Crate paralight 

Paralight: a lightweight parallelism library for indexed structures

This library allows you to distribute computation over indexed sources (slices, ranges, Vec, etc.) among multiple threads. It aims to uphold the highest standards of documentation, testing and safety, see the FAQ below.

It is designed to be as lightweight as possible, following the principles outlined in the blog post Optimization adventures: making a parallel Rust workload 10x faster with (or without) Rayon. Benchmarks on a real-world use case can be seen here.

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)
    .sum::<i32>();
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 supports various indexed sources out-of-the-box (slices, ranges, etc.), and can be extended to other types via the ParallelSource trait, together with the conversion traits (IntoParallelSource, IntoParallelRefSource and IntoParallelRefMutSource).

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.

Recommendation: It depends. While AvailableParallelism may be a good default, it usually returns twice the number of CPU cores (at least on Intel) to account for hyper-threading. Whether this is optimal or not depends on your workload, for example whether it’s compute bound or memory bound, whether a single thread can saturate the resources of one core or not, etc. Generally, the long list of caveats mentioned in the documentation of available_parallelism() applies.

On some workloads, hyper-threading doesn’t provide a performance boost over using only one thread per core, because two hyper-threads would compete on resources on the core they share (e.g. memory caches). In this case, using half of what available_parallelism() returns can reduce contention and perform better.

If your program is not running alone on your machine but is competing with other programs, using too many threads can also be detrimental to the overall performance of your system.

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.

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).

Note: In work-stealing mode, each thread processes an arbitrary subset of items in arbitrary order, meaning that a 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 sequential order). Fortunately, a lot of common operations are commutative and associative, but be mindful of this.

let s = ['a', 'b', 'c', 'd', 'e', 'f', 'g', 'h', 'i', 'j']
    .par_iter()
    .with_thread_pool(&mut thread_pool)
    .map(|c: &char| c.to_string())
    .reduce(String::new, |mut a: String, b: String| {
        a.push_str(&b);
        a
    });
// ⚠️ There is no guarantee that this check passes. In practice, `s` contains any permutation
// of the input, such as "fgdebachij".
assert_eq!(s, "abcdefghij");

// This makes sure the example panics anyway if the permutation is (by luck) the identity.
panic!("Congratulations, you won the lottery and the assertion passed this time!");

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.

Recommendation: 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 a ThreadPool by mutable reference &mut. This is a deliberate design choice because only one pipeline can be run at a time on a given Paralight thread pool (for more flexible options, see “Bringing your own thread pool” below).

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())
    .sum::<i32>();
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.

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())
            .sum::<i32>()
    })
    .sum::<i32>();

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

Bringing your own thread pool

As an alternative to the provided ThreadPool implementation, you can use Paralight with any thread pool that implements the GenericThreadPool interface, via the with_thread_pool() adaptor.

Note that the GenericThreadPool trait is marked as unsafe due to the requirements that your implementation must uphold.

If you don’t need the default ThreadPool implementation, you can disable the default-thread-pool feature of Paralight and still benefit from all the iterator API.

Rayon thread pools

For convenience, the RayonThreadPool wrapper around Rayon is available under the rayon feature, and implements GenericThreadPool.

However, note that this backend isn’t tested with Miri nor ThreadSanitizer, because Rayon is broken with them.

This wrapper allows you to control how many tasks are run on Rayon’s thread pool. It is recommended to match the number of threads in the pool (as well as the available parallelism), to have one Paralight task per thread (and per available CPU core). In that case, performance should be similar to vanilla Paralight. This is of course only a guideline: as usual benchmark and profile your code to know which configuration is optimal.

use paralight::iter::{IntoParallelRefSource, ParallelIteratorExt, ParallelSourceExt};
use paralight::{RangeStrategy, RayonThreadPool, ThreadCount};

let thread_pool = RayonThreadPool::new_global(
    ThreadCount::try_from(rayon_core::current_num_threads())
        .expect("Paralight cannot operate with 0 threads"),
    RangeStrategy::WorkStealing,
);

let input = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10];
let sum = input.par_iter().with_thread_pool(&thread_pool).sum::<i32>();
assert_eq!(sum, 5 * 11);

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)
    .sum::<usize>();

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.

FAQ

Documentation

All public APIs of Paralight are documented, which is enforced by the forbid(missing_docs) lint. The aim is to have at least one example per API, naturally tested via rustdoc.

Testing

Paralight is thoroughly tested, with code coverage as close to 100% as possible.

The testing strategy combines rustdoc examples, top-level stress tests and unit tests on the most critical components.

Safety

Paralight aims to use as little unsafe code as possible. As a first measure, the following lints are enabled throughout the code base, to make sure each use of an unsafe API is explained and each new unsafe function documents its pre- and post-conditions.

