Paralight: a lightweight parallelism library for indexed structures

This library allows you to distribute computation over slices (and other indexed sources) among multiple threads. Each thread processes a subset of the items, and a final step reduces the outputs from all threads into a single result.

use paralight::iter::{
    IntoParallelRefSource, IntoParallelRefMutSource, ParallelIteratorExt, ParallelSourceExt,
    ZipableSource,
};
use paralight::{CpuPinningPolicy, RangeStrategy, ThreadCount, ThreadPoolBuilder};

// Create a thread pool with the given parameters.
let mut thread_pool = ThreadPoolBuilder {
    num_threads: ThreadCount::AvailableParallelism,
    range_strategy: RangeStrategy::WorkStealing,
    cpu_pinning: CpuPinningPolicy::No,
}
.build();

// Compute the sum of a slice.
let input = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10];
let sum = input
    .par_iter()
    .with_thread_pool(&mut thread_pool)
    .copied()
    .reduce(|| 0, |x, y| x + y);
assert_eq!(sum, 5 * 11);

// Add slices together.
let mut output = [0; 10];
let left = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10];
let right = [11, 12, 13, 14, 15, 16, 17, 18, 19, 20];

(output.par_iter_mut(), left.par_iter(), right.par_iter())
    .zip_eq()
    .with_thread_pool(&mut thread_pool)
    .for_each(|(out, &a, &b)| *out = a + b);

assert_eq!(output, [12, 14, 16, 18, 20, 22, 24, 26, 28, 30]);

Paralight currently supports inputs that are a combination of slices and ranges, but can be extended to support other sources as long as they are indexed. This is done via the ParallelSource and IntoParallelSource traits.

Thread pool configuration

The ThreadPoolBuilder provides an explicit way to configure your thread pool, giving you fine-grained control over performance for your workload. There is no default, which is deliberate because the suitable parameters depend on your workload.

Number of worker threads

Paralight allows you to specify the number of worker threads to spawn in a thread pool with the ThreadCount enum:

• AvailableParallelism uses the number of threads returned by the standard library's available_parallelism() function,
• Count(_) uses the specified number of threads, which must be non-zero.

For convenience, ThreadCount implements the TryFrom<usize> trait to create a Count(_) instance, validating that the given number of threads is not zero.

Work-stealing strategy

Paralight offers two strategies in the RangeStrategy enum to distribute computation among threads:

• Fixed splits the input evenly and hands out a fixed sequential range of items to each worker thread,
• WorkStealing starts with the fixed distribution, but lets each worker thread steal items from others once it is done processing its items.

Note: In work-stealing mode, each thread processes an arbitrary subset of items in arbitrary order, meaning that the reduction operation must be both commutative and associative to yield a deterministic result (in contrast to the standard library's Iterator trait that processes items in order). Fortunately, a lot of common operations are commutative and associative, but be mindful of this.

Recommendation: If your pipeline is performing roughly the same amont of work for each item, you should probably use the Fixed strategy, to avoid paying the synchronization cost of work-stealing. This is especially true if the amount of work per item is small (e.g. some simple arithmetic operations). If the amoung of work per item is highly variable and/or large, you should probably use the WorkStealing strategy (e.g. parsing strings, processing files).

CPU pinning

Paralight allows pinning each worker thread to one CPU, on platforms that support it. For now, this is implemented for platforms whose target_os is among android, dragonfly, freebsd and linux (platforms that support libc::sched_setaffinity() via the nix crate).

Paralight offers three policies in the CpuPinningPolicy enum:

• No doesn't pin worker threads to CPUs,
• IfSupported attempts to pin each worker thread to a distinct CPU on supported platforms, but proceeds without pinning if running on an unsupported platform or if the pinning function fails,
• Always pins each worker thread to a distinct CPU, panicking if the platform isn't supported or if the pinning function returns an error.

Whether CPU pinning is useful or detrimental depends on your workload. If you're processing the same data over and over again (e.g. calling par_iter() multiple times on the same data), CPU pinning can help ensure that each subset of the data is always processed on the same CPU core and stays fresh in the lower-level per-core caches, speeding up memory accesses. This however depends on the amount of data: if it's too large, it may not fit in per-core caches anyway.

If your program is not running alone on your machine but is competing with other programs, CPU pinning may be detrimental, as a worker thread will be blocked whenever its required core is used by another program, even if another core is free and other worker threads are done (especially with the Fixed strategy). This of course depends on how the scheduler works on your OS.

Using a thread pool

To create parallel pipelines, be mindful that the with_thread_pool() function takes the thread pool by mutable reference &mut. This is a deliberate design choice because only one pipeline can be run at a time on a given thread pool.

To release the resources (i.e. the worker threads) created by a ThreadPool, simply drop() it.

