This is a guide for how to profile rustc with perf.
config.toml
:debuginfo-level = 1
- enables line debuginfouse-jemalloc = false
- lets you do memory use profiling with valgrind./x.py build
to get a full buildperf 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 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:
cargo build; cargo clean -p $C
may be helpful (where $C
is the crate name)touch src/lib.rs
and rebuild instead. =)CARGO_INCREMENTAL=0
can be helpful.perf.rust-lang.org
testOften 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
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.
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.
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
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:
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.
perf-focus
You can install perf-focus using cargo install
:
> cargo install perf-focus
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).
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.
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, '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, '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
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.
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, '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
).