#concurrency #thread #threadpool #async #futures

rusty_pool

Self growing / shrinking ThreadPool implementation based on crossbeam’s multi-producer multi-consumer channels that enables awaiting the result of a task and offers async support

12 unstable releases (5 breaking)

0.6.0 May 9, 2021
0.5.1 Feb 27, 2021
0.5.0 Jan 30, 2021
0.4.3 Aug 11, 2020
0.1.0 Apr 29, 2020

#27 in Asynchronous

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rusty_pool

Self growing / shrinking ThreadPool implementation based on crossbeam's multi-producer multi-consumer channels that enables awaiting the result of a task and offers async support.

This ThreadPool has two different pool sizes; a core pool size filled with threads that live for as long as the channel and a max pool size which describes the maximum amount of worker threads that may live at the same time. Those additional non-core threads have a specific keep_alive time described when creating the ThreadPool that defines how long such threads may be idle for without receiving any work before giving up and terminating their work loop.

This ThreadPool does not spawn any threads until a task is submitted to it. Then it will create a new thread for each task until the core pool size is full. After that a new thread will only be created upon an execute() call if the current pool is lower than the max pool size and there are no idle threads.

Functions like evaluate() and complete() return a JoinHandle that may be used to await the result of a submitted task or future. JoinHandles may be sent to the thread pool to create a task that blocks a worker thread until it receives the result of the other task and then operates on the result. If the task panics the JoinHandle receives a cancellation error. This is implemented using a futures oneshot channel to communicate with the worker thread.

This ThreadPool may be used as a futures executor if the "async" feature is enabled, which is the case by default. The "async" feature includes the spawn() and try_spawn() functions which create a task that polls the future one by one and creates a waker that re-submits the future to the pool when it can make progress. Without the "async" feature, futures can simply be executed to completion using the complete function, which simply blocks a worker thread until the future has been polled to completion.

The "async" feature can be disabled if not need by adding the following to your Cargo dependency:

[dependencies.rusty_pool]
default-features = false
version = "*"

When creating a new worker this ThreadPool tries to increment the worker count using a compare-and-swap mechanism, if the increment fails because the total worker count has been incremented to the specified limit (the core_size when trying to create a core thread, else the max_size) by another thread, the pool tries to create a non-core worker instead (if previously trying to create a core worker and no idle worker exists) or sends the task to the channel instead. Panicking workers are always cloned and replaced.

Locks are only used for the join functions to lock the Condvar, apart from that this ThreadPool implementation fully relies on crossbeam and atomic operations. This ThreadPool decides whether it is currently idle (and should fast-return join attempts) by comparing the total worker count to the idle worker count, which are two values stored in one AtomicUsize (both half the size of usize) making sure that if both are updated they may be updated in a single atomic operation.

The thread pool and its crossbeam channel can be destroyed by using the shutdown function, however that does not stop tasks that are already running but will terminate the thread the next time it will try to fetch work from the channel. The channel is only destroyed once all clones of the ThreadPool have been shut down / dropped.

Installation

To add rusty_pool to your project simply add the following Cargo dependency:

[dependencies]
rusty_pool = "0.5.1"

Or to exclude the "async" feature:

[dependencies.rusty_pool]
version = "0.5.1"
default-features = false

Usage

Create a new ThreadPool:

use rusty_pool::Builder;
use rusty_pool::ThreadPool;
// Create default `ThreadPool` configuration with the number of CPUs as core pool size
let pool = ThreadPool::default();
// Create a `ThreadPool` with default naming:
use std::time::Duration;
let pool2 = ThreadPool::new(5, 50, Duration::from_secs(60));
// Create a `ThreadPool` with a custom name:
let pool3 = ThreadPool::new_named(String::from("my_pool"), 5, 50, Duration::from_secs(60));
// using the Builder struct:
let pool4 = Builder::new().core_size(5).max_size(50).build();

Submit a closure for execution in the ThreadPool:

use rusty_pool::ThreadPool;
use std::thread;
use std::time::Duration;
let pool = ThreadPool::default();
pool.execute(|| {
    thread::sleep(Duration::from_secs(5));
    print!("hello");
});

