Crate iai_callgrind

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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.

§Table of contents

§Characteristics

  • 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-clock time
  • Regression: Iai-Callgrind reports the difference between benchmark runs to make it easy to spot detailed performance regressions and improvements.
  • 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

§Benchmarking

iai-callgrind can be divided into two sections: Benchmarking the library and its public functions and benchmarking of the binaries of a crate.

§Library Benchmarks

Use this scheme of the main macro if you want to benchmark functions of your crate’s library.

§Important default behavior

The environment variables are cleared before running a library benchmark. See also the Configuration section below if you need to change that behavior.

§Quickstart (#library-benchmarks)
use iai_callgrind::{
    library_benchmark, library_benchmark_group, main, LibraryBenchmarkConfig
};
use std::hint::black_box;

// Our function we want to test. Just assume this is a public function in your
// library.
fn bubble_sort(mut array: Vec<i32>) -> Vec<i32> {
    for i in 0..array.len() {
        for j in 0..array.len() - i - 1 {
            if array[j + 1] < array[j] {
                array.swap(j, j + 1);
            }
        }
    }
    array
}

// This function is used to create a worst case array we want to sort with our
// implementation of bubble sort
fn setup_worst_case_array(start: i32) -> Vec<i32> {
    if start.is_negative() {
        (start..0).rev().collect()
    } else {
        (0..start).rev().collect()
    }
}

// The #[library_benchmark] attribute let's you define a benchmark function which you
// can later use in the `library_benchmark_groups!` macro.
#[library_benchmark]
fn bench_bubble_sort_empty() -> Vec<i32> {
    // The `black_box` is needed to tell the compiler to not optimize what's inside
    // black_box or else the benchmarks might return inaccurate results.
    black_box(bubble_sort(black_box(vec![])))
}

// This benchmark uses the `bench` attribute to setup benchmarks with different
// setups. The big advantage is, that the setup costs and event counts aren't
// attributed to the benchmark (and opposed to the old api we don't have to deal with
// callgrind arguments, toggles, ...)
#[library_benchmark]
#[bench::empty(vec![])]
#[bench::worst_case_6(vec![6, 5, 4, 3, 2, 1])]
// Function calls are fine too
#[bench::worst_case_4000(setup_worst_case_array(4000))]
// The argument of the benchmark function defines the type of the argument from the
// `bench` cases.
fn bench_bubble_sort(array: Vec<i32>) -> Vec<i32> {
    // Note `array` is not put in a `black_box` because that's already done for you.
    black_box(bubble_sort(array))
}

// You can use the `benches` attribute to specify multiple benchmark runs in one go. You can
// specify multiple `benches` attributes or mix the `benches` attribute with `bench`
// attributes.
#[library_benchmark]
// This is the simple form. Each `,`-separated element is another benchmark run and is
// passed to the benchmarking function as parameter. So, this is the same as specifying
// two `#[bench]` attributes #[bench::multiple_0(vec![1])] and #[bench::multiple_1(vec![5])].
#[benches::multiple(vec![1], vec![5])]
// You can also use the `args` argument to achieve the same. Using `args` is necessary if you
// also want to specify a `config` or `setup` function.
#[benches::with_args(args = [vec![1], vec![5]], config = LibraryBenchmarkConfig::default())]
// Usually, each element in `args` is passed directly to the benchmarking function. You can
// instead reroute them to a `setup` function. In that case the (black boxed) return value of
// the setup function is passed as parameter to the benchmarking function.
#[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))
}

// A benchmarking function with multiple parameters requires the elements to be specified as
// tuples.
#[library_benchmark]
#[benches::multiple((1, 2), (3, 4))]
fn bench_bubble_sort_with_multiple_parameters(a: i32, b: i32) -> Vec<i32> {
    black_box(bubble_sort(black_box(vec![a, b])))
}

// A group in which we can put all our benchmark functions
library_benchmark_group!(
    name = bubble_sort_group;
    benchmarks =
        bench_bubble_sort_empty,
        bench_bubble_sort,
        bench_bubble_sort_with_benches_attribute,
        bench_bubble_sort_with_multiple_parameters
);

// Finally, the mandatory main! macro which collects all `library_benchmark_groups`.
// The main! macro creates a benchmarking harness and runs all the benchmarks defined
// in the groups and benches.
main!(library_benchmark_groups = bubble_sort_group);

Note that it is important to annotate the benchmark functions with #[library_benchmark].

