How one can Use BOLT, Binary Optimization and Format Device
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Information middle functions are typically very massive and complicated, which makes code format an necessary optimization to enhance their efficiency. Such a method for code format is known as feedback-driven optimizations (FDO) or profile-guided optimizations (PGO). Nevertheless, resulting from their massive sizes, making use of FDO to those functions results in scalability points resulting from vital reminiscence and computation utilization and value, which makes this system virtually unattainable.
To beat this scalability difficulty, the usage of sample-based profiling strategies have been launched by totally different methods, comparable to Ispike, AutoFDO and HFSort. A few of them are utilized to a number of factors within the compilation chain, comparable to AutoFDO to compilation time, LIPO and HFSort to hyperlink time and Ispike to post-link time. Amongst them, the post-link optimizers have been comparatively unpopular in comparison with the compile-time ones, because the profile information is injected within the late part of the compilation chain.
Nevertheless, BOLT demonstrates that the post-link optimizations are nonetheless helpful as a result of injecting the profile information later permits extra correct use of the knowledge for higher code format, and mapping the profile information, which is collected on the binary stage, again to the binary stage (as a substitute of the compiler’s intermediate illustration) is far less complicated, leading to environment friendly low-level optimizations comparable to code format.
It’s to not be confused with the open supply software from Puppet to run ad-hoc instructions and scripts throughout infrastructure, which can be known as Bolt.
Continuously Requested Questions on BOLT
Q. What does BOLT stand for?
A. Binary Optimization and Format Device
Q. What does BOLT do?
A. BOLT has the next rewriting pipeline for a given executable binary:
- Operate discovery
- Learn debug data
- Learn profile information
- Disassembly
- CFG development (utilizing LLVM’s Tablegen-generated disassembler)
- Optimization pipeline
- Emit and hyperlink capabilities
- Rewrite binary file
Q. Can any of the optimization strategies be moved to earlier phases of compilation?
A. It is determined by the state of affairs.
- Pattern-based or instrumentation-based
- Code effectivity vs. runtime overhead
- Whether or not re-compilation is allowed
- Object information/executable binary in hyperlink/post-link part vs. compiler IR in compile part
Q. Why does BOLT run on the binary stage however not on the supply code stage or compiler IR stage?
A. First, profiling information sometimes collects binary-level occasions, and there are challenges in mapping such occasions to higher-level code illustration. Determine 1 reveals such a problem.
Determine 1. An instance of a problem in mapping binary-level occasions again to higher-level code representations
Second, consumer applications (object code) might be improved virtually immediately with minimal effort.
Q. Why is BOLT applied as a separate software?
A. There are two causes:
- There are a number of open supply linkers and deciding on certainly one of them to make use of for any explicit software is determined by a variety of circumstances which will additionally change over time.
- To facilitate the software’s adaptation.
Q. What sort of optimizations does BOLT carry out?
A. BOLT optimization pipeline makes use of:
strip-rep-ret: Strip ‘epzfromrepz retqdirections used for legacy AMD processors.icf: An identical code folding: further advantages from perform with out-ffunction-sectionsflag and capabilities with leap tablesicp: Oblique name promotion: leverages name frequency data to mutate a perform name right into a extra efficiency modelpeepholes: Easy peephole optimizationssimplify-to-loads: Fetch fixed information in.rodatawhose tackle is understood statically and mutates a load right into a transfer instructionicf: An identical code folding (second run)plt: Take away indirection fromPLTcallsreorder-bbs: Reorder primary blocks and break up scorching/chilly blocks into separate sections (format optimization)peepholes: Easy peephole optimization (second run)uce: Get rid of unreachable primary blocksfixup-branches: Repair primary block terminator directions to match the CFG and the present format (redone byreorder-bbs)reorder-functions: Apply HFSort to reorder capabilities (format optimization)sctc: Simplify conditional tail calls- f
rame-opts: Take away pointless caller-saved register spilling shrink-wrapping: Transfer callee-saved register spills nearer to the place they’re wanted, if profiling information reveals it’s higher to take action
Q. Can BOLT be used for dynamically loading libraries?
A. Sure, it simply requires an extra profiling step with dynamically loading libraries.
Q. Which profiling information does BOLT use?
A. BOLT makes use of Linux perf utility to gather coaching enter, together with:
- CPU cycles (in consumer mode solely)
- Sampled taken branches (and kind of branches)
Please check with the main points of perf occasions right here.
