[llvm-dev] Fwd: [RFC] Machine Function Splitter - Split out cold blocks from machine functions using profile data
aditya kumar via llvm-dev
llvm-dev at lists.llvm.org
Wed Aug 5 18:22:25 PDT 2020
Fwd:
On Wed, Aug 5, 2020 at 9:48 AM Xinliang David Li <davidxl at google.com> wrote:
>
>
> On Tue, Aug 4, 2020 at 10:51 PM aditya kumar <hiraditya at gmail.com> wrote:
>
>> Glad to hear that there is an interest in a function splitting pass.
>> There are advantages to splitting functions at different stages as you've
>> already noted.
>>
>
> Right -- with slightly different objectives. Machine Function Splitting
> Pass's main focus is on performance improvement.
>
Yeah, I'm very confident the function splitting will give nice perf
improvements as has been reported by other projects like BOLT. Initial
glance on the patch looks good. Looking forward to some benchmark numbers.
> - Having a target independent function splitting scales well to LTO,
>> ThinLTO, supporting multiple architectures and offers ease of maintenance.
>> - While HCS+merge-function helps significantly reduce the codesize, in
>> many cases the outlined functions tend to have identical function bodies
>> (e.g., assert-fail etc); they can be deduplicated by linker with careful
>> function naming. This reduces code-size regardless of function merging and
>> across the entire program. This technique should also help Machine Function
>> splitter in some cases but at some cost to the link time.
>>
>
> yes -- I think this can also be achieved with the partial inlining pass.
>
Partial inlining can do some of it.
>
> - It will be difficult to reduce argument setup and restore code in the
>> HCS except in some cases like tail call, internal function calls,
>> non-returning function calls etc. Having frame setup however should help
>> with debugging IMO.
>>
>> > Recent work at Google has shown that aggressive, profile-driven
>> inlining for performance has led to significant code bloat and icache
>> fragmentation
>>
>> Because the inliner can be too aggressive at times and can negatively affect icache-miss etc, HCS can be integrated with inliner to assist in partial inlining (For example: split the callee before inlining). Rodrigo (@rcorcs) has some ideas around that and we've been exploring that as part of GSoC project.
>>
>>
> The Inliner can also be hindered without splitting. Partial inlining can
> help a little, but it can be limited because many of the outlining
> opportunities are only exposed after inlining (in inline instances).
> Making inliner Machine Splitting or HCS aware is the way to go. Machine
> Function splitting has an advantage here as it does not need sophisticated
> analysis to figure out what part of code can and can not be split out post
> inlining.
>
>
>> We've performance numbers from Firefox which shows ~5% performance
>> improvement with HCS (cc: Ruijie @rjf). Vedant also reported performance
>> numbers across iOS and Swift benchmarks in the past. I could find (
>> https://github.com/apple/swift/pull/21016) which reported decent
>> performance improvement in core ios Frameworks.
>>
>>
> Nice. If possible, the same performance tests can be done for Machine
> Splitting once the patch is posted :)
>
>
>
>> That said we have been working on improving the cost model which I think
>> will help alleviate many of the limitations that we typically don't have in
>> a Machine Splitting optimization. I'd like to hear your ideas on how the
>> cost model can be improved.
>>
>>
> The partial inliner pass has introduced many cost analysis using profile
> data. I think HCS should probably share those code (as utilities) as the
> nature of the transformation is similar. Cost model can sometimes be quite
> tricky though -- it is hard to compare the cost with the actual benefit
> brought by the splitting. The beauty of machine splitting is that it does
> not depend on sophisticated cost/benefit model.
>
>
>
>> > In contrast, the machine function splitter extracts cold code into a separate
>> section.
>> HCS also adds a section prefix to all the cold functions. It is possible
>> that the cold functions are still in the same section as the hot one
>> depending on the linker. Ruijie has a patch to move all the cold functions
>> to a separate section, we are still evaluating the results (
>> https://github.com/ruijiefang/llvm-hcs/commit/4966997e135050c99b4adc0aa971242af99ba832).
