 09:38:39 PDT 2015

Rong Xu via llvm-dev <llvm-dev at lists.llvm.org> writes:
> Instrumentation based Profile Guided Optimization (PGO) is a compiler
> technique that leverages important program runtime information, such as
> precise edge counts and frequent value information, to make frequently
> executed code run faster. It's proven to be one of the most effective ways to
> improve program performance.
> An important design point of PGO is to decide where to place the
> instrumentation. In current LLVM PGO, the instrumentation is done in the Clang
> Front-End (will be referred as FE based instrumentation). Doing this early in
> the front-end gives better coverage information as the there are more precise
> line number information. The resulted profile also has relatively high
> tolerance to compiler changes. All the compiler change after the
> instrumentation point will not lead to mismatched IR that invalidates the
> profile.
> On the other hand, doing this too early gives the compiler fewer opportunities
> to optimize the code before instrumentation. It has significant impact on
> instrumentation runtime performance. In addition, it tends to produce larger
> binary and  profile data file.  Our internal C++ benchmarks shows that FE
> based instrumentation degrades the performance (compared to non-instrumented
> version) by 58.3%, and in some extreme cases, the application speed/throughput
> decreases by 95% (21x runtime slowdown).

While I agree that we can and should improve the overhead of profiling
instrumentation, I think that there's a lot of low hanging fruit in
improving the frontend instrumentation that makes some of the
comparisons below misleading.

The cost of maintaining two separate ways of profiling is fairly high,
so we really have to be sure that the gains are worth it if we're going
to go this route.

> Running the instrumented binary too slow is not desirable in PGO for the
> following reasons:
>    * This slows down already lengthy build time. In the worst case, the
> instrumented binary is so slow that it fails to run a representative workload,
> because slow execution can leads to more time-outs in many server programs.
> Other typical issues include: text size too big, failure to link instrumented
> binaries, memory usage exceeding system limits.
>    * Slow runtime affects the program behavior. Real applications sometimes
> monitor the program runtime and have different execution path when the program
> run too slow. This would defeat the underlying assumption of PGO and make it
> less effective.
> This work proposes an option to turn on middle-end based instrumentation (new)
> that aims to speed up instrumentation runtime. The new instrumentation is
> referred to as ME based instrumentation in this document. Our experimental
> results show that ME instrumentation can speed up the instrumentation by 80%
> on average for typical C++ programs. Here are the two main design objectives:
>    * Co-existing with FE Instrumenter: We do not propose to replace the FE
> based instrumentation because FE based instrumentation has its advantages and
> applications. User can choose which phase to do instrumentation via command
> line options.
>    * PGO Runtime Support Sharing: The ME instrumenter will completely re-use
> the existing PGO’s runtime support.
> 1. FE Based Instrumentation Runtime Overhead Analysis
> Instrumented binaries are expected to run slower due to instrumentation code
> inserted. With FE based instrumentation, the overhead is especially high and
> runtime slowdown can be unacceptable in many cases.  Further analysis shows
> that there are 3  important factors  contributing to FE instrumentation
> slowdown :
>    * [Main] Redundant counter updates of inlined functions. C++
>      programs can introduce large abstraction penalties by using lots of
>      small inline functions (assignment operators, getters, setters,
>      ctors/dtors etc). Overhead of instrumenting those small functions
>      can be very large, making training runs too slow and in some cases
>      to usable;

As Sean pointed out, this problem might be solveable in other ways.
Beyond the naive approach of simply ignoring small functions with a
certain hotness that he suggested, the frontend instrumentation could be
easily tweaked to do better in these cases in a couple of ways.

1. It's pretty easy to recognize classes of function in the frontend. It
   would be trivial to optionally ignore getter-style functions or
   simple constructors, or whatever. This loses some information that's
   useful for the coverage use case, but not in a very harmful way.

2. These will generally be dominated by a counter update in the calling
   function. We could insert a pass at some point that recognizes redundant
   calls to instrprof.increment and combines them. This combine could
   even keep the fidelity of all of the counters by creating extra
   symbolic counters that map to multiple updates, if we're willing to
   pay the space cost.