Q. What functions have been examined to benchmark BOLT?
A. Bigger functions (greater than 100MB). It’s higher to aggressively cut back I-cache occupation, because the cache is among the most constrained sources within the information middle area. The followings are examined by Fb utilizing BOLT:
- HHVM: PHP/Hack digital machine that powers the online servers
- TAO: a extremely distributed, in reminiscence, data-cache service
- Proxygen: a cluster load balancer
- Multifeed: a range of what’s proven within the Fb Information Feed
- Clang: a compiler frontend for programming languages
- GCC: an optimizing compiler by GNU Undertaking
Present Standing of BOLT
The unique analysis paper was printed by CGO 2019 by Fb engineers. The supply code has been launched and maintained at GitHub since 2015. The BOLT undertaking was added to the mainstream of the LLVM undertaking model 14 in March.
BOLT operates on x86-64 and AAarch64 ELF binaries. The binaries ought to have an unstripped image desk; to get most efficiency positive aspects, they need to be linked with relocations (--emit-relocs or –q linker flag).
BOLT is at present incompatible with the -freorder-blocks-and-partition compiler possibility. GCC 8 and later variations allow this selection by default; you need to explicitly disable it by including a -fno-reorder-blocks-and-partition flag.
The most recent code commits have been accomplished 4 months in the past, and they’re non-functional modifications.
How one can Construct and Check BOLT
This part describes learn how to construct BOLT and take a look at with easy executables.
Constructing BOLT
Step 1. Get supply code.
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git clone https://github.com/facebookincubator/BOLT llvm-bolt |
Step 2. Construct BOLT.
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cd llvm–bolt mkdir construct cd construct cmake –G Ninja ../llvm–bolt/llvm –DLLVM_TARGETS_TO_BUILD=“X86;AArch64” –DCMAKE_BUILD_TYPE=Launch –DLLVM_ENABLE_ASSERTIONS=ON –DLLVM_ENABLE_PROJECTS=“clang;lld;bolt” ninja |
Be aware that you simply may want to change the PATH variable in your surroundings to incorporate ./llvm-bolt/construct/bin.
Check with Easy Executable
Step 1. Write t.cc.
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// t.cc #embody <iostream> #embody <vector>
utilizing namespace std;
int x[5] = { 0xba, 0xbb, 0xbc, 0xbd, 0xbe };
bool p(int n) { for (int i = 2; i*i <= n; i++) { if (n % i == 0) return false; } return true; }
int f(int i) { return x[i]; }
int most important() { int sum = 0; for (int okay = 2; okay < 1000000; okay++) { if (p(okay)) { sum++; } } cout << sum << endl;
} |
Step 2. Write a Makefile.
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# Makefile
t: t.cc clang++ –Wl,–emit–relocs –o t t.cc
clear: rm t |
Step 3. Construct an executable from t.cc.
Step 4. Get profile information p.information from executable t by working perf utility.
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$ perf report –e cycles:u –j any,u –o p.information — ./t 78498 [ perf record: Woken up 3 times to write data ] [ perf record: Captured and wrote 0.526 MB p.data (1280 samples) ] |
Step 5. Convert perf information, p.information, to BOLT format, p.fdata, by executing perf2bolt.
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$ perf2bolt –p p.information –o p.fdata ./t PERF2BOLT: Beginning information aggregation job for p.information PERF2BOLT: spawning perf job to learn department occasions PERF2BOLT: spawning perf job to learn mem occasions PERF2BOLT: spawning perf job to learn course of occasions PERF2BOLT: spawning perf job to learn process occasions BOLT–INFO: Goal structure: x86_64 BOLT–INFO: BOLT model: 88c70afe9d388ad430cc150cc158641701397f70 BOLT–INFO: first alloc tackle is 0x400000 BOLT–INFO: creating new program header desk at tackle 0x800000, offset 0x400000 BOLT–INFO: enabling relocation mode BOLT–INFO: enabling strict relocation mode for aggregation functions BOLT–INFO: pre–processing profile utilizing perf information aggregator BOLT–WARNING: construct–id will not be checked as a result of we might not learn one from enter binary PERF2BOLT: ready for perf mmap occasions assortment to end… PERF2BOLT: parsing perf–script mmap occasions output PERF2BOLT: ready for perf process occasions assortment to end… PERF2BOLT: parsing perf–script process occasions output PERF2BOLT: enter binary is related with 1 PID(s) PERF2BOLT: ready for perf occasions assortment to end… PERF2BOLT: parse department occasions… PERF2BOLT: learn 1280 samples and 20335 LBR entries PERF2BOLT: 0 samples (0.0%) have been ignored PERF2BOLT: traces mismatching disassembled perform contents: 0 (0.0%) PERF2BOLT: out of vary traces involving unknown areas: 253 (1.3%) BOLT–WARNING: Ignored 0 capabilities due to chilly fragments. PERF2BOLT: processing department occasions… PERF2BOLT: wrote 17 objects and 0 reminiscence objects to p.fdata |
Be aware that you simply may have to grant customers permission to execute perf.