§Configuration (#library-benchmarks)

It’s possible to configure some of the behavior of iai-callgrind. See the docs of crate::LibraryBenchmarkConfig for more details. Configure library benchmarks at top-level with the crate::main macro, at group level within the crate::library_benchmark_group, at crate::library_benchmark level

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.

See also the docs of crate::library_benchmark_group. The README of this crate includes more explanations, common recipes and some examples.

§Binary Benchmarks

Use this scheme of the main macro to benchmark one or more binaries of your crate (or any other executable). The documentation for setting up binary benchmarks with the binary_benchmark_group macro can be found in the docs of crate::binary_benchmark_group.

§Important default behavior

Per default, all binary benchmarks run with the environment variables cleared. See also crate::BinaryBenchmarkConfig::env_clear for how to change this behavior.

§Quickstart (#binary-benchmarks)

There are two apis to set up binary benchmarks, but we only describe the high-level api using the #[binary_benchmark] attribute here. See the docs of binary_benchmark_group for more details about the low level api. The #[binary_benchmark] attribute works almost the same as the #[library_benchmark] attribute. You will find the same parameters setup, teardown, config, etc. in #[binary_benchmark] as in #[library_benchmark] and the inner attributes #[bench], #[benches]. But, there are also substantial (differences)[#differences-to-library-benchmarks].

Suppose your crate’s binaries are named my-foo and my-bar

use iai_callgrind::{
    main, binary_benchmark, binary_benchmark_group,
};
use std::path::PathBuf;
use std::ffi::OsString;

// In binary benchmarks there's no need to return a value from the setup function
fn my_setup() {
    println!("Put code in here which will be run before the actual command");
}

#[binary_benchmark]
#[bench::just_a_fixture("benches/fixture.json")]
// First big difference to library benchmarks! `my_setup` is not evaluated right away and the
// return value of `my_setup` is not used as input for the `bench_foo` function. Instead,
// `my_setup()` is executed before the execution of the `Command`.
#[bench::with_other_fixture_and_setup(args = ("benches/other_fixture.txt"), setup = my_setup())]
#[benches::multiple("benches/fix_1.txt", "benches/fix_2.txt")]
// All functions annotated with `#[binary_benchmark]` need to return a `iai_callgrind::Command`
fn bench_foo(path: &str) -> iai_callgrind::Command {
    let path: PathBuf = path.into();
    // We can put any code in here which is needed to configure the `Command`.
    let stdout = if path.extension().unwrap() == "txt" {
        iai_callgrind::Stdio::Inherit
    } else {
        iai_callgrind::Stdio::File(path.with_extension("out"))
    };
    // Configure the command depending on the arguments passed to this function and the code
    // above
    iai_callgrind::Command::new(env!("CARGO_BIN_EXE_my-foo"))
        .stdout(stdout)
        .arg(path)
        .build()
}

#[binary_benchmark]
// The id just needs to be unique within the same `#[binary_benchmark]`, so we can reuse
// `just_a_fixture` if we want to
#[bench::just_a_fixture("benches/fixture.json")]
// The function can be generic, too.
fn bench_bar<P>(path: P) -> iai_callgrind::Command
where
   P: Into<OsString>
{
    iai_callgrind::Command::new(env!("CARGO_BIN_EXE_my-bar"))
        .arg(path)
        .build()
}

// Put all `#[binary_benchmark]` annotated functions you want to benchmark into the `benchmarks`
// section of this macro
binary_benchmark_group!(
    name = my_group;
    benchmarks = bench_foo, bench_bar
);

// As last step specify all groups you want to benchmark in the macro argument
// `binary_benchmark_groups`. As the binary_benchmark_group macro, the main macro is
// always needed and finally expands to a benchmarking harness
main!(binary_benchmark_groups = my_group);
§Differences to library benchmarks

As opposed to library benchmarks the function annotated with the binary_benchmark attribute always returns a iai_callgrind::Command. More specifically, this function is not a benchmark function, since we don’t benchmark functions anymore but Commands instead which are the return value of the #[binary_benchmark] function.