>> In case it is not difficult to rerun the experiments, it'll help to see the
>> numbers with the llvm-trunk and this patch from @rjf.
>>
>> > Furthermore, this may not play well with optimization passes such as
>> MachineOutliner.
>> Can you share an example where HCS and Machine Outliner don't play well
>> together?
>>
>
> One thing I can think of is that Machine Outliner is based on code
> pattern, while HCS also looks at hotness. The inconsistency can lead to
> missing opportunities in machine outliner ?
>
While at a high level it may appear to be true, unless we have a specific
example, it is hard to tell.
>
>> > Synergistic optimizations are harder to reason about due to the pass
>> timing. For example, inlining can be more aggressive if any cold code introduced
>> is trimmed.
>> How does this regress workloads if we have profile information and cold
>> portions of a callee is outlined? Does the inliner always regress workloads
>> we are evaluating?
>>
>>
> Currently inliner only looks at static code size (as cost). We are working
> on improving it.
>
Looking forward to improvements in the inliner, I think that would help
many workloads.
>
>
>> PS: One correction I'd like to make is HCS splits SEME regions (Thanks to
>> Vedant). In case some SEME aren't getting outlined, there are CFG
>> transformations to make them friendly to HCS. I'd love to see such an
>> example, as that'd motivate some of the future work.
>>
>>
> It is common to see multiple entry cold regions post inlining. After
> block layout, we may see long chains of cold blocks with many blocks in the
> chain being jump targets (from hot regions). If there are multiple blocks
> exiting the cold region, we get multiple exits case.
>
Makes sense, these cases will be outlined by HCS AFAICT.
> thanks,
>
> David
>
>
>
>
>
>> Aditya Kumar
>> Compiler Engineer
>> https://bitsimplify.com
>>
>>
>> On Tue, Aug 4, 2020 at 5:31 PM Snehasish Kumar <snehasishk at google.com>
>> wrote:
>>
>>> Greetings,
>>>
>>> We present “Machine Function Splitter”, a codegen optimization pass
>>> which splits functions into hot and cold parts. This pass leverages the
>>> basic block sections feature recently introduced in LLVM from the Propeller
>>> project. The pass targets functions with profile coverage, identifies cold
>>> blocks and moves them to a separate section. The linker groups all cold
>>> blocks across functions together, decreasing fragmentation and improving
>>> icache and itlb utilization. Our experiments show >2% performance
>>> improvement on clang bootstrap, ~1% improvement on Google workloads and
>>> 1.6% mean performance improvement on SPEC IntRate 2017.
>>> Motivation
>>>
>>> Recent work at Google has shown that aggressive, profile-driven inlining
>>> for performance has led to significant code bloat and icache fragmentation (AsmDB
>>> - Ayers et al ‘2019 <https://research.google/pubs/pub48320/>). We find
>>> that most functions 5 KiB or larger have inlined children more than 10
>>> layers deep bringing in exponentially more code at each inline level, not
>>> all of which is necessarily hot. Generally, in roughly half of even the
>>> hottest functions, more than 50% of the code bytes are never executed, but
>>> likely to be in the cache.
>>>
>>> Function splitting is a well known compiler transformation primarily
>>> targeting improved code locality to improve performance. LLVM has a
>>> middle-end, target agnostic hot cold splitting pass
>>> <https://llvm.org/devmtg/2019-10/slides/Kumar-HotColdSplitting.pdf> as
>>> well as a partial inlining pass
>>> <https://github.com/llvm/llvm-project/blob/master/llvm/lib/Transforms/IPO/PartialInlining.cpp>
>>> which performs similar transformations, as noted by the authors in a
>>> recent email thread
>>> <https://lists.llvm.org/pipermail/llvm-dev/2020-June/142429.html>.
