iai-callgrind

Crates.ioiai-callgrind
lib.rsiai-callgrind
version0.11.0
sourcesrc
created_at2023-03-08 22:06:27.685048
updated_at2024-05-09 05:52:34.80999
descriptionHigh-precision and consistent benchmarking framework/harness for Rust
homepagehttps://github.com/iai-callgrind/iai-callgrind
repositoryhttps://github.com/iai-callgrind/iai-callgrind
max_upload_size
id805163
size364,414
(Joining7943)

documentation

README

Iai-Callgrind

High-precision and consistent benchmarking framework/harness for Rust
Released API Docs | Changelog

GitHub branch checks state Crates.io docs.rs MSRV

Iai-Callgrind is a benchmarking framework/harness which primarily uses Valgrind's Callgrind and the other Valgrind tools to provide extremely accurate and consistent measurements of Rust code, making it perfectly suited to run in environments like a CI.

This crate started as a fork of the great Iai crate rewritten to use Valgrind's Callgrind instead of Cachegrind but also adds a lot of other improvements and features.

Table of Contents

Features

  • Precision: High-precision measurements allow you to reliably detect very small optimizations of your code
  • Consistency: Iai-Callgrind can take accurate measurements even in virtualized CI environments
  • Performance: Since Iai-Callgrind only executes a benchmark once, it is typically a lot faster to run than benchmarks measuring the execution and wall time
  • Regression: Iai-Callgrind reports the difference between benchmark runs to make it easy to spot detailed performance regressions and improvements. You can define limits for specific event kinds to fail a benchmark if that limit is breached.
  • CPU and Cache Profiling: Iai-Callgrind generates a Callgrind profile of your code while benchmarking, so you can use Callgrind-compatible tools like callgrind_annotate or the visualizer kcachegrind to analyze the results in detail.
  • Memory Profiling: You can run other Valgrind tools like DHAT: a dynamic heap analysis tool and Massif: a heap profiler with the Iai-Callgrind benchmarking framework. Their profiles are stored next to the callgrind profiles and are ready to be examined with analyzing tools like dh_view.html, ms_print and others.
  • Visualization: Iai-Callgrind is capable of creating regular and differential flamegraphs from the Callgrind output format.
  • Valgrind Client Requests: Support of zero overhead Valgrind Client Requests (compared to native valgrind client requests overhead) on many targets
  • Stable-compatible: Benchmark your code without installing nightly Rust

Installation

In order to use Iai-Callgrind, you must have Valgrind installed. This means that Iai-Callgrind cannot be used on platforms that are not supported by Valgrind.

To start with Iai-Callgrind, add the following to your Cargo.toml file:

[dev-dependencies]
iai-callgrind = "0.11.0"

To be able to run the benchmarks you'll also need the iai-callgrind-runner binary installed somewhere in your $PATH, for example with

cargo install --version 0.11.0 iai-callgrind-runner

or with binstall

cargo binstall iai-callgrind-runner@0.11.0

There's also the possibility to install the binary somewhere else and point the IAI_CALLGRIND_RUNNER environment variable to the absolute path of the iai-callgrind-runner binary like so:

cargo install --version 0.11.0 --root /tmp iai-callgrind-runner
IAI_CALLGRIND_RUNNER=/tmp/bin/iai-callgrind-runner cargo bench --bench my-bench

When updating the iai-callgrind library, you'll also need to update iai-callgrind-runner and vice-versa or else the benchmark runner will exit with an error. Otherwise, there is no need to interact with iai-callgrind-runner as it is just an implementation detail.

Since the iai-callgrind-runner version must match the iai-callgrind library version it's best to automate this step in the CI. A job step in the github actions ci could look like this

- name: Install iai-callgrind-runner
  run: |
    version=$(cargo metadata --format-version=1 |\
      jq '.packages[] | select(.name == "iai-callgrind").version' |\
      tr -d '"'
    )
    cargo install iai-callgrind-runner --version $version

If you want to make use of the Valgrind Client Requests you need libclang (clang >= 5.0) installed. See also the requirements of bindgen) and of cc.

iai-callgrind needs the debug symbols when running the benchmarks. There are multiple places where you can configure profiles, see the Benchmarking section below for more details.

Benchmarking

iai-callgrind can be used to benchmark libraries or binaries. Library benchmarks benchmark functions and methods of a crate and binary benchmarks benchmark the executables of a crate. The different benchmark types cannot be intermixed in the same benchmark file but having different benchmark files for library and binary benchmarks is no problem. More on that in the following sections.