>    * Non-optimal placement of the count updates;

A lot of this has quite a bit of room for improvement in the frontend
instrumentation. More on this below.

>    * A third factor is related value profiling (to be turned on in the
>      future). Small and hot callee functions taking function pointer
>      (callbacks) can incur overhead due to indirect call target
>      profiling.

Can you elaborate on this? I can't see how a feature we don't have yet
can contribute to the slowdown, and I don't really understand how the
late instrumentation would help with whatever the hypothetical problem

> 1.1 Redundant Counter Update
> If checking the assembly of the instrumented binary generated by current LLVM
> implementation, we can find many sequence of consecutive 'incq' instructions
> that updating difference counters in the same basic block. As an example that
> extracted from real binary:
>   ...
>  incq   0xa91d80(%rip)        # 14df4b8
> <__llvm_profile_counters__ZN13LowLevelAlloc5ArenaC2Ev+0x1b8>
>  incq   0xa79011(%rip)        # 14c6750
> <__llvm_profile_counters__ZN10MallocHook13InvokeNewHookEPKvm>
>  incq   0xa79442(%rip)        # 14c6b88
> <__llvm_profile_counters__ZNK4base8internal8HookListIPFvPKvmEE5emptyEv>
>  incq   0x9c288b(%rip)        # 140ffd8
> <__llvm_profile_counters__ZN4base6subtle12Acquire_LoadEPVKl>
>  ...
> From profile use point of view, many of these counter update are redundant.
> Considering the following example:
> void bar(){
>  sum++;
> }
> void foo() {
>  bar();
> }
> FE based instrumentation needs to insert counter update for the only BB of the
> bar().
> bar:                                    # @bar
> # BB#0:                                 # %entry
>        incq    .L__llvm_profile_counters_bar(%rip)
>        incl    sum(%rip)
>        retq
> It also need to insert the update the BB in function foo().  After inlining
> bar to foo(), the code is:
> foo:                                    # @foo
> # BB#0:                                 # %entry
>        incq    .L__llvm_profile_counters_foo(%rip)
>        incq    .L__llvm_profile_counters_bar(%rip)
>        incl    sum(%rip)
>        retq
> If bar() should be always inlined, .L__llvm_profile_counters_bar(%rip) is
> redundant -- the counter won't help downstream optimizations. On the other
> hand, if bar() is a large function and may not be suitable to be inlined for
> all callsites, this counter updated is necessary in order to produce more
> accurate profile data for the out-of-line instance of the callee.
> If foo() is a hot function, the overhead of updating two counters can be
> significant. This is especially bad for C++ program where there are tons of
> small inline functions.

On the other hand, unless foo is bar's only caller, you're losing
information by dropping one of these counters. How do you determine when
this transformation is safe?

> There is another missing opportunity in FE based instrumentation. The small
> functions’ control flow can usually be simplified when they are inlined into
> caller contexts. Once the control flow is simplified, many counter updates can
> therefore be eliminated. This is only possible for a middle end based late
> instrumenter. Defining a custom clean-up pass to remove redundant counter
> update is unrealistic and cannot be done in a sane way without destroying the
> profile integrity of neither the out-of-line nor inline instances of the
> callee.

Wait, but doing it this way destroys the profile integrity of one of
those anyway - it isn't updating all of the counters. How is doing this
in a separate pass any different?