>>> However, due to the timing of the respective passes as well as the code
>>> extraction techniques employed, the overall gains on large, complex
>>> applications leave headroom for improvement. By deferring function
>>> splitting to the codegen phase we can maximize the opportunity to remove
>>> cold code as well as refine the code extraction technique. Furthermore, by
>>> performing function splitting very late, earlier passes can perform more
>>> aggressive optimizations.
>>> Implementation
>>>
>>> We propose a new machine function splitting pass which leverages the basic
>>> block sections feature <https://reviews.llvm.org/D68063> to split
>>> functions without the caveats of code extraction in the middle-end. The
>>> pass uses profile information to identify cold basic blocks very late in
>>> LLVM CodeGen, after regalloc and all other machine passes have executed.
>>> This allows our implementation to be precise in its assessment of cold
>>> regions while maximizing opportunity.
>>>
>>> Each function is split into two parts. The hot cluster includes the
>>> function entry and all blocks which are not cold. All the cold blocks are
>>> grouped together as a Cold Section cluster
>>> <https://github.com/llvm/llvm-project/blob/5934df0c9abe94fc450fbcf0ceca21cf838840e9/llvm/include/llvm/CodeGen/MachineBasicBlock.h#L63>.
>>> With basic block sections, the cold blocks are assigned appropriate debug
>>> and call frame information and emitted as part of the .text.unlikely
>>> section. Unlike Propeller
>>> <https://lists.llvm.org/pipermail/llvm-dev/2019-September/135393.html>,
>>> which is presently the main user of the basic block sections feature, this
>>> pass does not require an additional round of profiling and uses existing
>>> instrumentation based FDO or CSFDO profile information.
>>>
>>>
>>>
>>> In the illustration above, the functions foo and bar contain a cold
>>> block each, index 5 and E respectively. We show a possible layout for these
>>> functions which optimizes for fall throughs. Note that all the blocks are
>>> kept in a contiguous region described by the symbols foo and bar. Using the
>>> machine function splitter, the cold blocks (5 and E) are moved to a
>>> separate section. These blocks can then be grouped along with other cold
>>> blocks (and functions) in a separate output section in the final binary.
>>> The key highlights of this approach are:
>>>
>>> -
>>>
>>> Profile driven, profile type agnostic approach.
>>> -
>>>
>>> Cold basic blocks are split out using jumps.
>>> -
>>>
>>> No additional instructions are added to the function for
>>> setup/teardown.
>>> -
>>>
>>> Runs as the last step before emitting assembly, no
>>> analysis/optimizations are hindered.
>>>
>>>
>>> Exceptions
>>>
>>> All eh pads are grouped together regardless of their coldness and are
>>> part of the original function. There are outstanding issues with splitting
>>> eh pads if they reside in separate sections in the binary. This remains as
>>> part of future work.
>>>
>>> DebugInfo and CFI
>>>
>>> Debug information and CFI directives are updated and kept consistent by
>>> the underlying basic block sections framework. Support added in the
>>> following patches
>>>
>>> -
>>>
>>> DebugInfo (https://reviews.llvm.org/D78851)
>>> -
>>>
>>> CFI (https://reviews.llvm.org/D79978).
>>>
>>>
>>>
>>> Distinction between Machine Function Splitter and Propeller
>>>
>>>
>>> Full Propeller optimizations include function splitting and layout
>>> optimizations, however it requires an additional round of profiling using
>>> perf on top of the peak (FDO/CSFDO + ThinLTO) binary. In this work we
>>> experiment with applying function splitting using the instrumented profile
>>> in the build instead of adding an additional round of profiling.
