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A perf script for helping to answer queries about how much time a given function occupies, where it is called from, and so forth.
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Cargo.toml declare 1.0, what the hell :) Feb 16, 2018

perf focus is an in-progress tool that can you be used with linux perf to answer queries about runtimes. Note that this tool is under heavy development and you should not expect stable usage at this point. Moreover, this documentation is incomplete, run perf-focus --help for a complete list of options (or, of course, you can read the source).

Install and gather data

The first step is to install the tool:

> cargo install perf-focus

Next, you will want to gather some data by running perf. Use the following settings to get a good backtrace:

> perf record -F 99 -g <some-command-here>

Note: if you are profiling a Rust program, enable debuginfo to ensure that we can get a backtrace.

Critical idea: a matcher

When you use the tool, you must specify a matcher. The matcher lets you filter the samples to find those that take place in a paricular function of interest; this lets you easily analyze cross-cutting parts of the code that are invokved from many places. Here is a simple example:

> perf focus '{^middle::traits}..{^je_}'

This call will analyze the file generated by perf record and report what percentage of samples were taken when some function whose name begins with middle::traits was on the stack and it invoked (transitively) a function whose name began with je_. In the query syntax, {<regex>} matches a single function whose name is given by the embedded regular expression. The , operator first matches The ..M prefix skips over any number of frames before matching M. It can also be used as a binary operator, so that M..N is equivalent to M,..N.

> perf focus '{^a$},{^b$}'

Reports how often a function named a directly called a function named b.

> perf focus '{^a$},!..{^b$}'

Reports how often a function named a was found on the stack without having (transitively) called a function named b.

Profiling rustc queries

If you pass --rustc-query, the tool will apply filter that strips out everything but rustc queries. This is useful for figuring out rustc compilation time. I suggest using this in conjunction with --tree-callees.


> perf focus '{main}' --rustc-query --tree-callees | head
Matcher    : {main}
Matches    : 2690
Not Matches: 0
Percentage : 100%

| matched `{main}` (100% total, 25% self)
: | mir_borrowck<'tcx>> (35% total, 35% self)
: : | normalize_projection_ty<'tcx>> (0% total, 0% self)
: | typeck_item_bodies<'tcx>> (11% total, 0% self)


You can generate callee and caller trees by passing one of the following options.


This will give output showing each function that was called, along with the percentage of time spent in that subtree ("total") as well as in that actual function ("self"). (As always, these are precentages of total program execution.) You can customize these with --tree-max-depth and --tree-min-percent, which are useful for culling uninteresting things.

Example output:

> perf focus '{add_drop_live_constraint}' --tree-callees --tree-max-depth 3 --tree-min-percent 3
Matcher    : {add_drop_live_constraint}
Matches    : 708
Not Matches: 1982
Percentage : 26%

| matched `{add_drop_live_constraint}` (26% total, 0% self)
: | rustc_mir::borrow_check::nll::type_check::TypeChecker::fully_perform_op (25% total, 0% self)
: : | rustc::infer::InferCtxt::commit_if_ok (23% total, 0% self)
: : : | <std::collections::hash::set::HashSet<T, S>>::insert (8% total, 0% self) [...]


You can also generate call graphs by passing one of the following options:


The first option will generate call graphs including all the frames in every matching sample. The --graph-callers option geneates a call graph including only those frames that invoked the matched code. --graph-callees only includes those frame sthat were called by the matched code.

In the graph, each node and edge is labeled with a percentage, indicating the percentage of samples in which it appeared. This percentage is always an absolute percentage across all samples in the run (it is not a percentage of the matching samples, in particular).

By default, the graph includes the top 22 most significant functions (and edges between them). You can include more or less by passing --threshold N (to include the top N functions).

You can use --rename <regex> <match> to munge the names of functions that appear in the graph. This can be useful for stripping parts of the fn name, or coallescing functions:

// convert things like `middle::traits::select::foo::bar` to
// `middle::traits::select`:
--rename '(^middle::traits::[a-zA-Z0-9_]+)::.*' '$1'

// strip the last `...::XXX` suffix:
--rename '::[a-zA-Z0-9_]+$' ''


Instead of a graph, you can use the histogram options to just dump out the most common functions and the percentage of samples in which they appeared (as above, all percentages are absolute):

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