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$ sudo sysctl kernel.perf_event_paranoid=-1 kernel.perf_event_paranoid = –1 |
Step 6. Generate optimized binary t.bolt from t.
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$ llvm–bolt ./t –o ./t.bolt –information=p.fdata –reorder–blocks=cache+ –reorder–capabilities=hfsort –break up–capabilities=2 –break up–all–chilly –break up–eh –dyno–stats BOLT–INFO: Goal structure: x86_64 BOLT–INFO: BOLT model: 88c70afe9d388ad430cc150cc158641701397f70 BOLT–INFO: first alloc tackle is 0x400000 BOLT–INFO: creating new program header desk at tackle 0x800000, offset 0x400000 BOLT–INFO: enabling relocation mode BOLT–INFO: enabling lite mode BOLT–INFO: pre–processing profile utilizing department profile reader BOLT–WARNING: Ignored 0 capabilities due to chilly fragments. BOLT–INFO: 2 out of 16 capabilities in the binary (12.5%) have non–empty execution profile BOLT–INFO: 10 directions have been shortened BOLT–INFO: primary block reordering modified format of 2 (9.09%) capabilities BOLT–INFO: UCE eliminated 0 blocks and 0 bytes of code. BOLT–INFO: splitting separates 76 scorching bytes from 51 chilly bytes (59.84% of break up capabilities is scorching). BOLT–INFO: 0 Capabilities have been reordered by LoopInversionPass BOLT–INFO: program–extensive dynostats after all optimizations earlier than SCTC and FOP:
13531 : executed ahead branches 6165 : taken ahead branches 0 : executed backward branches 0 : taken backward branches 13644 : executed unconditional branches 141 : all perform calls 0 : oblique calls 0 : PLT calls 96484 : executed directions 41335 : executed load directions 7716 : executed retailer directions 0 : taken leap desk branches 0 : taken unknown oblique branches 27175 : whole branches 19809 : taken branches 7366 : non–taken conditional branches 6165 : taken conditional branches 13531 : all conditional branches
7258 : executed ahead branches (-46.4%) 16 : taken ahead branches (-99.7%) 6273 : executed backward branches (+627200.0%) 6246 : taken backward branches (+624500.0%) 174 : executed unconditional branches (-98.7%) 141 : all perform calls (=) 0 : oblique calls (=) 0 : PLT calls (=) 82987 : executed directions (-14.0%) 41335 : executed load directions (=) 7716 : executed retailer directions (=) 0 : taken leap desk branches (=) 0 : taken unknown oblique branches (=) 13705 : whole branches (-49.6%) 6436 : taken branches (-67.5%) 7269 : non–taken conditional branches (-1.3%) 6262 : taken conditional branches (+1.6%) 13531 : all conditional branches (=)
BOLT–INFO: SCTC: patched 0 tail calls (0 ahead) tail calls (0 backward) from a whole of 0 whereas eradicating 0 double jumps and eradicating 0 primary blocks totalling 0 bytes of code. CTCs whole execution depend is 0 and the quantity of instances CTCs are taken is 0. BOLT–INFO: padding code to 0xc00000 to accommodate scorching textual content BOLT–INFO: setting _end to 0x600df0 BOLT–INFO: setting __hot_start to 0xa00000 BOLT–INFO: setting __hot_end to 0xa00092 |
Step 7. Examine the file dimension and the execution time for t and t.bolt.
