This document shows an example of how you would go about analyzing applications built with XRay instrumentation. Here we will attempt to debug llc compiling some sample LLVM IR generated by Clang.
To debug an application with XRay instrumentation, we need to build it with a Clang that supports the -fxray-instrument option. See XRay for more technical details of how XRay works for background information.
In our example, we need to add -fxray-instrument to the list of flags passed to Clang when building a binary. Note that we need to link with Clang as well to get the XRay runtime linked in appropriately. For building llc with XRay, we do something similar below for our LLVM build:
$ mkdir -p llvm-build && cd llvm-build
# Assume that the LLVM sources are at ../llvm
$ cmake -GNinja ../llvm -DCMAKE_BUILD_TYPE=Release \
-DCMAKE_C_FLAGS_RELEASE="-fxray-instrument" -DCMAKE_CXX_FLAGS="-fxray-instrument" \
# Once this finishes, we should build llc
$ ninja llc
To verify that we have an XRay instrumented binary, we can use objdump to look for the xray_instr_map section.
$ objdump -h -j xray_instr_map ./bin/llc
./bin/llc: file format elf64-x86-64
Sections:
Idx Name Size VMA LMA File off Algn
14 xray_instr_map 00002fc0 00000000041516c6 00000000041516c6 03d516c6 2**0
CONTENTS, ALLOC, LOAD, READONLY, DATA
By default, XRay does not write out the trace files or patch the application before main starts. If we just run llc it should just work like a normally built binary. However, if we want to get a full trace of the application’s operations (of the functions we do end up instrumenting with XRay) then we need to enable XRay at application start. To do this, XRay checks the XRAY_OPTIONS environment variable.
# The following doesn't create an XRay trace by default.
$ ./bin/llc input.ll
# We need to set the XRAY_OPTIONS to enable some features.
$ XRAY_OPTIONS="patch_premain=true" ./bin/llc input.ll
==69819==XRay: Log file in 'xray-log.llc.m35qPB'
At this point we now have an XRay trace we can start analysing.
Having a trace then allows us to do basic accounting of the functions that were instrumented, and how much time we’re spending in parts of the code. To make sense of this data, we use the llvm-xray tool which has a few subcommands to help us understand our trace.
One of the simplest things we can do is to get an accounting of the functions that have been instrumented. We can see an example accounting with llvm-xray account:
$ llvm-xray account xray-log.llc.m35qPB -top=10 -sort=sum -sortorder=dsc -instr_map ./bin/llc
Functions with latencies: 29
funcid count [ min, med, 90p, 99p, max] sum function
187 360 [ 0.000000, 0.000001, 0.000014, 0.000032, 0.000075] 0.001596 LLLexer.cpp:446:0: llvm::LLLexer::LexIdentifier()
85 130 [ 0.000000, 0.000000, 0.000018, 0.000023, 0.000156] 0.000799 X86ISelDAGToDAG.cpp:1984:0: (anonymous namespace)::X86DAGToDAGISel::Select(llvm::SDNode*)
138 130 [ 0.000000, 0.000000, 0.000017, 0.000155, 0.000155] 0.000774 SelectionDAGISel.cpp:2963:0: llvm::SelectionDAGISel::SelectCodeCommon(llvm::SDNode*, unsigned char const*, unsigned int)
188 103 [ 0.000000, 0.000000, 0.000003, 0.000123, 0.000214] 0.000737 LLParser.cpp:2692:0: llvm::LLParser::ParseValID(llvm::ValID&, llvm::LLParser::PerFunctionState*)
88 1 [ 0.000562, 0.000562, 0.000562, 0.000562, 0.000562] 0.000562 X86ISelLowering.cpp:83:0: llvm::X86TargetLowering::X86TargetLowering(llvm::X86TargetMachine const&, llvm::X86Subtarget const&)
125 102 [ 0.000001, 0.000003, 0.000010, 0.000017, 0.000049] 0.000471 Verifier.cpp:3714:0: (anonymous namespace)::Verifier::visitInstruction(llvm::Instruction&)
90 8 [ 0.000023, 0.000035, 0.000106, 0.000106, 0.000106] 0.000342 X86ISelLowering.cpp:3363:0: llvm::X86TargetLowering::LowerCall(llvm::TargetLowering::CallLoweringInfo&, llvm::SmallVectorImpl<llvm::SDValue>&) const
124 32 [ 0.000003, 0.000007, 0.000016, 0.000041, 0.000041] 0.000310 Verifier.cpp:1967:0: (anonymous namespace)::Verifier::visitFunction(llvm::Function const&)
123 1 [ 0.000302, 0.000302, 0.000302, 0.000302, 0.000302] 0.000302 LLVMContextImpl.cpp:54:0: llvm::LLVMContextImpl::~LLVMContextImpl()
139 46 [ 0.000000, 0.000002, 0.000006, 0.000008, 0.000019] 0.000138 TargetLowering.cpp:506:0: llvm::TargetLowering::SimplifyDemandedBits(llvm::SDValue, llvm::APInt const&, llvm::APInt&, llvm::APInt&, llvm::TargetLowering::TargetLoweringOpt&, unsigned int, bool) const
This shows us that for our input file, llc spent the most cumulative time in the lexer (a total of 1 millisecond). If we wanted for example to work with this data in a spreadsheet, we can output the results as CSV using the -format=csv option to the command for further analysis.