>>>
>>> Link to Propeller RFC
>>> <https://lists.llvm.org/pipermail/llvm-dev/2019-September/135393.html>
>>>
>>>
>>> Split Binary Characteristics
>>>
>>> Binaries produced by the compiler with function splitting enabled
>>> contain additional symbols. A function which has been split into a hot and
>>> cold part is non-contiguous. The symbol table entry for the hot part
>>> retains the symbol name of the original function with type FUNC. The symbol
>>> for the cold part contains a “.cold” suffix attached to the original symbol
>>> name, the type is not set for this symbol. Using a suffix has been the norm
>>> for such optimizations e.g. -hot-cold-split in LLVM and the prior GCC
>>> implementation detailed earlier. We expect standardized tooling to handle
>>> split functions appropriately, e.g demangling works as expected --
>>>
>>> $ c++filt _Z3foov.cold
>>>
>>> foo() [clone .cold]
>>>
>>> Contrast with HotColdSplit (HCS)
>>>
>>> Function splitting in the middle-end in LLVM employs extraction of cold
>>> single-entry-single-exit (SESE) regions into separate functions. In
>>> general, the pass has been found to be impactful in reducing code size by
>>> deduplication of cold regions; however our experiments show it does not
>>> improve performance of large workloads.
>>>
>>> The key differences are:
>>>
>>> Extraction methodology and tradeoffs
>>>
>>> HCS extracts cold code from SESE regions using a function call. This may
>>> incur a spill and fill of caller registers along with additional setup and
>>> teardown if live values modified in the cold region need to be communicated
>>> back to the original function. This has a couple of implications
>>>
>>> 1.
>>>
>>> The “residue” of each extracted region is non-trivial and there is a
>>> tradeoff between the amount of code that needs to be cold before it is
>>> profitable to extract. Thus the cost of mischaracterization is high.
>>> 2.
>>>
>>> Since each SESE region is extracted separately the net reduction in
>>> code size of the original function is less.
>>>
>>>
>>> In contrast, the machine function splitter extracts cold code into a
>>> separate section. Control is transferred to cold code via jumps. More often
>>> than not these jumps may already exist as part of the original layout thus
>>> incurring no additional cost. No additional instructions are inserted to
>>> accommodate splitting. Finally, no additional setup/teardown is necessary
>>> for live values modified in cold regions.
>>>
>>> Pass timing and interaction with other optimizations
>>>
>>> The HCS pass is run on the IR in the optimizer. This allows it to be
>>> target agnostic and allow later stages to merge identical code if
>>> necessary. However, there are some drawbacks to this approach. In
>>> particular,
>>>
>>> 1.
>>>
>>> Splitting early may miss opportunities introduced by later passes
>>> such as library call inlining and CFG simplification resulting from a
>>> combination of optimizations. Furthermore, this may not play well with
>>> optimization passes such as MachineOutliner.
>>> 2.
>>>
>>> Synergistic optimizations are harder to reason about due to the pass
>>> timing. For example, inlining can be more aggressive if any cold code
>>> introduced is trimmed.
>>>
>>>
>>> In contrast, the machine function splitter runs as the last step in
>>> codegen. This ensures that the opportunity for splitting is maximised
>>> without hindering existing analyses and synergistic decisions can be made
>>> in earlier optimization passes. We rely on accurate profile count
>>> propagation across optimizations to maximise opportunities. This works
>>> particularly well for instrumented profiles while improving the pass for
>>> sampled profiles is ongoing work.
>>>
>>> We have provided a contrived example in the Appendix which demonstrates
>>> the code generated for both approaches. The key differences are highlighted
>>> inline.
>>>
>>> Evaluation
>>>
>>> In this section, we present an in-depth evaluation of the impact on
>>> clang bootstrap and summary results for two google internal workloads,
>>> Search1 and Search2 as well overall results on the SPECInt 2017 benchmarks.
>>> All experiments are conducted on Intel Skylake based systems unless
>>> otherwise noted. Profile guided optimizations using instrumented profiles
>>> are enabled for all builds.
>>>
>>> clang-bootstrap
>>>
>>> We pick 500 compiler invocations from a bootstrap build of clang and
>>> then evaluate the performance of a PGO+ThinLTO optimized version with that
>>> of PGO+ThinLTO+Split compiler. For the latter, the final optimized build
>>> includes the machine function splitter.