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$ ls –l t t.bolt –rwxrwxr–x 1 wjeon wjeon 10400 Feb 10 17:10 t –rwxrwxrwx 1 wjeon wjeon 8394880 Feb 10 17:18 t.bolt $ time ./t 78498
actual 0m0.309s consumer 0m0.309s sys 0m0.000s wjeon@fw0014107 ~/maplejs/maple $ time ./t.bolt 78498
actual 0m0.259s consumer 0m0.259s sys 0m0.000s |
Easy Trial with Maple JavaScript
Of their analysis paper, the Fb groups use two classes of binaries to judge BOLT. The primary is the precise workloads working on Fb’s information facilities. They’re (1) HHVM, the PHP/Hack digital machine, (2) TAO, a distributed, in-memory, data-caching service, (3) Proxygen, a cluster load balancer constructed on high of the identical open supply library and (4) Multifeed, a service for Fb Information Feed. The second class of binaries are (1) Clang and (2) GCC compilers.
First, we tried to make use of the engine for our concentrating on binary to optimize. Javascript engine is an in-house JavaScript runtime engine developed by Futurewei Applied sciences. Two workloads have been used for Maple JavaScript: First one is prime.js which finds prime numbers lower than 1 million, and the second is 3d-cube.js, which performs matrix computations for rotating a 3D dice.
Step 1: The Cmake construct script have to be modified to maintain relocations within the executable file.
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diff —git a/maple_engine/src/CMakeLists.txt b/maple_engine/src/CMakeLists.txt index 8eec9d1..323f1a2 100644 — a/maple_engine/src/CMakeLists.txt +++ b/maple_engine/src/CMakeLists.txt @@ –74,6 +74,8 @@ find_library( PBjaddr2_LIB java_addr2line “${CMAKE_CURRENT_SOURCE_DIR}/../lib/*” find_library( PBmplre_LIB mplre “${CMAKE_CURRENT_SOURCE_DIR}/../lib/*” ) find_library( PBunwind_LIB unwind “${CMAKE_CURRENT_SOURCE_DIR}/../lib/*” )
+target_link_options(mplre–dyn PRIVATE –Wl,–emit–relocs) + target_link_libraries( mplre–dyn “${CMAKE_CURRENT_SOURCE_DIR}/../../../mapleall/out/ark-clang-release/lib/64/libHWSecureC.a” “${CMAKE_CURRENT_SOURCE_DIR}/../../../mapleall/jscre/construct/libjscre.a” icuio icui18n icuuc icudata) #target_link_libraries( mplsh ${PBmpl_LIB} ) #target_link_libraries( mplsh ${PBcorea_LIB} ) |
Step 2: Construct the binary for the Maple JavaScript engine.
Step 3: Modify the run script to get profile information.
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diff —git a/maple_build/instruments/run–js–app.sh b/maple_build/instruments/run–js–app.sh index 0af9c8d..a4c0cae 100755 — a/maple_build/instruments/run–js–app.sh +++ b/maple_build/instruments/run–js–app.sh @@ –46,4 +46,5 @@ $MPLCG –O2 —quiet —no–pie —verbose–asm —fpic $file.mmpl /usr/bin/x86_64–linux–gnu–g++-5 –g3 –pie –O2 –x assembler–with–cpp –c $file.s –o $file.o /usr/bin/x86_64–linux–gnu–g++-5 –g3 –pie –O2 –fPIC –shared –o $file.so $file.o –rdynamic export LD_LIBRARY_PATH=$MAPLE_RUNTIME_ROOT/lib/x86_64 -$DBCMD $MPLSH –cp $file.so +#$DBCMD $MPLSH -cp $file.so +perf report –e cycles:u –j any,u –o perf.information — $DBCMD $MPLSH –cp $file.so |
Step 4: Write the benchmark JavaScript software, for instance, prime.js.
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if (typeof console == “object”) print = console.log; if (typeof console === ‘undefined’) console = {log:print}; perform p(n) { for (let i = 2;i * i <= n;i++) { if (n % i == 0) { return false; } } return true; }
var sum = 0; for (var okay = 2;okay < 1000000;okay++) { if (p(okay)) { sum++; } } print(sum); |
Step 5: Get profile information by working prime.js with the Maple JavaScript engine.
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$ run–js–app.sh prime.js 78498 [ perf record: Woken up 37 times to write data ] [ perf record: Captured and wrote 9.468 MB perf.data (22989 samples) ] |
Step 6: Convert perf information output to BOLT format.
