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<main>
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<h1 id="profiling-with-perf"><a class="header" href="#profiling-with-perf">Profiling with perf</a></h1>
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<p>This is a guide for how to profile rustc with <a href="https://perf.wiki.kernel.org/index.php/Main_Page">perf</a>.</p>
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<h2 id="initial-steps"><a class="header" href="#initial-steps">Initial steps</a></h2>
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<ul>
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<li>Get a clean checkout of rust-lang/master, or whatever it is you want
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to profile.</li>
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<li>Set the following settings in your <code>bootstrap.toml</code>:
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<ul>
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<li><code>rust.debuginfo-level = 1</code> - enables line debuginfo</li>
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<li><code>rust.jemalloc = false</code> - lets you do memory use profiling with valgrind</li>
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<li>leave everything else the defaults</li>
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</ul>
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</li>
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<li>Run <code>./x build</code> to get a full build</li>
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<li>Make a rustup toolchain pointing to that result
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<ul>
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<li>see <a href="../building/how-to-build-and-run.html#toolchain">the "build and run" section for instructions</a></li>
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</ul>
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</li>
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</ul>
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<h2 id="gathering-a-perf-profile"><a class="header" href="#gathering-a-perf-profile">Gathering a perf profile</a></h2>
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<p>perf is an excellent tool on linux that can be used to gather and
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analyze all kinds of information. Mostly it is used to figure out
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where a program spends its time. It can also be used for other sorts
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of events, though, like cache misses and so forth.</p>
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<h3 id="the-basics"><a class="header" href="#the-basics">The basics</a></h3>
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<p>The basic <code>perf</code> command is this:</p>
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<pre><code class="language-bash">perf record -F99 --call-graph dwarf XXX
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</code></pre>
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<p>The <code>-F99</code> tells perf to sample at 99 Hz, which avoids generating too
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much data for longer runs (why 99 Hz you ask? It is often chosen
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because it is unlikely to be in lockstep with other periodic
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activity). The <code>--call-graph dwarf</code> tells perf to get call-graph
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information from debuginfo, which is accurate. The <code>XXX</code> is the
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command you want to profile. So, for example, you might do:</p>
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<pre><code class="language-bash">perf record -F99 --call-graph dwarf cargo +<toolchain> rustc
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</code></pre>
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<p>to run <code>cargo</code> -- here <code><toolchain></code> should be the name of the toolchain
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you made in the beginning. But there are some things to be aware of:</p>
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<ul>
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<li>You probably don't want to profile the time spend building
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dependencies. So something like <code>cargo build; cargo clean -p $C</code> may
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be helpful (where <code>$C</code> is the crate name)
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<ul>
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<li>Though usually I just do <code>touch src/lib.rs</code> and rebuild instead. =)</li>
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</ul>
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</li>
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<li>You probably don't want incremental messing about with your
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profile. So something like <code>CARGO_INCREMENTAL=0</code> can be helpful.</li>
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</ul>
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<p>In case to avoid the issue of <code>addr2line xxx/elf: could not read first record</code> when reading
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collected data from <code>cargo</code>, you may need use the latest version of <code>addr2line</code>:</p>
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<pre><code class="language-bash">cargo install addr2line --features="bin"
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</code></pre>
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<h3 id="gathering-a-perf-profile-from-a-perfrust-langorg-test"><a class="header" href="#gathering-a-perf-profile-from-a-perfrust-langorg-test">Gathering a perf profile from a <code>perf.rust-lang.org</code> test</a></h3>
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<p>Often we want to analyze a specific test from <code>perf.rust-lang.org</code>.