>>>
>>> Results:
>>>
>>> We observe a mean 2.33% improvement in end to end runtime. The
>>> improvements in runtime are driven by reduction in icache and TLB miss
>>> rates. The table below summarizes our experiment, each data point is
>>> averaged over multiple iterations. The observed variation for each metric
>>> is < 1%.
>>>
>>> Event
>>>
>>> Split (MPKI)
>>>
>>> Baseline (MPKI)
>>>
>>> % Reduction
>>>
>>> itlb_miss
>>>
>>> 0.87
>>>
>>> 1.28
>>>
>>> 31.70
>>>
>>> stlb_miss
>>>
>>> 0.08
>>>
>>> 0.12
>>>
>>> 32.51
>>>
>>> l1i_miss
>>>
>>> 5.98
>>>
>>> 6.61
>>>
>>> 9.56
>>>
>>> l2_miss
>>>
>>> 0.27
>>>
>>> 0.34
>>>
>>> 20.02
>>>
>>> In this experiment, the function splitting pass moved cold code from
>>> ~30K functions in .text and .text.hot. We present a comparison of the
>>> binary contents using bloaty <https://github.com/google/bloaty>
>>>
>>>
>>> FILE SIZE VM SIZE
>>>
>>> -------------- --------------
>>>
>>> +23% +8.26Mi +23% +8.26Mi .text.unlikely
>>>
>>> +6.5% +761Ki [ = ] 0 .strtab
>>>
>>> +4.8% +247Ki +4.8% +247Ki .eh_frame
>>>
>>> +6.1% +193Ki [ = ] 0 .symtab
>>>
>>> +8.5% +63.1Ki +8.5% +63.1Ki .eh_frame_hdr
>>>
>>> +0.3% +31.3Ki +0.3% +31.3Ki .rodata
>>>
>>> +0.4% +3 [ = ] 0 [Unmapped]
>>>
>>> -0.3% -8 -0.3% -8 .init_array
>>>
>>> [ = ] 0 -33.3% -8 [LOAD #4 [RW]]
>>>
>>> [ = ] 0 -0.2% -416 .bss
>>>
>>> -57.1% -4.04Mi -57.1% -4.04Mi .text.hot
>>>
>>> -48.4% -4.13Mi -48.4% -4.13Mi .text
>>>
>>> +1.6% +1.35Mi +0.6% +430Ki TOTAL
>>>
>>> We see that 48% and 57% of code in .text and .text.hot respectively was
>>> moved to the .text.unlikely section. We also note a small increase in
>>> overall binary size due to the following reasons:
>>>
>>> -
>>>
>>> Some additional jump instructions may be inserted.
>>> -
>>>
>>> Small increase in associated metadata, e.g. debug information.
>>> -
>>>
>>> Additional symbols of type foo.cold for cold parts.
>>> -
>>>
>>> Alignment requirements for both original and split function parts.
>>>
>>>
>>> Comparison with HotColdSplit
>>>
>>> For the clang-bootstrap benchmark we also compared the performance of
>>> the hot-cold-split pass with split-machine-functions. We summarize the
>>> results for performance and the characteristics of the binary built by each
>>> pass in the table below. Each metric is presented as change vs the
>>> baseline, an FDO optimized build of clang.
>>>
>>>
>>> Hot Cold Split
>>>
>>> Machine Function Splitter
>>>
>>> Performance
>>>
>>> 1.10%
>>>
>>> 2.65%
>>>
>>> .text size
>>>
>>> -41.5% -2.89Mi
>>>
>>> -49.2% -3.43Mi
>>>
>>> .text.hot size
>>>
>>> -46.9% -2.52Mi
>>>
>>> -57.1% -3.07Mi
>>>
>>> Full binary size
>>>
>>> 9.6% +7.56Mi
>>>
>>> 1.7% +1.37Mi
>>>
>>> Note that the increase in overall binary size increase for HCS is due to
>>> the increase in .eh_frame (+61% +3.03Mi). HCS extracts each cold SESE
>>> region as a separate function whereas the machine function splitter
>>> extracts the cold code as a single region thus incurring a constant
>>> overhead per function.
