Profiling with perf

This is a guide for how to profile rustc with perf.

Initial steps

Get a clean checkout of rust-lang/master, or whatever it is you want to profile.
Set the following settings in your config.toml:
- debuginfo-level = 1 - enables line debuginfo
- use-jemalloc = false - lets you do memory use profiling with valgrind
- leave everything else the defaults
Run ./x.py build to get a full build
Make a rustup toolchain pointing to that result
- see the "build and run" section for instructions

perf is an excellent tool on linux that can be used to gather and analyze all kinds of information. Mostly it is used to figure out where a program spends its time. It can also be used for other sorts of events, though, like cache misses and so forth.

The basics

The basic perf command is this:

> perf record -F99 --call-graph dwarf XXX

The -F99 tells perf to sample at 99 Hz, which avoids generating too much data for longer runs (why 99 Hz you ask? It is often chosen because it is unlikely to be in lockstep with other periodic activity). The --call-graph dwarf tells perf to get call-graph information from debuginfo, which is accurate. The XXX is the command you want to profile. So, for example, you might do:

> perf record -F99 --call-graph dwarf cargo +<toolchain> rustc

to run cargo -- here <toolchain> should be the name of the toolchain you made in the beginning. But there are some things to be aware of:

You probably don't want to profile the time spend building dependencies. So something like cargo build; cargo clean -p $C may be helpful (where $C is the crate name)
- Though usually I just do touch src/lib.rs and rebuild instead. =)
You probably don't want incremental messing about with your profile. So something like CARGO_INCREMENTAL=0 can be helpful.

Gathering a perf profile from a `perf.rust-lang.org` test

Often we want to analyze a specific test from perf.rust-lang.org. To do that, the first step is to clone the rustc-perf repository:

> git clone https://github.com/rust-lang-nursery/rustc-perf

Doing it the easy way

Once you've cloned the repo, you can use the collector executable to do profiling for you! You can find instructions in the rustc-perf readme.

For example, to measure the clap-rs test, you might do:

> ./target/release/collector
    --output-repo /path/to/place/output
    profile perf-record
    --rustc /path/to/rustc/executable/from/your/build/directory
    --cargo `which cargo`
    --filter clap-rs
    --builds Check

You can also use that same command to use cachegrind or other profiling tools.

Doing it the hard way

If you prefer to run things manually, that is also possible. You first need to find the source for the test you want. Sources for the tests are found in the collector/benchmarks directory. So let's go into the directory of a specific test; we'll use clap-rs as an example:

> cd collector/benchmarks/clap-rs

In this case, let's say we want to profile the cargo check performance. In that case, I would first run some basic commands to build the dependencies:

# Setup: first clean out any old results and build the dependencies:
> cargo +<toolchain> clean
> CARGO_INCREMENTAL=0 cargo +<toolchain> check

(Again, <toolchain> should be replaced with the name of the toolchain we made in the first step.)

Next: we want record the execution time for just the clap-rs crate, running cargo check. I tend to use cargo rustc for this, since it also allows me to add explicit flags, which we'll do later on.

> touch src/lib.rs
> CARGO_INCREMENTAL=0 perf record -F99 --call-graph dwarf cargo rustc --profile check --lib

Note that final command: it's a doozy! It uses the cargo rustc command, which executes rustc with (potentially) additional options; the --profile check and --lib options specify that we are doing a cargo check execution, and that this is a library (not a binary).

At this point, we can use perf tooling to analyze the results. For example:

> perf report

will open up an interactive TUI program. In simple cases, that can be helpful. For more detailed examination, the perf-focus tool can be helpful; it is covered below.

A note of caution. Each of the rustc-perf tests is its own special snowflake. In particular, some of them are not libraries, in which case you would want to do touch src/main.rs and avoid passing --lib. I'm not sure how best to tell which test is which to be honest.

Gathering NLL data

If you want to profile an NLL run, you can just pass extra options to the cargo rustc command, like so:

> touch src/lib.rs
> CARGO_INCREMENTAL=0 perf record -F99 --call-graph dwarf cargo rustc --profile check --lib -- -Zborrowck=mir

Analyzing a perf profile with `perf focus`

Once you've gathered a perf profile, we want to get some information about it. For this, I personally use perf focus. It's a kind of simple but useful tool that lets you answer queries like:

"how much time was spent in function F" (no matter where it was called from)
"how much time was spent in function F when it was called from G"
"how much time was spent in function F excluding time spent in G"
"what functions does F call and how much time does it spend in them"

To understand how it works, you have to know just a bit about perf. Basically, perf works by sampling your process on a regular basis (or whenever some event occurs). For each sample, perf gathers a backtrace. perf focus lets you write a regular expression that tests which functions appear in that backtrace, and then tells you which percentage of samples had a backtrace that met the regular expression. It's probably easiest to explain by walking through how I would analyze NLL performance.

Installing `perf-focus`

You can install perf-focus using cargo install:

> cargo install perf-focus

Example: How much time is spent in MIR borrowck?

Let's say we've gathered the NLL data for a test. We'd like to know how much time it is spending in the MIR borrow-checker. The "main" function of the MIR borrowck is called do_mir_borrowck, so we can do this command:

> perf focus '{do_mir_borrowck}'
Matcher    : {do_mir_borrowck}
Matches    : 228
Not Matches: 542
Percentage : 29%

The '{do_mir_borrowck}' argument is called the matcher. It specifies the test to be applied on the backtrace. In this case, the {X} indicates that there must be some function on the backtrace that meets the regular expression X. In this case, that regex is just the name of the function we want (in fact, it's a subset of the name; the full name includes a bunch of other stuff, like the module path). In this mode, perf-focus just prints out the percentage of samples where do_mir_borrowck was on the stack: in this case, 29%.