> A much simpler and cleaner solution is to do a pre-inline pass to inline all
> the trivial inlines before instrumentation.  In addition to removing the
> unnecessary count updates for the inline instances,  another advantage of
> pre-inline is to  provide context sensitive profile for these small inlined
> functions. This context senstive profile can further improve the PGO based
> optimizations. Here is a contrived example:
> void bar (int n) {
>   if (n&1)
>     do_sth1();
>   else
>     do_sth2();
> }
> void caller() {
>   int s = 1;
>   for (; s<100; s+=2)
>     bar(s);
>   for (s = 102; s< 200; s+=2)
>     bar(s);
> }
> The direction of the branch inside bar will be totally opposite between two
> different callsites in ‘caller’. Without pre-inlining, the branch probability
> will be 50-50 which will be useless for later optimizations. With
> pre-inlining, the profile will have the perfect branch count for each
> callsite. The positive performance impact of context sensitive profiling due
> to pre-inlining has been observed in real world large C++ programs. Supporting
> context sensitive profiling is another way to solve this, but it will
> introduce large additional runtime/memory overhead.

So this a pretty interesting transformation. Do you have any data on
exactly how large the overhead of doing this with a context sensitive
approach would be, or how much extra complexity would entail?

> 1.2 Non-optimal placement of count update
> Another much smaller showdown factor is the placement of the counter updates.
> Current front-end based instrumentation applies the instrumentation to each
> front-end lexical construct. It also minimizes the number of static
> instrumentations. Note that it always instruments the entry count of the CFG.
> This may result in higher dynamic instruction counts. For example,
>      BB0
>      | 100
>     BB1
> 90 /   \ 10
>   BB2  BB3
> 90 \   / 10
>     BB4
> Like the the above example, FE based instrumentation will always insert count
> update in BB0.  The dynamic instrumentation count will be either 110
> (Instrument bb0->bb1 and bb1->bb2) or 190 (bb0->bb1 and bb1->bb3). A better
> instrumentation is to instrument (bb1->bb2 and bb1->bb3) where the dynamic
> instrumentation count is 100.
> Our experimental shows that the optimal placement based on edge hotness can
> improve instrumented code performance by about 10%.  While it’s hard to find
> the optimal placement of count update,  compiler heuristics can be used the
> get the better placement. These heuristics  can be based on static profile
> prediction or user annotations (like __buildin_expect) to  estimate the
> relative edge hotness and put instrumentations on the less hot edges. The
> initial late instrumentation has not fully implemented this placement strategy
> yet.  With that implemented, we expect even better results than what is
> reported here. For real world programs, another major source of the slowdown
> is the data racing and false sharing of the counter update for highly threaded
> programs. Pre-inlining can alleviate this problem as the counters in the
> inline instances are not longer shared. But the complete solution to the data
> racing issue is orthogonal to the problem we try to solve here.

I suspect most of these heuristics can be applied to the frontend
approach today. User annotations are visible to the frontend by
definition, so we can pretty easily modify our counter placement based
on those. Using previous profiles introduces a need to record somewhere
why we made an "abnormal" decision, but that problem exists with the
late instrumentation as well.