For a quickstart and examples of benchmarking libraries see the Library Benchmark Section and for executables see the Binary Benchmark Section. Read the docs!

As mentioned in above in the Installation section, it's required to run the benchmarks with debugging symbols switched on. For example in your ~/.cargo/config or your project's Cargo.toml:

[profile.bench]
debug = true

Now, all benchmarks you run with cargo bench include the debug symbols. (See also Cargo Profiles and Cargo Config).

It's required that settings like strip = true or other configuration options stripping the debug symbols need to be disabled explicitly for the bench profile if you have changed this option for the release profile. For example:

[profile.release]
strip = true

[profile.bench]
debug = true
strip = false

Per default, iai-callgrind runs all benchmarks with Valgrind's cache simulation turned on (--cache-sim=yes) in order to calculate an estimation for the total cpu cycles. See also the Metrics Output section for more infos. However, if you want to opt-out of the cache simulation and the calculation of estimated cycles, you can easily do so within the benchmark with the LibraryBenchmarkConfig (or BinaryBenchmarkConfig):

LibraryBenchmarkConfig::default().raw_callgrind_args(["--cache-sim=no"])

in the cli with environment variables

IAI_CALLGRIND_CALLGRIND_ARGS="--cache-sim=no" cargo bench

or with arguments

cargo bench -- --callgrind-args="--cache-sim=no"

To be able to run the latter command, some additional configuration might be needed.

Library Benchmarks

Use this scheme if you want to micro-benchmark specific functions of your crate's library.

Important default behavior

The environment variables are cleared before running a library benchmark. Have a look into the Configuration section if you need to change that behavior.

Quickstart

Add

[[bench]]
name = "my_benchmark"
harness = false

to your Cargo.toml file and then create a file with the same name in benches/my_benchmark.rs with the following content:

use iai_callgrind::{main, library_benchmark_group, library_benchmark};
use std::hint::black_box;

fn fibonacci(n: u64) -> u64 {
    match n {
        0 => 1,
        1 => 1,
        n => fibonacci(n - 1) + fibonacci(n - 2),
    }
}

#[library_benchmark]
#[bench::short(10)]
#[bench::long(30)]
fn bench_fibonacci(value: u64) -> u64 {
    black_box(fibonacci(value))
}

library_benchmark_group!(
    name = bench_fibonacci_group;
    benchmarks = bench_fibonacci
);

main!(library_benchmark_groups = bench_fibonacci_group);

Note that it is important to annotate the benchmark functions with #[library_benchmark]. But, there's no need to annotate benchmark functions with inline(never) anymore. The bench attribute takes any expression what includes function calls. The following would have worked too and avoids setup code within the benchmark function eliminating the need to pass toggle-collect arguments to callgrind:

fn some_setup_func(value: u64) -> u64 {
    value
}

#[library_benchmark]
#[bench::long(some_setup_func(30))]
fn bench_fibonacci(value: u64) -> u64 {
    black_box(fibonacci(value))
}

Now, you can run this benchmark with cargo bench --bench my_benchmark in your project root and you should see something like this:

test_lib_bench_readme_example_fibonacci::bench_fibonacci_group::bench_fibonacci short:10
  Instructions:                1733|N/A             (*********)
  L1 Hits:                     2359|N/A             (*********)
  L2 Hits:                        0|N/A             (*********)
  RAM Hits:                       2|N/A             (*********)
  Total read+write:            2361|N/A             (*********)
  Estimated Cycles:            2429|N/A             (*********)
test_lib_bench_readme_example_fibonacci::bench_fibonacci_group::bench_fibonacci long:30
  Instructions:            26214733|N/A             (*********)
  L1 Hits:                 35638617|N/A             (*********)
  L2 Hits:                        0|N/A             (*********)
  RAM Hits:                       4|N/A             (*********)
  Total read+write:        35638621|N/A             (*********)
  Estimated Cycles:        35638757|N/A             (*********)