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$ llvm–bolt libmplre–dyn.so –o libmplre–dyn.bolt.so –information=perf.fdata –reorder–blocks=cache+ –reorder–capabilities=hfsort –break up–capabilities=2 – break up–all–chilly –break up–eh –dyno–stats BOLT–INFO: shared object or place–impartial executable detected BOLT–INFO: Goal structure: x86_64 BOLT–INFO: BOLT model: 88c70afe9d388ad430cc150cc158641701397f70 BOLT–INFO: first alloc tackle is 0x0 BOLT–INFO: creating new program header desk at tackle 0x400000, offset 0x400000 BOLT–WARNING: debug information will be stripped from the binary. Use –replace–debug–sections to hold it. BOLT–INFO: enabling relocation mode BOLT–WARNING: disabling –break up–eh for shared object BOLT–INFO: enabling lite mode BOLT–INFO: pre–processing profile utilizing department profile reader BOLT–WARNING: Ignored 0 capabilities due to chilly fragments. BOLT–INFO: forcing –leap–tables=transfer as PIC leap desk was detected in perform _ZN5maple21InvokeInterpretMeth odERNS_12DynMFunctionE BOLT–INFO: 14 out of 1205 capabilities in the binary (1.2%) have non–empty execution profile BOLT–INFO: 1 perform with profile might not be optimized BOLT–INFO: profile for 1 objects was ignored BOLT–INFO: the enter accommodates 25 (dynamic depend : 1) alternatives for macro–fusion optimization. Will repair ins tances on a scorching path. BOLT–INFO: 1241 directions have been shortened BOLT–INFO: eliminated 5 empty blocks BOLT–INFO: primary block reordering modified format of 10 (0.47%) capabilities BOLT–INFO: UCE eliminated 0 blocks and 0 bytes of code. BOLT–INFO: splitting separates 3334 scorching bytes from 3048 chilly bytes (52.24% of break up capabilities is scorching). BOLT–INFO: 0 Capabilities have been reordered by LoopInversionPass BOLT–INFO: program–extensive dynostats after all optimizations earlier than SCTC and FOP:
21327 : executed ahead branches 10516 : taken ahead branches 652 : executed backward branches 459 : taken backward branches 648 : executed unconditional branches 2085 : all perform calls 988 : oblique calls 988 : PLT calls 327015 : executed directions 81409 : executed load directions 56643 : executed retailer directions 8029 : taken leap desk branches 0 : taken unknown oblique branches 22627 : whole branches 11623 : taken branches 11004 : non–taken conditional branches 10975 : taken conditional branches 21979 : all conditional branches
21205 : executed ahead branches (-0.6%) 255 : taken ahead branches (-97.6%) 774 : executed backward branches (+18.7%) 513 : taken backward branches (+11.8%) 329 : executed unconditional branches (-49.2%) 2085 : all perform calls (=) 988 : oblique calls (=) 988 : PLT calls (=) 326401 : executed directions (-0.2%) 81409 : executed load directions (=) 56643 : executed retailer directions (=) 8029 : taken leap desk branches (=) 0 : taken unknown oblique branches (=) 22308 : whole branches (-1.4%) 1097 : taken branches (-90.6%) 21211 : non–taken conditional branches (+92.8%) 768 : taken conditional branches (-93.0%) 21979 : all conditional branches (=)
BOLT–INFO: SCTC: patched 0 tail calls (0 ahead) tail calls (0 backward) from a whole of 0 whereas eradicating 0 d ouble jumps and eradicating 0 primary blocks totalling 0 bytes of code. CTCs whole execution depend is 0 and the num ber of instances CTCs are taken is 0. BOLT–INFO: padding code to 0x800000 to accommodate scorching textual content BOLT–INFO: setting _end to 0x80e43c BOLT–INFO: setting _end to 0x80e43c BOLT–INFO: setting __hot_start to 0x600000 BOLT–INFO: setting __hot_end to 0x6205e7 |
Step 7: Rename the Maple JavaScript runtime library libmplre-dyn.so.
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$ mv libmplre–dyn.so libmplre–dyn.so~ $ mv libmplre–dyn.bolt.so libmplre–dyn.so |
Step 8: Execute prime.js with the Maple JavaScript engine by utilizing the unique run script.
Step 9: Examine the file dimension and the execution time.