This change has far-reaching consequences but also simplifies things. Since the function itself is not benchmarked you can put any code into this function, and it does not influence the benchmark of the Command itself. However, this function is run only once to build the Command and when we collect all commands and its configuration to be able to actually execute the Commands later in the benchmark runner. Whichever code you want to run before the Command is executed has to go into the setup. And, into teardown for code you want to run after the execution of the Command.

In library benchmarks the setup argument only takes a path to a function, more specifically the function pointer. In binary benchmarks however, the setup (and teardown) parameters of the #[binary_benchmark], #[bench] and #[benches] attribute take expressions which includes function calls for example setup = my_setup(). Only in the special case that the expression is a function pointer, we pass the args of the #[bench] and #[benches] attributes into the setup, teardown and the function itself. Also, these expressions are not executed right away but in a separate process before the Command is executed. This is the main reason why the return value of the setup function is simply ignored and not routed back into the benchmark function as it would be the case in library benchmarks. We simply don’t need to. To sum it up, put code you need to configure the Command into the annotated function and code you need to execute before (after) the execution of the Command into the setup (teardown).

§Configuration (#binary-benchmarks)

Much like the configuration of library benchmarks (See above) 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 also configured at a lower and last level in Command directly.

For further details see the section about binary benchmarks of the crate::main docs the docs of crate::binary_benchmark_group and Command. Also, the README of this crate includes some introductory documentation with additional examples.

§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 details and command line arguments. The additional tools can be specified in LibraryBenchmarkConfig, BinaryBenchmarkConfig. For example to run DHAT for all library benchmarks:

use iai_callgrind::{main, LibraryBenchmarkConfig, Tool, ValgrindTool};
main!(
    config = LibraryBenchmarkConfig::default()
                .tool(Tool::new(ValgrindTool::DHAT));
    library_benchmark_groups = some_group
);

§Client requests

iai-callgrind supports valgrind client requests. See the documentation of the client_requests module.

§Flamegraphs

Flamegraphs are opt-in and can be created if you pass a FlamegraphConfig to the BinaryBenchmarkConfig::flamegraph or LibraryBenchmarkConfig::flamegraph. 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 specific event type, much like callgrind_annotate does 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.

Re-exports§

  • pub use bincode;
    default
  • pub use cty;
    client_requests_defs

Modules§

Macros§

Structs§

Enums§

  • DelayKinddefault
    The kind of Delay
  • Directiondefault
    The Direction in which the flamegraph should grow.
  • EntryPointdefault
    The EntryPoint of a library benchmark
  • EventKinddefault
    All EventKinds callgrind produces and additionally some derived events
  • ExitWithdefault
    Set the expected exit status of a binary benchmark
  • FlamegraphKinddefault
    The kind of Flamegraph which is going to be constructed
  • Pipedefault
    Configure the Stream which should be used as pipe in Stdin::Setup
  • Stdindefault
    This is a special Stdio for the stdin method of Command
  • Stdiodefault
    Configure the Stdio of Stdin, Stdout and Stderr
  • ValgrindTooldefault
    The valgrind tools which can be run in addition to callgrind

Functions§

  • black_boxdefault
    DEPRECATED: A function that is opaque to the optimizer

Attribute Macros§

  • binary_benchmarkdefault
    Used to annotate functions building the to be benchmarked iai_callgrind::Command
  • library_benchmarkdefault
    The #[library_benchmark] attribute lets you define a benchmark function which you can later use in the library_benchmark_groups! macro.