Submit a task and await the result:

use rusty_pool::ThreadPool;
use std::thread;
use std::time::Duration;
let pool = ThreadPool::default();
let handle = pool.evaluate(|| {
    thread::sleep(Duration::from_secs(5));
    return 4;
});
let result = handle.await_complete();
assert_eq!(result, 4);

Spawn futures using the ThreadPool:

async fn some_async_fn(x: i32, y: i32) -> i32 {
    x + y
}

async fn other_async_fn(x: i32, y: i32) -> i32 {
    x - y
}

use rusty_pool::ThreadPool;
let pool = ThreadPool::default();

// simply complete future by blocking a worker until the future has been completed
let handle = pool.complete(async {
    let a = some_async_fn(4, 6).await; // 10
    let b = some_async_fn(a, 3).await; // 13
    let c = other_async_fn(b, a).await; // 3
    some_async_fn(c, 5).await // 8
});
assert_eq!(handle.await_complete(), 8);

use std::sync::{Arc, atomic::{AtomicI32, Ordering}};

// spawn future and create waker that automatically re-submits itself to the threadpool if ready to make progress, this requires the "async" feature which is enabled by default
let count = Arc::new(AtomicI32::new(0));
let clone = count.clone();
pool.spawn(async move {
    let a = some_async_fn(3, 6).await; // 9
    let b = other_async_fn(a, 4).await; // 5
    let c = some_async_fn(b, 7).await; // 12
    clone.fetch_add(c, Ordering::SeqCst);
});
pool.join();
assert_eq!(count.load(Ordering::SeqCst), 12);

Join and shut down the ThreadPool:

use std::thread;
use std::time::Duration;
use rusty_pool::ThreadPool;
use std::sync::{Arc, atomic::{AtomicI32, Ordering}};

let pool = ThreadPool::default();
for _ in 0..10 {
    pool.execute(|| { thread::sleep(Duration::from_secs(10)) })
}
// wait for all threads to become idle, i.e. all tasks to be completed including tasks added by other threads after join() is called by this thread or for the timeout to be reached
pool.join_timeout(Duration::from_secs(5));

let count = Arc::new(AtomicI32::new(0));
for _ in 0..15 {
    let clone = count.clone();
    pool.execute(move || {
        thread::sleep(Duration::from_secs(5));
        clone.fetch_add(1, Ordering::SeqCst);
    });
}

// shut down and drop the only instance of this `ThreadPool` (no clones) causing the channel to be broken leading all workers to exit after completing their current work
// and wait for all workers to become idle, i.e. finish their work.
pool.shutdown_join();
assert_eq!(count.load(Ordering::SeqCst), 15);

Performance

In terms of performance from the perspective of a thread submitting tasks to the pool, rusty_pool should offer better performance than any pool using std::sync::mpsc (such as rust-threadpool) in most scenarios thanks to the great work of the crossbeam team. In some cases with extreme contention rusty_pool might fall behind rust-threadpool, though the scenarios where this has been found to be the case are hardly practical as they require to submit empty tasks in a loop and it depends on the platform. macOS seems to perform particularly well in the tested scenario, presumably macOS has spent a lot of effort optimising atomic operations as Swift's reference counting depends on it. Apparently this should be amplified on Apple Silicon but rusty_pool has not been tested on that platform. The following tests were executed on a PC with an AMD Ryzen 9 3950X for Linux and Windows and on a MacBook Pro 15" 2019 with an Intel i9-9880H for macOS.