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The easiest way to do that is to use the <a href="https://github.com/rust-lang/rustc-perf">rustc-perf</a>
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benchmarking suite, this approach is described <a href="with_rustc_perf.html">here</a>.</p>
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<p>Instead of using the benchmark suite CLI, you can also profile the benchmarks manually. First,
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you need to clone the <a href="https://github.com/rust-lang/rustc-perf">rustc-perf</a> repository:</p>
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<pre><code class="language-bash">$ git clone https://github.com/rust-lang/rustc-perf
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</code></pre>
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<p>and then find the source code of the test that you want to profile. Sources for the tests
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are found in <a href="https://github.com/rust-lang/rustc-perf/tree/master/collector/compile-benchmarks">the <code>collector/compile-benchmarks</code> directory</a>
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and <a href="https://github.com/rust-lang/rustc-perf/tree/master/collector/runtime-benchmarks">the <code>collector/runtime-benchmarks</code> directory</a>. So let's
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go into the directory of a specific test; we'll use <code>clap-rs</code> as an example:</p>
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<pre><code class="language-bash">cd collector/compile-benchmarks/clap-3.1.6
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</code></pre>
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<p>In this case, let's say we want to profile the <code>cargo check</code>
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performance. In that case, I would first run some basic commands to
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build the dependencies:</p>
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<pre><code class="language-bash"># Setup: first clean out any old results and build the dependencies:
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cargo +<toolchain> clean
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CARGO_INCREMENTAL=0 cargo +<toolchain> check
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</code></pre>
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<p>(Again, <code><toolchain></code> should be replaced with the name of the
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toolchain we made in the first step.)</p>
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<p>Next: we want record the execution time for <em>just</em> the clap-rs crate,
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running cargo check. I tend to use <code>cargo rustc</code> for this, since it
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also allows me to add explicit flags, which we'll do later on.</p>
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<pre><code class="language-bash">touch src/lib.rs
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CARGO_INCREMENTAL=0 perf record -F99 --call-graph dwarf cargo rustc --profile check --lib
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</code></pre>
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<p>Note that final command: it's a doozy! It uses the <code>cargo rustc</code>
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command, which executes rustc with (potentially) additional options;
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the <code>--profile check</code> and <code>--lib</code> options specify that we are doing a
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<code>cargo check</code> execution, and that this is a library (not a binary).</p>
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<p>At this point, we can use <code>perf</code> tooling to analyze the results. For example:</p>
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<pre><code class="language-bash">perf report
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</code></pre>
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<p>will open up an interactive TUI program. In simple cases, that can be
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helpful. For more detailed examination, the <a href="https://github.com/nikomatsakis/perf-focus"><code>perf-focus</code> tool</a>
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can be helpful; it is covered below.</p>
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<p><strong>A note of caution.</strong> Each of the rustc-perf tests is its own special
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snowflake. In particular, some of them are not libraries, in which
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case you would want to do <code>touch src/main.rs</code> and avoid passing
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<code>--lib</code>. I'm not sure how best to tell which test is which to be
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honest.</p>
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<h3 id="gathering-nll-data"><a class="header" href="#gathering-nll-data">Gathering NLL data</a></h3>
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<p>If you want to profile an NLL run, you can just pass extra options to
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the <code>cargo rustc</code> command, like so:</p>
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<pre><code class="language-bash">touch src/lib.rs
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CARGO_INCREMENTAL=0 perf record -F99 --call-graph dwarf cargo rustc --profile check --lib -- -Z borrowck=mir
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</code></pre>
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<h2 id="analyzing-a-perf-profile-with-perf-focus"><a class="header" href="#analyzing-a-perf-profile-with-perf-focus">Analyzing a perf profile with <code>perf focus</code></a></h2>
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<p>Once you've gathered a perf profile, we want to get some information
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about it. For this, I personally use <a href="https://github.com/nikomatsakis/perf-focus">perf focus</a>. It's a kind of
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simple but useful tool that lets you answer queries like:</p>
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<ul>
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<li>"how much time was spent in function F" (no matter where it was called from)</li>
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<li>"how much time was spent in function F when it was called from G"</li>
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<li>"how much time was spent in function F <em>excluding</em> time spent in G"</li>
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<li>"what functions does F call and how much time does it spend in them"</li>
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</ul>
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<p>To understand how it works, you have to know just a bit about
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perf. Basically, perf works by <em>sampling</em> your process on a regular
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basis (or whenever some event occurs). For each sample, perf gathers a
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backtrace. <code>perf focus</code> lets you write a regular expression that tests
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which functions appear in that backtrace, and then tells you which
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percentage of samples had a backtrace that met the regular
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expression. It's probably easiest to explain by walking through how I
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would analyze NLL performance.</p>
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<h3 id="installing-perf-focus"><a class="header" href="#installing-perf-focus">Installing <code>perf-focus</code></a></h3>
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<p>You can install perf-focus using <code>cargo install</code>:</p>
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<pre><code class="language-bash">cargo install perf-focus
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</code></pre>
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<h3 id="example-how-much-time-is-spent-in-mir-borrowck"><a class="header" href="#example-how-much-time-is-spent-in-mir-borrowck">Example: How much time is spent in MIR borrowck?</a></h3>
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<p>Let's say we've gathered the NLL data for a test. We'd like to know
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how much time it is spending in the MIR borrow-checker. The "main"
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function of the MIR borrowck is called <code>do_mir_borrowck</code>, so we can do
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this command:</p>
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<pre><code class="language-bash">$ perf focus '{do_mir_borrowck}'
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Matcher : {do_mir_borrowck}
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Matches : 228
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Not Matches: 542
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Percentage : 29%
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</code></pre>
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<p>The <code>'{do_mir_borrowck}'</code> argument is called the <strong>matcher</strong>. It
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specifies the test to be applied on the backtrace. In this case, the
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<code>{X}</code> indicates that there must be <em>some</em> function on the backtrace
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that meets the regular expression <code>X</code>. In this case, that regex is
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just the name of the function we want (in fact, it's a subset of the name;
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the full name includes a bunch of other stuff, like the module
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path). In this mode, perf-focus just prints out the percentage of
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samples where <code>do_mir_borrowck</code> was on the stack: in this case, 29%.</p>
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<p><strong>A note about c++filt.</strong> To get the data from <code>perf</code>, <code>perf focus</code>
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currently executes <code>perf script</code> (perhaps there is a better
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way...). I've sometimes found that <code>perf script</code> outputs C++ mangled
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names. This is annoying. You can tell by running <code>perf script | head</code> yourself — if you see names like <code>5rustc6middle</code> instead of
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<code>rustc::middle</code>, then you have the same problem. You can solve this
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by doing:</p>
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<pre><code class="language-bash">perf script | c++filt | perf focus --from-stdin ...