A note about c++filt. To get the data from perf, perf focus currently executes perf script (perhaps there is a better way...). I've sometimes found that perf script outputs C++ mangled names. This is annoying. You can tell by running perf script | head yourself — if you see names like 5rustc6middle instead of rustc::middle, then you have the same problem. You can solve this by doing:

> perf script | c++filt | perf focus --from-stdin ...

This will pipe the output from perf script through c++filt and should mostly convert those names into a more friendly format. The --from-stdin flag to perf focus tells it to get its data from stdin, rather than executing perf focus. We should make this more convenient (at worst, maybe add a c++filt option to perf focus, or just always use it — it's pretty harmless).

Example: How much time does MIR borrowck spend solving traits?

Perhaps we'd like to know how much time MIR borrowck spends in the trait checker. We can ask this using a more complex regex:

> perf focus '{do_mir_borrowck}..{^rustc::traits}'
Matcher    : {do_mir_borrowck},..{^rustc::traits}
Matches    : 12
Not Matches: 1311
Percentage : 0%

Here we used the .. operator to ask "how often do we have do_mir_borrowck on the stack and then, later, some function whose name begins with rusc::traits?" (basically, code in that module). It turns out the answer is "almost never" — only 12 samples fit that description (if you ever see no samples, that often indicates your query is messed up).

If you're curious, you can find out exactly which samples by using the --print-match option. This will print out the full backtrace for each sample. The | at the front of the line indicates the part that the regular expression matched.

Example: Where does MIR borrowck spend its time?

Often we want to do a more "explorational" queries. Like, we know that MIR borrowck is 29% of the time, but where does that time get spent? For that, the --tree-callees option is often the best tool. You usually also want to give --tree-min-percent or --tree-max-depth. The result looks like this:

> perf focus '{do_mir_borrowck}' --tree-callees --tree-min-percent 3
Matcher    : {do_mir_borrowck}
Matches    : 577
Not Matches: 746
Percentage : 43%

Tree
| matched `{do_mir_borrowck}` (43% total, 0% self)
: | rustc_mir::borrow_check::nll::compute_regions (20% total, 0% self)
: : | rustc_mir::borrow_check::nll::type_check::type_check_internal (13% total, 0% self)
: : : | core::ops::function::FnOnce::call_once (5% total, 0% self)
: : : : | rustc_mir::borrow_check::nll::type_check::liveness::generate (5% total, 3% self)
: : : | <rustc_mir::borrow_check::nll::type_check::TypeVerifier<'a, 'b, 'gcx, 'tcx> as rustc::mir::visit::Visitor<'tcx>>::visit_mir (3% total, 0% self)
: | rustc::mir::visit::Visitor::visit_mir (8% total, 6% self)
: | <rustc_mir::borrow_check::MirBorrowckCtxt<'cx, 'gcx, 'tcx> as rustc_mir::dataflow::DataflowResultsConsumer<'cx, 'tcx>>::visit_statement_entry (5% total, 0% self)
: | rustc_mir::dataflow::do_dataflow (3% total, 0% self)

What happens with --tree-callees is that

we find each sample matching the regular expression
we look at the code that is occurs after the regex match and try to build up a call tree

The --tree-min-percent 3 option says "only show me things that take more than 3% of the time. Without this, the tree often gets really noisy and includes random stuff like the innards of malloc. --tree-max-depth can be useful too, it just limits how many levels we print.

For each line, we display the percent of time in that function altogether ("total") and the percent of time spent in just that function and not some callee of that function (self). Usually "total" is the more interesting number, but not always.

Relative percentages

By default, all in perf-focus are relative to the total program execution. This is useful to help you keep perspective — often as we drill down to find hot spots, we can lose sight of the fact that, in terms of overall program execution, this "hot spot" is actually not important. It also ensures that percentages between different queries are easily compared against one another.

That said, sometimes it's useful to get relative percentages, so perf focus offers a --relative option. In this case, the percentages are listed only for samples that match (vs all samples). So for example we could get our percentages relative to the borrowck itself like so:

> perf focus '{do_mir_borrowck}' --tree-callees --relative --tree-max-depth 1 --tree-min-percent 5
Matcher    : {do_mir_borrowck}
Matches    : 577
Not Matches: 746
Percentage : 100%

Tree
| matched `{do_mir_borrowck}` (100% total, 0% self)
: | rustc_mir::borrow_check::nll::compute_regions (47% total, 0% self) [...]
: | rustc::mir::visit::Visitor::visit_mir (19% total, 15% self) [...]
: | <rustc_mir::borrow_check::MirBorrowckCtxt<'cx, 'gcx, 'tcx> as rustc_mir::dataflow::DataflowResultsConsumer<'cx, 'tcx>>::visit_statement_entry (13% total, 0% self) [...]
: | rustc_mir::dataflow::do_dataflow (8% total, 1% self) [...]

Here you see that compute_regions came up as "47% total" — that means that 47% of do_mir_borrowck is spent in that function. Before, we saw 20% — that's because do_mir_borrowck itself is only 43% of the total time (and .47 * .43 = .20).

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