>>>
>>> Google workloads
>>>
>>> We evaluated the impact of function splitting on a couple of search
>>> workloads, Search1 and Search2. A key difference with respect to the clang
>>> experiment above is the use of huge pages for code. Overall, we find that
>>> on Intel Skylake the key benefit is from reduction of iTLB misses whereas
>>> on AMD the key benefit is from the reduction of icache misses. This is due
>>> to the fewer iTLB entries available for hugepages on Intel architectures.
>>> We find that overall throughput for Search1 and Search2 improve between
>>> 0.8% to 1.2%; a significant improvement on these benchmarks. The workloads
>>> are built with FDO and CSFDO respectively. On Intel Skylake, iTLB misses
>>> reduce by 16% to 35%, sTLB misses reduce by 62% to 67%. On AMD, L1 icache
>>> misses improve by 1.2% to 2.6% whereas L2 instruction misses improve by
>>> 4.8% to 5.1%.
>>>
>>> Comparison with HotColdSplit
>>>
>>> An evaluation of the hot-cold-split pass did not yield performance
>>> improvements on google workloads.
>>>
>>> SPECInt 2017
>>>
>>> We evaluated the impact of the machine function splitter on SPECInt 2017
>>> using the int rate metrics. Overall, we found a 1.6% geomean intrate
>>> improvement for the benchmarks where performance improved (500.perlbench_r,
>>> 502.gcc_r, 505.mcf_r, 520.omnetpp_r). For the benchmarks that didn’t
>>> improve performance, the average degradation was 0.6% (523.xalancbmk_r,
>>> 525.x264_r, 531.deepsjeng_r, 541.leela_r).
>>>
>>> We note that the instruction footprint of SPEC workloads are smaller
>>> than most modern workloads and our work is primarily focused on reducing
>>> the footprint to improve performance. These experiments were performed on
>>> Intel Haswell machines.
>>>
>>> Appendix
>>>
>>> Example to illustrate hot-cold-split and split-machine-functions
>>>
>>> Input IR
>>>
>>> ```
>>>
>>> @i = external global i32, align 4
>>>
>>> define i32 @foo(i32 %0, i32 %1) nounwind !prof !1 {
>>>
>>> %3 = icmp eq i32 %0, 0
>>>
>>> br i1 %3, label %6, label %4, !prof !2
>>>
>>> 4: ; preds = %2
>>>
>>> %5 = call i32 @L1()
>>>
>>> br label %9
>>>
>>> 6: ; preds = %2
>>>
>>> %7 = call i32 @R1()
>>>
>>> %8 = add nsw i32 %1, 1
>>>
>>> br label %9
>>>
>>> 9: ; preds = %6, %4
>>>
>>> %10 = phi i32 [ %1, %4 ], [ %8, %6 ]
>>>
>>> %11 = load i32, i32* @i, align 4
>>>
>>> %12 = add nsw i32 %10, %11
>>>
>>> store i32 %12, i32* @i, align 4
>>>
>>> ret i32 %12
>>>
>>> }
>>>
>>> declare i32 @L1()
>>>
>>> declare i32 @R1() cold nounwind
>>>
>>> !1 = !{!"function_entry_count", i64 7}
>>>
>>> !2 = !{!"branch_weights", i32 0, i32 7}
>>>
>>> ```
>>>
>>> Code generated by Machine Function Splitter
>>>
