Crate iai_callgrind

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
• Benchmarking
  • Library Benchmarks
    • Important Default Behavior
    • Quickstart
    • Configuration
  • Binary Benchmarks
    • Temporary workspace and other important default behavior
    • Quickstart
    • Configuration
• Valgrind Tools
• Client Requests
• Flamegraphs

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 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. If you really like to, it’s possible to benchmark any executable file in the PATH or any executable specified with an absolute path. The documentation for setting up binary benchmarks with the binary_benchmark_group macro can be found in the docs of crate::binary_benchmark_group.

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 crate::BinaryBenchmarkConfig::sandbox for a deeper explanation and how to control and change this behavior. Also, the environment variables of benchmarked binaries are cleared before the benchmark is run. See also crate::BinaryBenchmarkConfig::env_clear for how to change this behavior.

Quickstart (#binary-benchmarks)

Suppose your crate’s binary is named my-exe and you have a fixtures directory in benches/fixtures with a file test1.txt in it:

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

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

// We specify a cmd `"my-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("benches/fixtures"));
    benchmark = |"my-exe", 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
        .bench(Run::with_arg(Arg::new(
            "positional arguments",
            ["foo", "foo bar"],
        )))

        // 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);

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 configured at a lower and last level within Run directly.

For further details see the section about binary benchmarks of the crate::main docs the docs of crate::binary_benchmark_group and Run. 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 or Run. 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, Run::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

• client_requestsclient_requests_defs
  The iai-callgrind rustified interface to Valgrind’s Client Request Mechanism

Macros

• binary_benchmark_groupdefault
  Macro used to define a group of binary benchmarks
• cstringclient_requests_defs
  Convenience macro to create a \0-byte terminated std::ffi::CString from a literal string
• format_cstringclient_requests_defs
  Convenience macro to create a \0-byte terminated std::ffi::CString from a format string
• library_benchmark_groupdefault
  Macro used to define a group of library benchmarks
• maindefault
  The iai_callgrind::main macro expands to a main function which runs all of the benchmarks.
• valgrind_printfclient_requests_defs
  Allow prints to valgrind log
• valgrind_printf_backtraceclient_requests_defs
  Allow prints to valgrind log with a backtrace
• valgrind_printf_backtrace_uncheckedclient_requests_defs
  Allow prints to valgrind log with a backtrace
• valgrind_printf_uncheckedclient_requests_defs
  Allow prints to valgrind log
• valgrind_printlnclient_requests_defs
  Allow prints to valgrind log ending with a newline
• valgrind_println_backtraceclient_requests_defs
  Allow prints to valgrind log with a backtrace ending the formatted string with a newline
• valgrind_println_backtrace_uncheckedclient_requests_defs
  Allow prints to valgrind log with a backtrace ending the formatted string with a newline
• valgrind_println_uncheckedclient_requests_defs
  Allow prints to valgrind log ending with a newline

Structs

• Argdefault
  The arguments needed for Run which are passed to the benchmarked binary
• BenchmarkIddefault
  An id for an Arg which can be used to produce unique ids from parameters
• BinaryBenchmarkConfigdefault
  The main configuration of a binary benchmark.
• BinaryBenchmarkGroupdefault
  The BinaryBenchmarkGroup lets you configure binary benchmark Runs
• Fixturesdefault
  A builder of Fixtures to specify the fixtures directory which will be copied into the sandbox
• FlamegraphConfigdefault
  The FlamegraphConfig which allows the customization of the created flamegraphs
• LibraryBenchmarkConfigdefault
  The main configuration of a library benchmark.
• RegressionConfigdefault
  Configure performance regression checks and behavior
• Rundefault
  Run let’s you set up and configure a benchmark run of a binary
• Tooldefault
  Configure to run other valgrind tools like DHAT or Massif in addition to callgrind

Enums

• Directiondefault
  The Direction in which the flamegraph should grow.
• 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
• ValgrindTooldefault
  An enum with all possible valgrind tools

Functions

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

Attribute Macros

• library_benchmarkdefault
  The #[library_benchmark] attribute let’s you define a benchmark function which you can later use in the library_benchmark_groups! macro.