In addition, you'll find the callgrind output in target/iai, if you want to investigate further with a tool like callgrind_annotate. When running the same benchmark again, the output will report the differences between the current and the previous run. Say you've made change to the fibonacci function, then you may see something like this:

test_lib_bench_readme_example_fibonacci::bench_fibonacci_group::bench_fibonacci short:10
  Instructions:                2804|1733            (+61.8003%) [+1.61800x]
  L1 Hits:                     3815|2359            (+61.7211%) [+1.61721x]
  L2 Hits:                        0|0               (No change)
  RAM Hits:                       2|2               (No change)
  Total read+write:            3817|2361            (+61.6688%) [+1.61669x]
  Estimated Cycles:            3885|2429            (+59.9424%) [+1.59942x]
test_lib_bench_readme_example_fibonacci::bench_fibonacci_group::bench_fibonacci long:30
  Instructions:            16201596|26214733        (-38.1966%) [-1.61803x]
  L1 Hits:                 22025878|35638617        (-38.1966%) [-1.61803x]
  L2 Hits:                        0|0               (No change)
  RAM Hits:                       3|4               (-25.0000%) [-1.33333x]
  Total read+write:        22025881|35638621        (-38.1966%) [-1.61803x]
  Estimated Cycles:        22025983|35638757        (-38.1965%) [-1.61803x]
Specify multiple benchmarks at once with the #[benches] attribute

Let's start with an example:

fn setup_worst_case_array(start: i32) -> Vec<i32> {
    if start.is_negative() {
        (start..0).rev().collect()
    } else {
        (0..start).rev().collect()
    }
}

#[library_benchmark]
#[benches::multiple(vec![1], vec![5])]
#[benches::with_setup(args = [1, 5], setup = setup_worst_case_array)]
fn bench_bubble_sort_with_benches_attribute(input: Vec<i32>) -> Vec<i32> {
    black_box(bubble_sort(input))
}

Usually the arguments are passed directly to the benchmarking function as it can be seen in the #[benches::multiple(...)] case. In #[benches::with_setup(...)], the arguments are passed to the setup function instead. The above #[library_benchmark] is pretty much the same as

#[library_benchmark]
#[bench::multiple_0(vec![1])]
#[bench::multiple_1(vec![5])]
#[bench::with_setup_0(setup_worst_case_array(1)])]
#[bench::with_setup_1(setup_worst_case_array(5)])]
fn bench_bubble_sort_with_benches_attribute(input: Vec<i32>) -> Vec<i32> {
    black_box(bubble_sort(input))
}

but a lot more concise especially if a lot of values are passed to the same setup function.

Comparing benchmark functions

Comparing benchmark functions is supported via the optional library_benchmark_group! argument compare_by_id (The default value for compare_by_id is false). Only benches with the same id are compared, which allows to single out cases which don't need to be compared. In the following example, the case_3 and multiple bench are compared with each other in addition to the usual comparison with the previous run:

#[library_benchmark]
#[bench::case_3(vec![1, 2, 3])]
#[benches::multiple(args = [vec![1, 2], vec![1, 2, 3, 4]])]
fn bench_bubble_sort_best_case(input: Vec<i32>) -> Vec<i32> {
    black_box(bubble_sort(input))
}

#[library_benchmark]
#[bench::case_3(vec![3, 2, 1])]
#[benches::multiple(args = [vec![2, 1], vec![4, 3, 2, 1]])]
fn bench_bubble_sort_worst_case(input: Vec<i32>) -> Vec<i32> {
    black_box(bubble_sort(input))
}

library_benchmark_group!(
    name = bench_bubble_sort;
    compare_by_id = true;
    benchmarks = bench_bubble_sort_best_case, bench_bubble_sort_worst_case
);

Note if compare_by_id is true, all benchmark functions are compared with each other, so you are not limited to two benchmark functions per comparison group.

Here's a curated excerpt from the output of the above example to see what is happening:

test_lib_bench_compare::bubble_sort_compare::bench_bubble_sort_best_case case_3:vec! [1, 2, 3]
  Instructions:                  94|N/A             (*********)
  L1 Hits:                      124|N/A             (*********)
  L2 Hits:                        0|N/A             (*********)
  RAM Hits:                       4|N/A             (*********)
  Total read+write:             128|N/A             (*********)
  Estimated Cycles:             264|N/A             (*********)
test_lib_bench_compare::bubble_sort_compare::bench_bubble_sort_worst_case case_3:vec! [3, 2, 1]
  Instructions:                 103|N/A             (*********)
  L1 Hits:                      138|N/A             (*********)
  L2 Hits:                        0|N/A             (*********)
  RAM Hits:                       5|N/A             (*********)
  Total read+write:             143|N/A             (*********)
  Estimated Cycles:             313|N/A             (*********)
  Comparison with bench_bubble_sort_best_case case_3:vec! [1, 2, 3]
  Instructions:                  94|103             (-8.73786%) [-1.09574x]
  L1 Hits:                      124|138             (-10.1449%) [-1.11290x]
  L2 Hits:                        0|0               (No change)
  RAM Hits:                       4|5               (-20.0000%) [-1.25000x]
  Total read+write:             128|143             (-10.4895%) [-1.11719x]
  Estimated Cycles:             264|313             (-15.6550%) [-1.18561x]

