[llvm-dev] [RFC] Machine Function Splitter - Split out cold blocks from machine functions using profile data

Wenlei He via llvm-dev llvm-dev at lists.llvm.org
Fri Aug 7 00:40:36 PDT 2020

Cool stuff – nice to see a late splitting pass in LLVM.

> 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.

I’d expect propeller or BOLT to be more effective at doing this due to better post-inline profile. Of course the usability advantage of not needing a separate profile is very practical, but just wondering did you see profile quality getting in the way here?

> uses existing instrumentation based FDO or CSFDO profile information.

Similarly, with instrumentation FDO alone, the post-inline profile may not be accurate, so for this splitting, is it more effective when used with CSFDO? Was the evaluation result from FDO or CSFDO?

Also wondering does this work with Sample FDO, and do you have numbers that you can share when used with Sample FDO?


From: llvm-dev <llvm-dev-bounces at lists.llvm.org> on behalf of Snehasish Kumar via llvm-dev <llvm-dev at lists.llvm.org>
Reply-To: Snehasish Kumar <snehasishk at google.com>
Date: Tuesday, August 4, 2020 at 5:41 PM
To: llvm-dev <llvm-dev at lists.llvm.org>, David Li <davidxl at google.com>, Eric Christopher <echristo at google.com>, Sriraman Tallam <tmsriram at google.com>, aditya kumar <hiraditya at gmail.com>, "efriedma at codeaurora.org" <efriedma at codeaurora.org>
Subject: [llvm-dev] [RFC] Machine Function Splitter - Split out cold blocks from machine functions using profile data


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.


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://urldefense.proofpoint.com/v2/url?u=https-3A__research.google_pubs_pub48320_&d=DwMFaQ&c=5VD0RTtNlTh3ycd41b3MUw&r=KfYo542rDdZQGClmgz-RBw&m=-cUmMKRcOXZHF-PpVxO_Dfg2mkIgP4L_QomIwDizeEE&s=5M8b2TUFoHuEJUjJjNRGvPxOEE0ktBbRfJVCoGAW4BQ&e=>). 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://urldefense.proofpoint.com/v2/url?u=https-3A__llvm.org_devmtg_2019-2D10_slides_Kumar-2DHotColdSplitting.pdf&d=DwMFaQ&c=5VD0RTtNlTh3ycd41b3MUw&r=KfYo542rDdZQGClmgz-RBw&m=-cUmMKRcOXZHF-PpVxO_Dfg2mkIgP4L_QomIwDizeEE&s=xfh7APIZXGJikzsEVba8f1JsDG3aMqQCrlbanFKDvsI&e=> 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://urldefense.proofpoint.com/v2/url?u=https-3A__lists.llvm.org_pipermail_llvm-2Ddev_2020-2DJune_142429.html&d=DwMFaQ&c=5VD0RTtNlTh3ycd41b3MUw&r=KfYo542rDdZQGClmgz-RBw&m=-cUmMKRcOXZHF-PpVxO_Dfg2mkIgP4L_QomIwDizeEE&s=dBjqHuzX7OwOwfdAUeWcmpcImYHy9ga6uGzqcdWFgAM&e=>. 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.


We propose a new machine function splitting pass which leverages the basic block sections feature<https://urldefense.proofpoint.com/v2/url?u=https-3A__reviews.llvm.org_D68063&d=DwMFaQ&c=5VD0RTtNlTh3ycd41b3MUw&r=KfYo542rDdZQGClmgz-RBw&m=-cUmMKRcOXZHF-PpVxO_Dfg2mkIgP4L_QomIwDizeEE&s=kQjqw1Wv-ojIDbl02uxcO-f40lom71ytRXVVHp1WbNI&e=> 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://urldefense.proofpoint.com/v2/url?u=https-3A__lists.llvm.org_pipermail_llvm-2Ddev_2019-2DSeptember_135393.html&d=DwMFaQ&c=5VD0RTtNlTh3ycd41b3MUw&r=KfYo542rDdZQGClmgz-RBw&m=-cUmMKRcOXZHF-PpVxO_Dfg2mkIgP4L_QomIwDizeEE&s=y0u_TamS9xnHRAQVD1cDCxl-AzE-QbTNmnYU73oxxFE&e=>, 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.

[cid:image001.png at 01D66C53.5D008990]

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.


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<https://urldefense.proofpoint.com/v2/url?u=https-3A__reviews.llvm.org_D78851&d=DwMFaQ&c=5VD0RTtNlTh3ycd41b3MUw&r=KfYo542rDdZQGClmgz-RBw&m=-cUmMKRcOXZHF-PpVxO_Dfg2mkIgP4L_QomIwDizeEE&s=A8or2dRzqfxiaH66OGKD5iVw4mOcqafXaCHhJpRLdYs&e=>)
  *   CFI (https://reviews.llvm.org/D79978<https://urldefense.proofpoint.com/v2/url?u=https-3A__reviews.llvm.org_D79978&d=DwMFaQ&c=5VD0RTtNlTh3ycd41b3MUw&r=KfYo542rDdZQGClmgz-RBw&m=-cUmMKRcOXZHF-PpVxO_Dfg2mkIgP4L_QomIwDizeEE&s=9AuVMR_KTlutU3emsXEzWynp-9eD1yE_42wF7O3DP6o&e=>).

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://urldefense.proofpoint.com/v2/url?u=https-3A__lists.llvm.org_pipermail_llvm-2Ddev_2019-2DSeptember_135393.html&d=DwMFaQ&c=5VD0RTtNlTh3ycd41b3MUw&r=KfYo542rDdZQGClmgz-RBw&m=-cUmMKRcOXZHF-PpVxO_Dfg2mkIgP4L_QomIwDizeEE&s=y0u_TamS9xnHRAQVD1cDCxl-AzE-QbTNmnYU73oxxFE&e=>

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.


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.


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.


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%.


Split (MPKI)

Baseline (MPKI)

% Reduction

















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




.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.


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



        .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


        addl    i(%rip), %ebx

        movl    %ebx, i(%rip)

        movl    %ebx, %eax

        popq    %rbx


        .section        .text.unlikely.foo,"ax", at progbits


        callq   R1

        incl    %ebx                    # Directly increment value

        jmp     .LBB0_2


        .size   foo.cold, .LBB_END0_3-foo.cold



        .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



        .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


        addl    i(%rip), %ebx

        movl    %ebx, i(%rip)

        movl    %ebx, %eax

        addq    $16, %rsp

        popq    %rbx



        callq   L1

        jmp     .LBB0_3


        .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



        .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_sym foo.cold.1


Snehasish Kumar
Software Engineer, Google

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