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</code></pre>
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<p>This will pipe the output from <code>perf script</code> through <code>c++filt</code> and
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should mostly convert those names into a more friendly format. The
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<code>--from-stdin</code> flag to <code>perf focus</code> tells it to get its data from
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stdin, rather than executing <code>perf focus</code>. We should make this more
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convenient (at worst, maybe add a <code>c++filt</code> option to <code>perf focus</code>, or
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just always use it — it's pretty harmless).</p>
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<h3 id="example-how-much-time-does-mir-borrowck-spend-solving-traits"><a class="header" href="#example-how-much-time-does-mir-borrowck-spend-solving-traits">Example: How much time does MIR borrowck spend solving traits?</a></h3>
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<p>Perhaps we'd like to know how much time MIR borrowck spends in the
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trait checker. We can ask this using a more complex regex:</p>
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<pre><code class="language-bash">$ perf focus '{do_mir_borrowck}..{^rustc::traits}'
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Matcher : {do_mir_borrowck},..{^rustc::traits}
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Matches : 12
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Not Matches: 1311
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Percentage : 0%
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</code></pre>
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<p>Here we used the <code>..</code> operator to ask "how often do we have
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<code>do_mir_borrowck</code> on the stack and then, later, some function whose
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name begins with <code>rustc::traits</code>?" (basically, code in that module). It
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turns out the answer is "almost never" — only 12 samples fit that
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description (if you ever see <em>no</em> samples, that often indicates your
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query is messed up).</p>
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<p>If you're curious, you can find out exactly which samples by using the
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<code>--print-match</code> option. This will print out the full backtrace for
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each sample. The <code>|</code> at the front of the line indicates the part that
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the regular expression matched.</p>
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<h3 id="example-where-does-mir-borrowck-spend-its-time"><a class="header" href="#example-where-does-mir-borrowck-spend-its-time">Example: Where does MIR borrowck spend its time?</a></h3>
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<p>Often we want to do more "explorational" queries. Like, we know that
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MIR borrowck is 29% of the time, but where does that time get spent?
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For that, the <code>--tree-callees</code> option is often the best tool. You
|
|
usually also want to give <code>--tree-min-percent</code> or
|
|
<code>--tree-max-depth</code>. The result looks like this:</p>
|
|
<pre><code class="language-bash">$ 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_borrowck::nll::compute_regions (20% total, 0% self)
|
|
: : | rustc_borrowck::nll::type_check::type_check_internal (13% total, 0% self)
|
|
: : : | core::ops::function::FnOnce::call_once (5% total, 0% self)
|
|
: : : : | rustc_borrowck::nll::type_check::liveness::generate (5% total, 3% self)
|
|
: : : | <rustc_borrowck::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_borrowck::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)
|
|
</code></pre>
|
|
<p>What happens with <code>--tree-callees</code> is that</p>
|
|
<ul>
|
|
<li>we find each sample matching the regular expression</li>
|
|
<li>we look at the code that occurs <em>after</em> the regex match and try
|
|
to build up a call tree</li>
|
|
</ul>
|
|
<p>The <code>--tree-min-percent 3</code> 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. <code>--tree-max-depth</code> can be useful too, it just limits how many
|
|
levels we print.</p>
|
|
<p>For each line, we display the percent of time in that function
|
|
altogether ("total") and the percent of time spent in <strong>just that
|
|
function and not some callee of that function</strong> (self). Usually
|
|
"total" is the more interesting number, but not always.</p>
|
|
<h3 id="relative-percentages"><a class="header" href="#relative-percentages">Relative percentages</a></h3>
|
|
<p>By default, all in perf-focus are relative to the <strong>total program
|
|
execution</strong>. 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.</p>
|
|
<p>That said, sometimes it's useful to get relative percentages, so <code>perf focus</code> offers a <code>--relative</code> 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:</p>
|
|
<pre><code class="language-bash">$ 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_borrowck::nll::compute_regions (47% total, 0% self) [...]
|
|
: | rustc::mir::visit::Visitor::visit_mir (19% total, 15% self) [...]
|
|
: | <rustc_borrowck::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) [...]
|
|
</code></pre>
|
|
<p>Here you see that <code>compute_regions</code> came up as "47% total" — that
|
|
means that 47% of <code>do_mir_borrowck</code> is spent in that function. Before,
|
|
we saw 20% — that's because <code>do_mir_borrowck</code> itself is only 43% of
|
|
the total time (and <code>.47 * .43 = .20</code>).</p>
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