Here's the procedure of the comparison algorithm:

  1. Run all benches in the first benchmark function
  2. Run the first bench in the second benchmark function and if there is a bench in the first benchmark function with the same id compare them
  3. Run the second bench in the second benchmark function ...
  4. ...
  5. Run the first bench in the third benchmark function and if there is a bench in the first benchmark function with the same id compare them. If there is a bench with the same id in the second benchmark function compare them.
  6. Run the second bench in the third benchmark function ...
  7. and so on ... until all benches are compared with each other

Neither the order nor the amount of benches within the benchmark functions matters, so it is not strictly necessary to mirror the bench ids of the first benchmark function in the second, third, etc. benchmark function.

Examples

For a fully documented and working benchmark see the test_lib_bench_groups benchmark file and read the library documentation!

Configuration

It's possible to configure some of the behavior of iai-callgrind. See the docs of LibraryBenchmarkConfig for more details. At top-level with the main! macro:

main!(
    config = LibraryBenchmarkConfig::default();
    library_benchmark_groups = ...
);

At group-level:

library_benchmark_groups!(
    name = some_name;
    config = LibraryBenchmarkConfig::default();
    benchmarks = ...
);

At library_benchmark level:

#[library_benchmark(config = LibraryBenchmarkConfig::default())]
...

and at bench level:

#[library_benchmark]
#[bench::some_id(args = (1, 2), config = LibraryBenchmarkConfig::default()]
...

The config at bench level overwrites the config at library_benchmark level. The config at library_benchmark level overwrites the config at group level and so on. Note that configuration values like envs are additive and don't overwrite configuration values of higher levels.

Binary Benchmarks

Use this scheme to benchmark one or more binaries of your crate. If you really like to, it's possible to benchmark any executable file in the PATH or any executable specified with an absolute path.

It's also possible to run functions of the same benchmark file before and after all benchmarks or to setup and teardown any benchmarked binary.

Unlike Library Benchmarks, there are no setup costs for binary benchmarks to pay attention at, since each benchmark run's command is passed directly to valgrind's callgrind.

Temporary Workspace and other important default behavior

Per default, all binary benchmarks and the before, after, setup and teardown functions are executed in a temporary directory. See the Switching off the sandbox for changing this behavior.

Also, the environment variables of benchmarked binaries are cleared before the benchmark is run. See also Environment variables for how to pass environment variables to the benchmarked binary.

Quickstart

Suppose your crate's binary is named benchmark-tests-printargs and you have a fixtures directory in fixtures with a file test1.txt in it:

use iai_callgrind::{
    binary_benchmark_group, main, Arg, BinaryBenchmarkConfig, BinaryBenchmarkGroup,
    Fixtures, Run,
};

fn my_setup() {
    println!("We can put code in here which will be run before each benchmark run");
}

// We specify a cmd `"benchmark-tests-exe"` for the whole group which is a
// binary of our crate. This eliminates the need to specify a `cmd` for each
// `Run` later on and we can use the auto-discovery of a crate's binary at group
// level. We'll also use the `setup` argument to run a function before each of
// the benchmark runs.
binary_benchmark_group!(
    name = my_exe_group;
    setup = my_setup;
    // This directory will be copied into the root of the sandbox (as `fixtures`)
    config = BinaryBenchmarkConfig::default().fixtures(Fixtures::new("fixtures"));
    benchmark =
        |"benchmark-tests-printargs", group: &mut BinaryBenchmarkGroup| {
            setup_my_exe_group(group)
    }
);

// Working within a macro can be tedious sometimes so we moved the setup code
// into this method
fn setup_my_exe_group(group: &mut BinaryBenchmarkGroup) {
    group
        // Setup our first run doing something with our fixture `test1.txt`. The
        // id (here `do foo with test1`) of an `Arg` has to be unique within the
        // same group
        .bench(Run::with_arg(Arg::new(
            "do foo with test1",
            ["--foo=fixtures/test1.txt"],
        )))

        // Setup our second run with two positional arguments. We're not
        // interested in anything happening before the main function in
        // `benchmark-tests-printargs`, so we set the entry_point.
        .bench(
            Run::with_arg(
                Arg::new(
                    "positional arguments",
                    ["foo", "foo bar"],
                )
            ).entry_point("benchmark_tests_printargs::main")
        )

        // Our last run doesn't take an argument at all.
        .bench(Run::with_arg(Arg::empty("no argument")));
}

// As last step specify all groups we want to benchmark in the main! macro
// argument `binary_benchmark_groups`. The main macro is always needed and
// finally expands to a benchmarking harness
main!(binary_benchmark_groups = my_exe_group);

You're ready to run the benchmark with cargo bench --bench my_binary_benchmark.