If we want to get a textual representation of the raw trace we can use the llvm-xray convert tool to get YAML output. The first few lines of that ouput for an example trace would look like the following:
$ llvm-xray convert -f yaml -symbolize -instr_map=./bin/llc xray-log.llc.m35qPB
---
header:
version: 1
type: 0
constant-tsc: true
nonstop-tsc: true
cycle-frequency: 2601000000
records:
- { type: 0, func-id: 110, function: __cxx_global_var_init.8, cpu: 37, thread: 69819, kind: function-enter, tsc: 5434426023268520 }
- { type: 0, func-id: 110, function: __cxx_global_var_init.8, cpu: 37, thread: 69819, kind: function-exit, tsc: 5434426023523052 }
- { type: 0, func-id: 164, function: __cxx_global_var_init, cpu: 37, thread: 69819, kind: function-enter, tsc: 5434426029925386 }
- { type: 0, func-id: 164, function: __cxx_global_var_init, cpu: 37, thread: 69819, kind: function-exit, tsc: 5434426030031128 }
- { type: 0, func-id: 142, function: '(anonymous namespace)::CommandLineParser::ParseCommandLineOptions(int, char const* const*, llvm::StringRef, llvm::raw_ostream*)', cpu: 37, thread: 69819, kind: function-enter, tsc: 5434426046951388 }
- { type: 0, func-id: 142, function: '(anonymous namespace)::CommandLineParser::ParseCommandLineOptions(int, char const* const*, llvm::StringRef, llvm::raw_ostream*)', cpu: 37, thread: 69819, kind: function-exit, tsc: 5434426047282020 }
- { type: 0, func-id: 187, function: 'llvm::LLLexer::LexIdentifier()', cpu: 37, thread: 69819, kind: function-enter, tsc: 5434426047857332 }
- { type: 0, func-id: 187, function: 'llvm::LLLexer::LexIdentifier()', cpu: 37, thread: 69819, kind: function-exit, tsc: 5434426047984152 }
- { type: 0, func-id: 187, function: 'llvm::LLLexer::LexIdentifier()', cpu: 37, thread: 69819, kind: function-enter, tsc: 5434426048036584 }
- { type: 0, func-id: 187, function: 'llvm::LLLexer::LexIdentifier()', cpu: 37, thread: 69819, kind: function-exit, tsc: 5434426048042292 }
- { type: 0, func-id: 187, function: 'llvm::LLLexer::LexIdentifier()', cpu: 37, thread: 69819, kind: function-enter, tsc: 5434426048055056 }
- { type: 0, func-id: 187, function: 'llvm::LLLexer::LexIdentifier()', cpu: 37, thread: 69819, kind: function-exit, tsc: 5434426048067316 }
So far in our examples, we haven’t been getting full coverage of the functions we have in the binary. To get that, we need to modify the compiler flags so that we can instrument more (if not all) the functions we have in the binary. We have two options for doing that, and we explore both of these below.