If you want to create a global thread pool, you will have to wrap it in a Mutex (or other suitable synchronization primitive) and manually lock it to obtain a suitable &mut ThreadPool. You can for example combine a mutex with the LazyLock pattern.

use paralight::iter::{IntoParallelRefSource, ParallelIteratorExt, ParallelSourceExt};
use paralight::{
    CpuPinningPolicy, RangeStrategy, ThreadPool, ThreadCount, ThreadPoolBuilder,
};
use std::ops::DerefMut;
use std::sync::{LazyLock, Mutex};

// A static thread pool protected by a mutex.
static THREAD_POOL: LazyLock<Mutex<ThreadPool>> = LazyLock::new(|| {
    Mutex::new(
        ThreadPoolBuilder {
            num_threads: ThreadCount::AvailableParallelism,
            range_strategy: RangeStrategy::WorkStealing,
            cpu_pinning: CpuPinningPolicy::No,
        }
        .build(),
    )
});

let items = (0..100).collect::<Vec<_>>();
let sum = items
    .par_iter()
    .with_thread_pool(THREAD_POOL.lock().unwrap().deref_mut())
    .copied()
    .reduce(|| 0, |a, b| a + b);
assert_eq!(sum, 99 * 50);

However, if you wrap a thread pool in a mutex like this, be mindful of potential panics or deadlocks if you try to run several nested parallel iterators on the same thread pool!

This limitation isn't specific to Paralight though, this happens for any usage of a Mutex that you try to lock recursively while already acquired.

This pitfall is the reason why Paralight doesn't provide an implicit global thread pool.

# use paralight::iter::{IntoParallelRefSource, ParallelIteratorExt, ParallelSourceExt};
# use paralight::{
#     CpuPinningPolicy, RangeStrategy, ThreadPool, ThreadCount, ThreadPoolBuilder,
# };
# use std::ops::DerefMut;
# use std::sync::{LazyLock, Mutex};
#
# static THREAD_POOL: LazyLock<Mutex<ThreadPool>> = LazyLock::new(|| {
#     Mutex::new(
#         ThreadPoolBuilder {
#             num_threads: ThreadCount::AvailableParallelism,
#             range_strategy: RangeStrategy::WorkStealing,
#             cpu_pinning: CpuPinningPolicy::No,
#         }
#         .build(),
#     )
# });
let matrix = (0..100)
    .map(|i| (0..100).map(|j| i + j).collect::<Vec<_>>())
    .collect::<Vec<_>>();

let sum = matrix
    .par_iter()
    // Lock the mutex on the outer loop (over the rows).
    .with_thread_pool(THREAD_POOL.lock().unwrap().deref_mut())
    .map(|row| {
        row.par_iter()
            // ⚠️ Trying to lock the mutex again here will panic or deadlock!
            .with_thread_pool(THREAD_POOL.lock().unwrap().deref_mut())
            .copied()
            .reduce(|| 0, |a, b| a + b)
    })
    .reduce(|| 0, |a, b| a + b);

// ⚠️ This statement is never reached due to the panic/deadlock!
assert_eq!(sum, 990_000);

Limitations

With the WorkStealing strategy, inputs with more than u32::MAX elements are currently not supported.

use paralight::iter::{IntoParallelSource, ParallelIteratorExt, ParallelSourceExt};
use paralight::{CpuPinningPolicy, RangeStrategy, ThreadCount, ThreadPoolBuilder};

let mut thread_pool = ThreadPoolBuilder {
    num_threads: ThreadCount::AvailableParallelism,
    range_strategy: RangeStrategy::WorkStealing,
    cpu_pinning: CpuPinningPolicy::No,
}
.build();

let _sum = (0..5_000_000_000_usize)
    .into_par_iter()
    .with_thread_pool(&mut thread_pool)
    .reduce(|| 0, |x, y| x + y);

Debugging

Two optional features are available if you want to debug performance.

• log, based on the log crate prints basic information about inter-thread synchronization: thread creation/shutdown, when each thread starts/finishes a computation, etc.
• log_parallelism prints detailed traces about which items are processed by which thread, and work-stealing statistics (e.g. how many times work was stolen among threads).

Note that in any case neither the input items nor the resulting computation are logged. Only the indices of the items in the input may be present in the logs. If you're concerned that these indices leak too much information about your data, you need to make sure that you depend on Paralight with the log and log_parallelism features disabled.

Experimental nightly APIs

Some experimental APIs are available under the nightly Cargo feature, for users who compile with a nightly Rust toolchain. As the underlying implementation is based on experimental features of the Rust language, these APIs are provided without guarantee and may break at any time when a new nightly toolchain is released.

Disclaimer

This is not an officially supported Google product.

Contributing

See CONTRIBUTING.md for details.

License

This software is distributed under the terms of both the MIT license and the Apache License (Version 2.0).

See LICENSE for details.