The output of this benchmark run could look like this:

my_binary_benchmark::my_exe_group do foo with test1:benchmark-tests-printargs "--foo=fixt...
  Instructions:              331082|N/A             (*********)
  L1 Hits:                   442452|N/A             (*********)
  L2 Hits:                      720|N/A             (*********)
  RAM Hits:                    3926|N/A             (*********)
  Total read+write:          447098|N/A             (*********)
  Estimated Cycles:          583462|N/A             (*********)
my_binary_benchmark::my_exe_group positional arguments:benchmark-tests-printargs foo "foo ba...
  Instructions:                3906|N/A             (*********)
  L1 Hits:                     5404|N/A             (*********)
  L2 Hits:                        8|N/A             (*********)
  RAM Hits:                      91|N/A             (*********)
  Total read+write:            5503|N/A             (*********)
  Estimated Cycles:            8629|N/A             (*********)
my_binary_benchmark::my_exe_group no argument:benchmark-tests-printargs
  Instructions:              330070|N/A             (*********)
  L1 Hits:                   441031|N/A             (*********)
  L2 Hits:                      716|N/A             (*********)
  RAM Hits:                    3925|N/A             (*********)
  Total read+write:          445672|N/A             (*********)
  Estimated Cycles:          581986|N/A             (*********)

You'll find the callgrind output files of each run of the benchmark my_binary_benchmark of the group my_exe_group in target/iai/$CARGO_PKG_NAME/my_binary_benchmark/my_exe_group.

Configuration

Much like the configuration of Library Benchmarks it's possible to configure binary benchmarks at top-level in the main! macro and at group-level in the binary_benchmark_groups! with the config = ...; argument. In contrast to library benchmarks, binary benchmarks can be configured at a lower and last level within Run directly.

Auto-discovery of a crate's binaries

Auto-discovery of a crate's binary works only when specifying the name of it at group level.

binary_benchmark_group!(
    name = my_exe_group;
    benchmark = |"my-exe", group: &mut BinaryBenchmarkGroup| {});

If you don't like specifying a default command at group level, you can use env!("CARGO_BIN_EXE_name) at Run-level like so:

binary_benchmark_group!(
    name = my_exe_group;
    benchmark = |group: &mut BinaryBenchmarkGroup| {
        group.bench(Run::with_cmd(env!("CARGO_BIN_EXE_my-exe"), Arg::empty("some id")));
    });

A benchmark run of a binary exits with error

Usually, if a benchmark exits with a non-zero exit code, the whole benchmark run fails and stops. If you expect the exit code of your benchmarked binary to be different from 0, you can set the expected exit code with Options at Run-level

binary_benchmark_group!(
    name = my_exe_group;
    benchmark = |"my-exe", group: &mut BinaryBenchmarkGroup| {
        group.bench(
            Run::with_arg(
                Arg::empty("some id")
            )
            .options(Options::default().exit_with(ExitWith::Code(100)))
        );
    });

Environment variables

Per default, the environment variables are cleared before running a benchmark.

It's possible to specify environment variables at Run-level which should be available in the binary:

binary_benchmark_group!(
    name = my_exe_group;
    benchmark = |"my-exe", group: &mut BinaryBenchmarkGroup| {
        group.bench(Run::with_arg(Arg::empty("some id")).envs(["KEY=VALUE", "KEY"]));
    });

Environment variables specified in the envs array are usually KEY=VALUE pairs. But, if env_clear is true (what is the default), single KEYs are environment variables to pass-through to the cmd. Pass-through environment variables are ignored if they don't exist in the root environment.

Switching off the sandbox

Per default, all binary benchmarks and the before, after, setup and teardown functions are executed in a temporary directory. This behavior can be switched off at group-level:

binary_benchmark_group!(
    name = my_exe_group;
    benchmark = |group: &mut BinaryBenchmarkGroup| {
        group.sandbox(false);
    });

Examples

See the test_bin_bench_groups benchmark file of this project for a working example.