Test 1: No contention

All tasks are submitted by the same thread and the task lasts longer than the test, meaning all atomic operations (reading and incrementing the worker counter) are performed by the main thread, since newly created workers do not alter the counter until after they completed their initial task and increment the idle counter.

fn main() {
    let now = std::time::Instant::now();

    let pool = rusty_pool::Builder::new().core_size(10).max_size(10).build();
    //let pool = threadpool::ThreadPool::new(10);

    for _ in 0..10000000 {
        pool.execute(|| {
            thread::sleep(std::time::Duration::from_secs(1));
        });
    }

    let millis = now.elapsed().as_millis();
    println!("millis: {}", millis);
}

Results (in milliseconds, average value):

rusty_pool 0.5.1:

Windows MacOS Linux
221.6 293.07 183.73

rusty_pool 0.5.0:

Windows MacOS Linux
224.6 315.6 187.0

rust-threadpool 1.8.1:

Windows MacOS Linux
476.4 743.4 354.3

rusty_pool 0.4.3:

Windows MacOS Linux
237.5 318.1 181.3

Test 2: Multiple producers

Next to the main thread there are 10 other threads submitting tasks to the pool. Unlike the previous test, the task no longer lasts longer than the test, thus there not only is contention between the producers for the worker counter but also between the worker threads updating the idle counter. This is a somewhat realistic albeit extreme example.

fn main() {
    let now = std::time::Instant::now();

    let pool = rusty_pool::Builder::new().core_size(10).max_size(10).build();
    //let pool = threadpool::ThreadPool::new(10);

    for _ in 0..10 {
        let pool = pool.clone();

        std::thread::spawn(move || {
            for _ in 0..10000000 {
                pool.execute(|| {
                    std::thread::sleep(std::time::Duration::from_secs(1));
                });
            }
        });
    }

    for _ in 0..10000000 {
        pool.execute(|| {
            std::thread::sleep(std::time::Duration::from_secs(1));
        });
    }

    let millis = now.elapsed().as_millis();
    println!("millis: {}", millis);
}

Results (in milliseconds, average value):

rusty_pool 0.5.1:

Windows* MacOS Linux
7692.4 3656.2 7514.53

rusty_pool 0.5.0:

Windows MacOS Linux Windows*
6251.0 4417.7 7903.1 7774.67

rust-threadpool 1.8.1:

Windows MacOS Linux
10030.5 5810.5 9743.3

rusty_pool 0.4.3:

Windows MacOS Linux Windows*
6342.2 4444.6 7962.0 8564.93

* When testing 0.5.1 the performance for Windows appeared to be considerably worse, so the results for previous versions of rusty_pool were recalculated and also found to be worse than when originally recorded, probably due to external influence (e.g. background task taking a lot of CPU time, though the test was retried with realtime priority with similar results). The results for rust-threadpool 1.8.1 were not fully recalculated as they appeared to be similar to the last recording.

Test 3: Worst case

This test case highlights the aforementioned worst-case scenario for rusty_pool where the pool is spammed with empty tasks. Since workers increment the idle counter after completing a task and the task is executed practically immediately, the increment of the idle counter coincides with the next execute() call in the loop reading the counter. The higher the number of workers the higher contention gets and the worse performance becomes.

fn main() {
    let now = std::time::Instant::now();

    let pool = rusty_pool::Builder::new().core_size(10).max_size(10).build();
    //let pool = threadpool::ThreadPool::new(10);

    for _ in 0..10000000 {
        pool.execute(|| {});
    }

    let millis = now.elapsed().as_millis();
    println!("millis: {}", millis);
}

rusty_pool 0.5.1:

Windows MacOS Linux
1967.93 698.8 2150.0

rusty_pool 0.5.0:

Windows MacOS Linux
1991.6 679.93 2175.1

rust-threadpool 1.8.1:

Windows MacOS Linux
980.33 1224.6 677.0

rusty_pool 0.4.3:

Windows MacOS Linux
2016.8 683.13 2175.1

Curiously, macOS heavily favours rusty_pool in this case while Windows and Linux favour rust-threadpool. However, this test case should hardly occur in a real world scenario. In all other tested scenarios rusty_pool performs better when submitting tasks, where macOS seems to gain a lead in cases where there is a lot of contention but falling behind in other cases, possibly due to the weaker hardware of the specific device used for testing. Linux seems to perform best in cases with little to no contention but performs the worst when contention is high.

Dependencies

~1MB
~21K SLoC