> 2. High Level Design
> We propose to perform a pre-profile inline pass before the PGO instrumentation
> pass. Since the instrumentation pass is after inine, it has to be done in the
> middle-end.
> (1) The pre-inline pass
> We will invoke a pre-inline pass before the instrumentation. When PGO is on,
> the inlining will be split into two passes:
>    * A pre-inline pass that is scheduled before the profile instrumentation/
> annotation
>    * A post-inline pass which is the regular inline pass after instrumentation
> /annotation
> By design, all beneficial callsites without requiring profile data should be
> inlined in the pre-inline pass. It includes all callsites that will shrink
> code size after inlining. All the remaining callsites will be left to the
> regular inline pass when profile data is available.
> After pre-inline, a CFG based profile instrumentation/annotation will be done.
> A minimum weight spanning tree (MST) in CFG is first computed, then only the
> edges not in the MST will be instrumented. The counter update instructions are
> placed in the basic blocks.
> (2) Minimum Spanning Tree (MST) based instrumentation
> A native way of instrumentation is to insert a count update for every edge in
> CFG which will result in  too many redundant updates that makes the runtime
> very slow. Knuth [1] proposed a minimum spanning tree based method: given a
> CFG, first compute a spanning tree. All edges that not in the MST will be
> instrumented. In the profile use compilation, the counters are populated (from
> the leaf of the spanning tree) to all the edges. Knuth proved this method
> inserts the minimum number of instrumentations. MST based method only
> guarantees the number static instrumentation are minimized, not the dynamic
> instance of instrumentation. To reduce the number of dynamic instrumentation,
> edges of potentially high counts will be put into MST first so that they will
> have less chance to be instrumented.
> 3. Experimental Results
> 3.1 Measurement of the efficiency of instrumentation
> Other than the runtime of the instrumented binaries, a more direct measurement
> of the instrumentation overhead is the the sum of the raw profile count
> values. Note that regardless what kind of instrumentations are used, the raw
> profile count should be able to reconstruct all the edge count values for the
> whole program. All raw profile value are obtained via incrementing the counter
> variable value by one. The sum of the raw profile count value is roughly the
> dynamic instruction count of the instrumented code. The lower of the value,
> the more efficient of the instrumentation.
> 3.2 LLVM instrumentations runtime for SPEC2006 C/C++ programs and SPEC2K eon
> The performance speedup is computed by (FE_instrumentation_runtime /
> ME_instrumentation_runtime - 1)
> We run the experiments on all C/C++ programs in SPEC2006 and 252.eon from
> SPEC2000. For C programs, except for one outlier 456.hmmer, there are small
> ups and downs across different programs. Late instrumentation improves hmmer a
> lot, but it is probably due to unrelated loop optimizations (90% of runtime
> spent in one loop nest).
> For C++ programs, the performance impact of late instrumentation is very
> large, which is as expected. The following table shows the result.  For some
> C++ program, the time speedup is huge. For example, in  483.xalancbmk, late
> instrumentation speeds up performance by ~60%.  Among all the SPEC C++
> programs, only 444.namd is an outlier -- it uses a lot of macros and is a very
> C like program.
> Program           Speedup
> 471.omnetpp       16.03%
> 473.astar          5.00%
> 483.xalancbmk     58.57%
> 444.namd          -0.90%
> 447.dealII        60.47%
> 450.soplex         8.20%
> 453.povray        11.34%
> 252.eon           35.33%
> -------------------------
> Geomean           21.01%
> 3.3 Statistics of LLVM profiles for SPEC2006 C/C++ programs
> We also collect some statistic of the profiles generated by FE based
> instrumentation and late instrumentation, namely, the following information:
>    1. the number of functions that being instrumented,
>    2. the result profile file size,
>    3. the sum of raw count values that was mentioned earlier -- we used it to
> measure the efficiency of the instrumentation.
> Next table shows the ratios of the each metrics by late instrumentation for
> the C++ programs, with FE based instrumentation as the base : column (1) shows
> the ratios of instrumented functions; column (2) shows the ratios of the
> profile file size; column (3) shows the ratios of the sum of raw count values.