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$ ls –l libmplre–dyn* –rwxrwxrwx 1 wjeon wjeon 8676416 Feb 10 18:22 libmplre–dyn.so –rwxrwxr–x 1 wjeon wjeon 19387232 Feb 10 11:37 libmplre–dyn.bolt.so
// authentic $ time run–js–app.sh prime.js 78498
actual 0m5.743s consumer 0m5.714s sys 0m0.046s
// with BOLT $ time run–js–app.sh prime.js 78498
actual 0m5.738s consumer 0m5.710s sys 0m0.045s
// authentic $ time run–js–app.sh 3d–dice.js
actual 0m51.210s consumer 0m51.183s sys 0m0.040s
// with BOLT $ time run–js–app.sh 3d–dice.js
actual 0m51.425s consumer 0m51.368s sys 0m0.073s |
Nevertheless, the advantages of binary optimization utilizing BOLT for the Maple JavaScript engine was not clearly seen. We consider the primary purpose is that the workloads which can be used with Maple JavaScript weren’t as difficult as those that have been utilized by the unique authors of the paper. The workloads merely have conditional branches, so BOLT may not have any good alternatives to optimize the binary of Maple JavaScript. Additionally, the period of the execution instances may be very quick in comparison with the workloads that the authors used.
BOLT Optimization for Clang
So we determined to make use of the identical benchmark workload used within the paper on our setup, which was Clang compiler. The detailed steps to breed the consequence offered within the paper was documented within the BOLT’s Github repository. Many of the steps have been an identical, however the later model 14 of Clang was chosen as a substitute of Clang 7. Right here is the abstract of the setup.
- Examined app: Clang 14 (14.x department of GitHub supply code)
- Examined surroundings: Ubuntu 18.04.4 LTS, 40-core CPU, 800GB reminiscence
- Totally different optimizations
- PGO+LTO: baseline setup with out BOLT (Profile Guided Optimization + Hyperlink-Time Optimization supplied by LLVM/Clang)
- PGO+LTO+BOLT: BOLT optimizations enabled (steered by BOLT GitHub undertaking)
- Algorithm for reordering of capabilities:
hfsort+ - Algorithm for reordering of primary blocks:
cache+(format optimizing I-cache habits) - Stage of perform splitting: Three (all capabilities)
- Fold capabilities with an identical code
- Algorithm for reordering of capabilities:
- BOLT-reorder capabilities: BOLT optimizations excluding reordering of capabilities
- BOLT-reorder blocks: BOLT optimizations excluding reordering of primary blocks
- BOLT-hot/chilly break up: BOLT optimizations excluding scorching/chilly splitting
- BOLT-ICF: BOLT optimizations excluding an identical code folding
The principle objective of this take a look at is to determine how a lot efficiency advantages come from what optimization choices of BOLT. PGO+LTO, which permits the fundamental optimization based mostly on PGO and LTO which can be supported by LLVM, was chosen as a baseline of the efficiency comparability.
PGO+LTO+BOLT signifies all BOLT optimizations have been enabled on high of PGO and LTO. No reorder capabilitiesallow all of the BOLT optimization (described of their documentation) besides no reordering of capabilities. Equally, No reorder blocks, No scorching/chilly break up and No ICF permits all of the BOLT optimization besides reordering of primary blocks, scorching/chilly splitting, and an identical code folding, respectively.
Desk 1 reveals the execution instances of various optimization configurations.
Desk 1. Execution time of Clang with totally different optimization configurations
From the desk exhibiting the execution time, the next single optimization amongst all of the BOLT optimization choices principally have an effect on the execution time: (1) reorder blocks, (2) scorching/chilly perform break up, (3) reorder capabilities, (4) an identical code folding, so as.
Desk 2 reveals totally different contributions of various optimization choices on L1-icache-misses.
Determine 2. Contribution of various BOLT optimizations
As seen, every single BOLT optimization possibility that principally impacts L1-icache-misses is (1) reorder blocks, (2) scorching/chilly break up, reorder capabilities (tie), and (3) an identical code folding, so as.
Desk 3 reveals extra outcomes on totally different optimization choices from different system parameters’ perspective.
Desk 3. Contribution of various BOLT optimizations
From Desk 3, two further system parameters are principally affected by totally different BOLT optimization choices, cpu-cycles and L1-icache-load-misses. CPU-cycles is usually affected by (1) reorder blocks, (2) scorching/chilly break up, (2) reorder capabilities (tie) and (3) an identical code folding, so as, and L1-icache-load-misses by (1) reorder blocks, (2) scorching/chilly break up, (3) reorder capabilities and (4) an identical code folding, so as.
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