Performance Regressions

With Iai-Callgrind you can define limits for each event kinds over which a performance regression can be assumed. There are no default regression checks and you have to opt-in with a RegressionConfig at benchmark level or at a global level with Command-line arguments or Environment variables.

A performance regression check consists of an EventKind and a percentage over which a regression is assumed. If the percentage is negative, then a regression is assumed to be below this limit. The default EventKind is EventKind::Ir with a value of +10%.For example, in a Library Benchmark, let's overwrite the default limit with a global limit of +5% for the total instructions executed (the Ir event kind):

main!(
    config = LibraryBenchmarkConfig::default()
        .regression(
            RegressionConfig::default()
                .limits([(EventKind::Ir, 5.0)])
        );
    library_benchmark_groups = some_group
);

For example SQLite uses mainly cpu instructions to measure performance improvements (and regressions).

For more details on regression checks consult the iai-callgrind docs.

Valgrind Tools

In addition to the default benchmarks, you can use the Iai-Callgrind framework to run other Valgrind profiling Tools like DHAT, Massif and the experimental BBV but also Memcheck, Helgrind and DRD if you need to check memory and thread safety of benchmarked code. See also the Valgrind User Manual for more details and command line arguments. The additional tools can be specified in LibraryBenchmarkConfig, BinaryBenchmarkConfig or Run. For example to run DHAT for all library benchmarks:

use iai_callgrind::{
    library_benchmark, library_benchmark_group, main, LibraryBenchmarkConfig, Tool,
    ValgrindTool
};

#[library_benchmark]
fn some_func() {
    println!("Hello, World!");
}

library_benchmark_group!(name = some_group; benchmarks = some_func);

main!(
    config = LibraryBenchmarkConfig::default()
                .tool(Tool::new(ValgrindTool::DHAT));
    library_benchmark_groups = some_group
);

All tools which produce an ERROR SUMMARY (Memcheck, DRD, Helgrind) have --error-exitcode=201 (See also) set, so if there are any errors, the benchmark run fails with 201. You can overwrite this default with

Tool::new(ValgrindTool::Memcheck).args("--error-exitcode=0")

which would restore the default of 0 from valgrind.

Valgrind Client Requests

iai-callgrind ships with it's own interface to Valgrind's Client Request Mechanism. iai-callgrind's client requests have (compared to the valgrind's client requests used in C code) zero overhead on many targets which are also natively supported by valgrind. My opinion may be biased, but compared to other crates that offer an interface to valgrind's client requests, iai-callgrind provides the most complete and best performant implementation.

Client requests are deactivated by default but can be activated with the client_requests feature.

[dev-dependencies]
iai-callgrind = { version = "0.11.0", features = ["client_requests"] }

If you need the client requests in your production code, you usually don't want them to do anything when not running under valgrind with iai-callgrind benchmarks. You can achieve that by adding iai-callgrind with the client_requests_defs feature to your runtime dependencies and with the client_requests feature to your dev-dependencies like so:

[dependencies]
iai-callgrind = { version = "0.11.0", default-features = false, features = [
    "client_requests_defs"
] }

[dev-dependencies]
iai-callgrind = { version = "0.11.0", features = ["client_requests"] }

With just the client_requests_defs feature activated, the client requests compile down to nothing and don't add any overhead to your production code. It simply provides the "definitions", method signatures and macros without body. Only with the activated client_requests feature they will be actually executed. Note that the client requests do not depend on any other part of iai-callgrind, so you could even use the client requests without the rest of iai-callgrind.

Use them in your code for example like so:

use iai_callgrind::client_requests;

fn main() {
    // Start callgrind event counting if not already started earlier
    client_requests::callgrind::start_instrumentation();

    // do something important

    // Toggle callgrind event counting off
    client_requests::callgrind::toggle_collect();
}

When building iai-callgrind with client requests, the valgrind header files must exist in your standard include path (most of the time /usr/include). This is usually the case if you've installed valgrind with your distribution's package manager. If not, you can point the IAI_CALLGRIND_VALGRIND_INCLUDE or IAI_CALLGRIND_<triple>_VALGRIND_INCLUDE environment variables to the include path. So, if the headers can be found in /home/foo/repo/valgrind/{valgrind.h, callgrind.h, ...}, the correct include path would be IAI_CALLGRIND_VALGRIND_INCLUDE=/home/foo/repo (not /home/foo/repo/valgrind)

This was just a small introduction, please see the docs for more details!

Flamegraphs

Flamegraphs are opt-in and can be created if you pass a FlamegraphConfig to the BinaryBenchmarkConfig, Run or LibraryBenchmarkConfig. Callgrind flamegraphs are meant as a complement to valgrind's visualization tools callgrind_annotate and kcachegrind.