>                 (1)       (2)       (3)
> 471.omnetpp    85.36%   110.26%    46.52%
> 473.astar      64.86%    72.72%    63.13%
> 483.xalancbmk  51.83%    56.11%    35.77%
> 444.namd       75.36%    82.82%    85.77%
> 447.dealII     43.42%    46.46%    26.75%
> 450.soplex     71.80%    87.54%    51.19%
> 453.povray     78.68%    83.57%    64.37%
> 252.eon        72.06%    91.22%    30.02%
> ----------------------------------------
> Geomean        66.50%    76.36%    47.01%
> For FE based instrumentation, profile count variables generated for the dead
> functions will not be removed (like __llvm_prf_names, __llvm_prf_data,  and
> __llvm_prf_cnts) from the data/text segment, nor in the result profile. There
> is a recent patch that removes these unused data for COMDAT functions, but
> that patch won’t touch regular functions. This is the main reason for the
> larger number of instrumented functions and larger profile file size for the
> FE based instrumentation. The reduction of the sum of raw count values is
> mainly due to the elimination of redundant profile updates enabled by the
> pre-inlining.
> For C programs, we observe similar improvement in the profile size (geomean
> ratio of 73.75%) and smaller improvement in the number of instrumented
> functions (geomean ratio of 87.49%) and the sum of raw count values (geomean
> of 82.76%).
> 3.4 LLVM instrumentations runtime performance for Google internal C/C++
> benchmarks
> We also use Google internal benchmarks (mostly typical C++ applications) to
> measure the relative performance b/w FE based instrumentation and late
> instrumentation.  The following table shows the speedup of late
> instrumentation vs FE based instrumentation. Note that C++benchmark01 is a
> very large multi-threaded C++ program. Late instrumentation sees 4x speedup.
> Larger than 3x speedups are also seen in many other programs.
> C++_bencharmk01    416.98%
> C++_bencharmk02      6.29%
> C++_bencharmk03     22.39%
> C++_bencharmk04     28.05%
> C++_bencharmk05      2.00%
> C++_bencharmk06    675.89%
> C++_bencharmk07    359.19%
> C++_bencharmk08    395.03%
> C_bencharmk09       15.11%
> C_bencharmk10        5.47%
> C++_bencharmk11      5.73%
> C++_bencharmk12      2.31%
> C++_bencharmk13     87.73%
> C++_bencharmk14      7.22%
> C_bencharmk15       -0.51%
> C++_bencharmk16     59.15%
> C++_bencharmk17    358.82%
> C++_bencharmk18    861.36%
> C++_bencharmk19     29.62%
> C++_bencharmk20     11.82%
> C_bencharmk21        0.53%
> C++_bencharmk22     43.10%
> ---------------------------
> Geomean             83.03%
> 3.5 Statistics of LLVM profiles for Google internal benchmarks
> The following shows the profile statics using Google internal benchmarks.
>                          (1)       (2)       (3)
> C++_bencharmk01         36.84%    40.29%     2.32%
> C++_bencharmk02         39.20%    40.49%    42.39%
> C++_bencharmk03         39.37%    40.65%    23.24%
> C++_bencharmk04         39.13%    40.68%    17.70%
> C++_bencharmk05         36.58%    38.27%    51.08%
> C++_bencharmk06         29.50%    27.87%     2.87%
> C++_bencharmk07         29.50%    27.87%     1.73%
> C++_bencharmk08         29.50%    27.87%     4.17%
> C_bencharmk09           53.95%    68.00%    11.18%
> C_bencharmk10           53.95%    68.00%    31.74%
> C++_bencharmk11         36.40%    37.07%    46.12%
> C++_bencharmk12         38.44%    41.90%    73.59%
> C++_bencharmk13         39.28%    42.72%    29.56%
> C++_bencharmk14         38.59%    42.20%    13.42%
> C_bencharmk15           57.45%    48.50%    66.99%
> C++_bencharmk16         36.86%    42.18%    16.53%
> C++_bencharmk17         37.82%    39.77%    13.68%
> C++_bencharmk18         37.82%    39.77%     7.96%
> C++_bencharmk19         37.52%    40.46%     1.85%
> C++_bencharmk20         32.37%    30.44%    19.69%
> C_bencharmk21           37.63%    40.42%    88.81%
> C++_bencharmk22         36.28%    36.92%    21.62%
> --------------------------------------------------
> Geomean                 38.22%    39.96%    15.58%
> 4. Implementation Details:
> We need to add new option(s) for the alternative PGO instrumentation pass in
> the middle end. It can in one of the following forms:
>    (1) Complete new options that are on par with current PGO options:
> -fprofile-late-instr-generate[=<profile_file>]? for PGO Instrumentation, and
> -fprofile-late-instr-use[=<profile_file>]? for PGO USE.
>    (2) Or, late instrumentation can be turned on with an additional option
> -fprofile-instr-late with current PGO options. I. e. -fprofile-instr-late