The first “blunt” way of doing this is by setting the minimum threshold for function bodies to 1. We can do that with the -fxray-instruction-threshold=N flag when building our binary. We rebuild llc with this option and observe the results:
$ rm CMakeCache.txt
$ cmake -GNinja ../llvm -DCMAKE_BUILD_TYPE=Release \
-DCMAKE_C_FLAGS_RELEASE="-fxray-instrument -fxray-instruction-threshold=1" \
-DCMAKE_CXX_FLAGS="-fxray-instrument -fxray-instruction-threshold=1"
$ ninja llc
$ XRAY_OPTIONS="patch_premain=true" ./bin/llc input.ll
==69819==XRay: Log file in 'xray-log.llc.5rqxkU'
$ llvm-xray account xray-log.llc.5rqxkU -top=10 -sort=sum -sortorder=dsc -instr_map ./bin/llc
Functions with latencies: 36652
funcid count [ min, med, 90p, 99p, max] sum function
75 1 [ 0.672368, 0.672368, 0.672368, 0.672368, 0.672368] 0.672368 llc.cpp:271:0: main
78 1 [ 0.626455, 0.626455, 0.626455, 0.626455, 0.626455] 0.626455 llc.cpp:381:0: compileModule(char**, llvm::LLVMContext&)
139617 1 [ 0.472618, 0.472618, 0.472618, 0.472618, 0.472618] 0.472618 LegacyPassManager.cpp:1723:0: llvm::legacy::PassManager::run(llvm::Module&)
139610 1 [ 0.472618, 0.472618, 0.472618, 0.472618, 0.472618] 0.472618 LegacyPassManager.cpp:1681:0: llvm::legacy::PassManagerImpl::run(llvm::Module&)
139612 1 [ 0.470948, 0.470948, 0.470948, 0.470948, 0.470948] 0.470948 LegacyPassManager.cpp:1564:0: (anonymous namespace)::MPPassManager::runOnModule(llvm::Module&)
139607 2 [ 0.147345, 0.315994, 0.315994, 0.315994, 0.315994] 0.463340 LegacyPassManager.cpp:1530:0: llvm::FPPassManager::runOnModule(llvm::Module&)
139605 21 [ 0.000002, 0.000002, 0.102593, 0.213336, 0.213336] 0.463331 LegacyPassManager.cpp:1491:0: llvm::FPPassManager::runOnFunction(llvm::Function&)
139563 26096 [ 0.000002, 0.000002, 0.000037, 0.000063, 0.000215] 0.225708 LegacyPassManager.cpp:1083:0: llvm::PMDataManager::findAnalysisPass(void const*, bool)
108055 188 [ 0.000002, 0.000120, 0.001375, 0.004523, 0.062624] 0.159279 MachineFunctionPass.cpp:38:0: llvm::MachineFunctionPass::runOnFunction(llvm::Function&)
62635 22 [ 0.000041, 0.000046, 0.000050, 0.126744, 0.126744] 0.127715 X86TargetMachine.cpp:242:0: llvm::X86TargetMachine::getSubtargetImpl(llvm::Function const&) const
The other way is to use configuration files for selecting which functions should always be instrumented by the compiler. This gives us a way of ensuring that certain functions are either always or never instrumented by not having to add the attribute to the source.
To use this feature, you can define one file for the functions to always instrument, and another for functions to never instrument. The format of these files are exactly the same as the SanitizerLists files that control similar things for the sanitizer implementations. For example, we can have two different files like below:
# always-instrument.txt
# always instrument functions that match the following filters:
fun:main
# never-instrument.txt
# never instrument functions that match the following filters:
fun:__cxx_*
Given the above two files we can re-build by providing those two files as arguments to clang as -fxray-always-instrument=always-instrument.txt or -fxray-never-instrument=never-instrument.txt.
The llvm-xray tool has a few other subcommands that are in various stages of being developed. One interesting subcommand that can highlight a few interesting things is the graph subcommand. Given for example the following toy program that we build with XRay instrumentation, we can see how the generated graph may be a helpful indicator of where time is being spent for the application.
// sample.cc
#include <iostream>
#include <thread>
[[clang::xray_always_intrument]] void f() {
std::cerr << '.';
}
[[clang::xray_always_intrument]] void g() {
for (int i = 0; i < 1 << 10; ++i) {
std::cerr << '-';
}
}
int main(int argc, char* argv[]) {
std::thread t1([] {
for (int i = 0; i < 1 << 10; ++i)
f();
});
std::thread t2([] {
g();
});
t1.join();
t2.join();
std::cerr << '\n';
}
We then build the above with XRay instrumentation:
$ clang++ -o sample -O3 sample.cc -std=c++11 -fxray-instrument -fxray-instruction-threshold=1
$ XRAY_OPTIONS="patch_premain=true" ./sample
We can then explore the graph rendering of the trace generated by this sample application. We assume you have the graphviz toosl available in your system, including both unflatten and dot. If you prefer rendering or exploring the graph using another tool, then that should be feasible as well. llvm-xray graph will create DOT format graphs which should be usable in most graph rendering applications. One example invocation of the llvm-xray graph command should yield some interesting insights to the workings of C++ applications:
$ llvm-xray graph xray-log.sample.* -m sample -color-edges=sum -edge-label=sum \
| unflatten -f -l10 | dot -Tsvg -o sample.svg
If you have some interesting analyses you’d like to implement as part of the llvm-xray tool, please feel free to propose them on the llvm-dev@ mailing list. The following are some ideas to inspire you in getting involved and potentially making things better.
- Implement a query/filtering library that allows for finding patterns in the XRay traces.
- A conversion from the XRay trace onto something that can be visualised better by other tools (like the Chrome trace viewer for example).
- Collecting function call stacks and how often they’re encountered in the XRay trace.