>>> $ llc < example.ll -mtriple=x86_64-unknown-linux-gnu
>>> -split-machine-functions
>>>
>>> ```
>>>
>>> .text
>>>
>>> .file "<stdin>"
>>>
>>> .globl foo # -- Begin function foo
>>>
>>> .p2align 4, 0x90
>>>
>>> .type foo, at function
>>>
>>> foo: # @foo
>>>
>>> # %bb.0:
>>>
>>> pushq %rbx
>>>
>>> movl %esi, %ebx
>>>
>>> testl %edi, %edi
>>>
>>> je foo.cold # Jump to cold code
>>>
>>> # %bb.1:
>>>
>>> callq L1
>>>
>>> .LBB0_2:
>>>
>>> addl i(%rip), %ebx
>>>
>>> movl %ebx, i(%rip)
>>>
>>> movl %ebx, %eax
>>>
>>> popq %rbx
>>>
>>> retq
>>>
>>> .section .text.unlikely.foo,"ax", at progbits
>>>
>>> foo.cold:
>>>
>>> callq R1
>>>
>>> incl %ebx # Directly increment value
>>>
>>> jmp .LBB0_2
>>>
>>> .LBB_END0_3:
>>>
>>> .size foo.cold, .LBB_END0_3-foo.cold
>>>
>>> .text
>>>
>>> .Lfunc_end0:
>>>
>>> .size foo, .Lfunc_end0-foo
>>>
>>> # -- End function
>>>
>>> .section ".note.GNU-stack","", at progbits
>>>
>>> ```
>>>
>>> Code generated by Hot Cold Split
>>>
>>> $ clang -c -O2 -S -mllvm --hot-cold-split -mllvm
>>> --hotcoldsplit-threshold=0 -x ir example.ll
>>>
>>> ```
>>>
>>> .text
>>>
>>> .file "example.ll"
>>>
>>> .globl foo # -- Begin function foo
>>>
>>> .p2align 4, 0x90
>>>
>>> .type foo, at function
>>>
>>> foo: # @foo
>>>
>>> # %bb.0:
>>>
>>> pushq %rbx
>>>
>>> subq $16, %rsp
>>>
>>> movl %esi, %ebx
>>>
>>> testl %edi, %edi
>>>
>>> jne .LBB0_1
>>>
>>> # %bb.2: # Residue block in original
>>> function
>>>
>>> leaq 12(%rsp), %rsi
>>>
>>> movl %ebx, %edi # Pass param to increment
>>>
>>> callq foo.cold.1 # Call to cold code
>>>
>>> movl 12(%rsp), %ebx # Fill incremented value from
>>> stack
>>>
>>> .LBB0_3:
>>>
>>> addl i(%rip), %ebx
>>>
>>> movl %ebx, i(%rip)
>>>
>>> movl %ebx, %eax
>>>
>>> addq $16, %rsp
>>>
>>> popq %rbx
>>>
>>> retq
>>>
>>> .LBB0_1:
>>>
>>> callq L1
>>>
>>> jmp .LBB0_3
>>>
>>> .Lfunc_end0:
>>>
>>> .size foo, .Lfunc_end0-foo
>>>
>>> # -- End function
>>>
>>> .p2align 4, 0x90 # -- Begin
>>> function foo.cold.1
>>>
>>> .type foo.cold.1, at function
>>>
>>> foo.cold.1: # @foo.cold.1
>>>
>>> # %bb.0: # %newFuncRoot
>>>
>>> pushq %rbp
>>>
>>> pushq %rbx
>>>
>>> pushq %rax
>>>
>>> movq %rsi, %rbx
>>>
>>> movl %edi, %ebp
>>>
>>> callq R1
>>>
>>> incl %ebp
>>>
>>> movl %ebp, (%rbx)
>>>
>>> addq $8, %rsp
>>>
>>> popq %rbx
>>>
>>> popq %rbp
>>>
>>> retq
>>>
>>> .Lfunc_end1:
>>>
>>> .size foo.cold.1, .Lfunc_end1-foo.cold.1
>>>
>>> # -- End function
>>>
>>> .cg_profile foo, L1, 0
>>>
>>> .cg_profile foo, foo.cold.1, 7
>>>
>>> .section ".note.GNU-stack","", at progbits
>>>
>>> .addrsig
>>>
>>> .addrsig_sym foo.cold.1
>>>
>>> ```
>>>
>>> Thanks,
>>> Snehasish Kumar
>>> Software Engineer, Google
>>>
>>>
>>>
>>>
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