> -fprofile-instr-generate[=<profile_file>]? for PGO instrumentation, and
> -fprofile-instr-late -fprofile-instr-use[=<profile_file>]? for PGO use.
>    (3) Alternatively to (2), only keep -fprofile-instr-late option in PGO
> instrumentation. Adding a magic tag in profile so that FE based profile and
> late instrumented profile can be automatically detected by profile loader In
> PGO use compilation. This requires a slight profile format change.
> In our prototype implementation, two new passes are added in the beginning of
> PassManagerBuilder::populateModulePassManager(), namely PreProfileInlinerPass
> and PGOLateInstrumentationPass.
> 4.1 Pre-inline pass:
> It is controlled by back-end option "-preinline" and "-disable-preinline". If
> the user specifies any llvm option of "-fprofile-late-instr-{generate|use},
> option "-mllvm -preinline" will be automatically inserted in the driver.. To
> disable the pre-inliner when late instrumentation is enabled, use option
> "-mllvm -disable-preinline".
> For now, only minimum tuning is done for the pre-inliner, which simply adjusts
> the inline threshold: If -Oz is specified, the threshold is set to 25.
> Otherwise, it is 75.
> The following clean up passes are added to PassManager, right after the
> PreProfileInline pass:
>   createEarlyCSEPass()
>   createJumpThreadingPass()
>   createCorrelatedValuePropagationPass()
>   createCFGSimplificationPass()
>   createInstructionCombiningPass()
>   createGVNPass(DisableGVNLoadPRE)
>   createPeepholePASS()
> Some of them might not be necessary.
> 4.2 Late Instrumentation Pass:
> The late instrumentation is right after the pre-inline pass and it's cleanup
> passes. It is controlled by opt option "-pgo-late-instr-gen" and
> "-pgo-late-instr-use". For "-pgo-late-instr-use" option, the driver will
> provide the profile name.
> For "-pgo-late-instr-gen", a pass that calls createInstrProfilingPass() is
> also added to PassManager to lower the instrumentation intrinsics 
> PGOLateInstrumeatnion is a module pass that applies the instrumentation to
> each function by class PGOLateInstrumentationFunc. For each function, perform
> the following steps:
>    1. First collect all the CFG edges. Assign an estimated weight to each
> edge. Critical edges and back-edges are assigned to high value of weights. One
> fake node and a few fake edges (from the fake node to the entry node, and from
> all the exit nodes to the fake node) are also added to the worklist.
>    2. Construct the MST. The edges with the higher weight will be put to MST
> first, unless it forms a cycle.
>    3. Traverse the CFG and compute the CFG hash using CRC32 of the index of
> each BB.
> The above three steps are the same for profile-generate and profile-use
> compilation.
> In the next step, for profile-generation compilation, all the edges that not
> in the MST are instrumented. If this is a critical edge, split the edge first.
> The actual instrumentation is to generate Intrinsic::instrprof_increment() in
> the instrumented BB. This intrinsic will be lowed by pass
> createInstrProfilingPass().
> In the next step, for profile-generation compilation, all the edges that not
> in the MST are instrumented. If this is a critical edge, split the edge first.
> The actual instrumentation is to generate Intrinsic::instrprof_increment() in
> the instrumented BB. This intrinsic will be lowed by pass
> createInstrProfilingPass().
> For -fprofile-use compilation, first read in the counters and the CFG hash
> from the profile file. If the CFG hash matches, populate the counters to all
> the edges in reverse topological order of the MST. Once having all the edge
> counts, set the branch weights metadata for the IR having multiple branches.
> Also apply the cold/hot function attributes based on function level counts.
> 4.3 Profile Format:
> The late instrumentation profile is mostly the same as the one from front-end
> instrument-ion. The difference is
>    * Function checksums are different.
>    * Function entry counts are no longer available.
> For llvm-profdata utility, options -lateinstr needs to be used to
> differentiate FE based and late instrumentation profiles, unless a magic tag
> is added to the profile.

If we do this, we'll probably want to add something to the file to
differentiate the profiles, otherwise the error messages will be pretty

> 5. References:
> [1] Donald E. Knuth, Francis R. Stevenson. Optimal measurement of points for
> program frequency counts. BIT Numerical Mathematics 1973, Volume 13, Issue 3,
> pp 313-322
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