Callgrind flamegraphs show the inclusive costs for functions and a single EventKind (default is EventKind::Ir), similar to callgrind_annotate but in a nicer (and clickable) way. Especially, differential flamegraphs facilitate a deeper understanding of code sections which cause a bottleneck or a performance regressions etc.

The produced flamegraph *.svg files are located next to the respective callgrind output file in the target/iai directory.

Command-line arguments and environment variables

It's possible to pass arguments to iai-callgrind separated by -- (cargo bench -- ARGS). If you're running into the error Unrecognized Option, see the FAQ. For a complete rundown of possible arguments, execute cargo bench --bench <benchmark> -- --help. Almost all command-line arguments have a corresponding environment variable. The environment variables which don't have a corresponding command-line argument are:

  • IAI_CALLGRIND_COLOR: Control the colored output of iai-callgrind. (Default is auto)
  • IAI_CALLGRIND_LOG: Define the log level (Default is WARN)

Comparing with baselines

Usually, two consecutive benchmark runs let iai-callgrind compare these two runs. It's sometimes desirable to compare the current benchmark run against a static reference, instead. For example, if you're working longer on the implementation of a feature, you may wish to compare against a baseline from another branch or the commit from which you started off hacking on your new feature to make sure you haven't introduced performance regressions. iai-callgrind offers such custom baselines. If you are familiar with criterion.rs, the following command line arguments should also be very familiar to you:

  • --save-baseline=NAME: Compare against the NAME baseline if present and then overwrite it. (env: IAI_CALLGRIND_SAVE_BASELINE)
  • --baseline=NAME: Compare against the NAME baseline without overwriting it (env: IAI_CALLGRIND_BASELINE)
  • --load-baseline=NAME: Load the NAME baseline as the new data set instead of creating a new one. This options needs also --baseline=NAME to be present. (env: IAI_CALLGRIND_LOAD_BASELINE)

If NAME is not present, NAME defaults to default.

For example to create a static reference from the main branch and compare it:

git checkout main
cargo bench --bench <benchmark> -- --save-baseline=main
git checkout feature
# ... HACK ... HACK
cargo bench --bench <benchmark> -- --baseline main

Machine-readable output

With --output-format=default|json|pretty-json (env: IAI_CALLGRIND_OUTPUT_FORMAT) you can change the terminal output format to the machine-readable json format. The json schema fully describing the json output is stored in summary.v2.schema.json. Each line of json output (if not pretty-json) is a summary of a single benchmark and you may want to combine all benchmarks in an array. You can do so for example with jq

cargo bench -- --output-format=json | jq -s

which transforms {...}\n{...} into [{...},{...}].

Instead of or in addition to changing the terminal output, it's possible to save a summary file for each benchmark with --save-summary=json|pretty-json (env: IAI_CALLGRIND_SAVE_SUMMARY). The summary.json files are stored next to the usual benchmark output files in the target/iai directory.

Changing the color output

The terminal output is colored per default but follows the value for the IAI_CALLGRIND_COLOR environment variable. If IAI_CALLGRIND_COLOR is not set, CARGO_TERM_COLOR is also tried. Accepted values are: always, never, auto (default). So, disabling colors can be achieved with setting IAI_CALLGRIND_COLOR or CARGO_TERM_COLOR=never.

Changing the logging output

This library uses env_logger and the default logging level WARN. To set the logging level to something different, set the environment variable IAI_CALLGRIND_LOG for example to IAI_CALLGRIND_LOG=DEBUG. Accepted values are: error, warn (default), info, debug, trace. The logging output is colored per default but follows the settings of IAI_CALLGRIND_COLOR and CARGO_TERM_COLOR (In this order of precedence). See also the documentation of env_logger.

Features and differences to Iai

This crate is built on the same idea like the original Iai, but over the time applied a lot of improvements. The biggest difference is, that it uses Callgrind under the hood instead of Cachegrind.

More stable metrics

Iai-Callgrind has even more precise and stable metrics across different systems. It achieves this by

  • only counting events of function calls within the benchmarking function. This behavior virtually encapsulates the benchmark function and separates the benchmark from the surrounding code.
  • separating the iai library with the main macro from the actual runner. This is the reason for the extra installation step of iai-callgrind-runner but before this separation even small changes in the iai library had effects on the benchmarks under test.

Below a local run of one of the benchmarks of this library

$ cd iai-callgrind
$ cargo bench --bench test_lib_bench_readme_example_fibonacci
test_lib_bench_readme_example_fibonacci::bench_fibonacci_group::bench_fibonacci short:10
  Instructions:                1733|N/A             (*********)
  L1 Hits:                     2359|N/A             (*********)
  L2 Hits:                        0|N/A             (*********)
  RAM Hits:                       2|N/A             (*********)
  Total read+write:            2361|N/A             (*********)
  Estimated Cycles:            2429|N/A             (*********)
test_lib_bench_readme_example_fibonacci::bench_fibonacci_group::bench_fibonacci long:30
  Instructions:            26214733|N/A             (*********)
  L1 Hits:                 35638617|N/A             (*********)
  L2 Hits:                        0|N/A             (*********)
  RAM Hits:                       4|N/A             (*********)
  Total read+write:        35638621|N/A             (*********)
  Estimated Cycles:        35638757|N/A             (*********)

For comparison, the output of the same benchmark but in the github CI is producing the same results. Usually, there's almost no difference between a CI run and a local run what makes benchmark runs and performance improvements of the benchmarked code even more comparable across systems.

Cleaner output of Valgrind's annotation tools

The now obsolete calibration run needed with Iai has just fixed the summary output of Iai itself, but the output of cg_annotate was still cluttered by the setup functions and metrics. The callgrind_annotate output produced by Iai-Callgrind is far cleaner and centered on the actual function under test.

Rework the metrics output

The statistics of the benchmarks are mostly not compatible with the original Iai anymore although still related. They now also include some additional information:

test_lib_bench_readme_example_fibonacci::bench_fibonacci_group::bench_fibonacci short:10
  Instructions:                1733|N/A             (*********)
  L1 Hits:                     2359|N/A             (*********)
  L2 Hits:                        0|N/A             (*********)
  RAM Hits:                       2|N/A             (*********)
  Total read+write:            2361|N/A             (*********)
  Estimated Cycles:            2429|N/A             (*********)

There is an additional line Total read+write which summarizes all event counters of the lines with Hits above it and the L1 Accesses line changed to L1 Hits.

In detail:

Total read+write = L1 Hits + L2 Hits + RAM Hits.

The formula for the Estimated Cycles hasn't changed and uses Itamar Turner-Trauring's formula from https://pythonspeed.com/articles/consistent-benchmarking-in-ci/:

Estimated Cycles = L1 Hits + 5 × (L2 Hits) + 35 × (RAM Hits)

For further details about how the caches are simulated and more, see the documentation of Callgrind

What hasn't changed

Iai-Callgrind cannot completely remove the influences of setup changes. However, these effects shouldn't be significant anymore.

FAQ

I'm getting the error Sentinel ... not found

You've most likely disabled creating debug symbols in your cargo bench profile. This can originate in an option you've added to the release profile since the bench profile inherits the release profile. For example, if you've added strip = true to your release profile which is perfectly fine, you need to disable this option in your bench profile to be able to run iai-callgrind benchmarks. See also the Benchmarking section for a more thorough example.

Running cargo bench results in an "Unrecognized Option" error

For cargo bench -- --some-valid-arg to work you can either specify the benchmark with --bench BENCHMARK, for example cargo bench --bench my_iai_benchmark -- --callgrind-args="--collect-bus=yes" or add the following to your Cargo.toml:

[lib]
bench = false

and if you have binaries

[[bin]]
name = "my-binary"
path = "src/bin/my-binary.rs"
bench = false

Setting bench = false disables the creation of the implicit default libtest harness which is added even if you haven't used #[bench] functions in your library or binary. Naturally, the default harness doesn't know of the iai-callgrind arguments and aborts execution printing the Unrecognized Option error.

If you cannot or don't want to add bench = false to your Cargo.toml, you can alternatively use environment variables. For every command-line argument exists a corresponding environment variable.

Contributing

A guideline about contributing to iai-callgrind can be found in the CONTRIBUTING.md file.

See also

Credits

Iai-Callgrind is forked from https://github.com/bheisler/iai and was originally written by Brook Heisler (@bheisler).

Iai-Callgrind wouldn't be possible without Valgrind.

License

Iai-Callgrind is like Iai dual licensed under the Apache 2.0 license and the MIT license at your option.

According to Valgrind's documentation:

The Valgrind headers, unlike most of the rest of the code, are under a BSD-style license so you may include them without worrying about license incompatibility.

We have included the original license where we make use of the original header files.

Commit count: 663

cargo fmt