#![forbid(
    missing_docs,
    unsafe_op_in_unsafe_fn,
    clippy::missing_safety_doc,
    clippy::multiple_unsafe_ops_per_block,
    clippy::undocumented_unsafe_blocks,
)]

Additionally, extensive testing is conducted with ThreadSanitizer and Miri. Vanilla Paralight is 100% compatible with them, including Miri’s detection of memory leaks.

This multi-layered approach to safety is crucial given how complex mixing memory safety with multi-threading is, and it has indeed caught a bug during development.

Note: The Rayon thread pool integration is unfortunately not tested under Miri, as Rayon in general triggers Miri errors. Likewise, Rayon local thread pools aren’t compatible with ThreadSanitizer.

Use of unsafe code

As mentioned, Paralight uses as little unsafe code as possible. Here is the list of places where unsafe is needed.

• Lifetime-erasure in core/util.rs. The goal is essentially to share an Arc<Mutex<&'a T>> between the main threads and worker threads where T contains a description of a parallel pipeline. The difficulty is that the lifetime 'a is only valid for a limited scope (for example, a parallel iterator may capture local variables by reference). Even though synchronization is in place to make sure the worker threads only access this pipeline during 'a, there is no way to write a safe Rust type for Arc<Mutex<&'a T>> where the lifetime 'a changes over time (the same mutex is reused for successive pipelines sent to Paralight). Glossing over the details, a type akin to Arc<Mutex<&'static T>> is used instead (i.e. the lifetime is marked 'static), with unsafe code to rescope the 'static lifetime to a local 'a as needed. Send and Sync implementations are also provided (when sound) on this wrapper type.
• The SliceParallelSource API in iter/source/slice.rs uses slice::get_unchecked() as it guides the compiler to better optimize the code. In particular, missed vectorized loops were observed without it.
• The MutSliceParallelSource API in iter/source/slice.rs. The goal is to provide parallel iterators that produce mutable references &mut T to items of a mutable slice &mut [T]. Sharing a &mut [T] with all the threads wouldn’t work as it would violate Rust’s aliasing rules. Using the slice::split_at_mut() API would not work either, as it isn’t known in advance where a split will occur nor which worker thread will consume which item (due to work stealing). An approach that decomposes the slice into a pointer-length pair is used instead, making it possible to share the raw pointer with all the worker threads.
• Similarly, the VecParallelSource API in iter/source/vec.rs provides parallel iterators that consume a Vec<T>. This is achieved by decomposing the vector into a pointer-length-capacity triple, and sharing the base pointer with all the worker threads so that they can consume items of type T (via std::ptr::read()). Additionally, the original Vec<T> allocation is released when the iterator is dropped (to avoid memory leaks), which involves reconstructing it from the pointer-allocation pair (via Vec::from_raw_parts()).
• Likewise, the ArrayParallelSource API in iter/source/array.rs provides parallel iterators that consume a [T; N]. The situation is similar to Vec<T>, except that the items aren’t allocated on the heap behind a pointer, but directly in the array. This involves a more careful combination of wrapper types. Note that a prior implementation was quickly reverted due to being unsound, highlighting once again the importance of code coverage and the effectiveness of Miri.
• Lastly, the definition of the SourceDescriptor trait in iter/source/mod.rs has unsafe methods because it requires the caller to pass each index once and only once. Indeed, the safety of the previously mentioned iterator sources (mutable slice, vector, array) assumes a correct calling pattern. The SourceDescriptor trait is public (so that dependents of Paralight can define their own sources of items), so these unsafe functions leak in the public API. Internally, this causes unsafe blocks each time the trait is implemented, and the associated safety comments are a good opportunity to check correctness.
• Symmetrically, GenericThreadPool is an unsafe trait, implemented for &mut ThreadPool in core/thread_pool.rs. This in turn relies on correctness of the (safe) work-stealing implementation in core/range.rs, that is still missing a formal proof.

And that’s all the unsafe code there is!

Disclaimer

This is not an officially supported Google product.

Contributing

See CONTRIBUTING.md for details.

Note that Paralight is still an early stage project, with many design changes as new features are added. Therefore it is highly recommended to file a GitHub issue for discussion before submitting a pull request, unless you’re making a trivial change or bug fix.

License

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

See LICENSE for details.

Modules

iter

Iterator adaptors to define parallel pipelines more conveniently.

Structs

RayonThreadPoolrayon

Adaptor to execute Paralight iterators over a thread pool provided by the Rayon crate.

ThreadPooldefault-thread-pool

A thread pool that can execute parallel pipelines.

ThreadPoolBuilderdefault-thread-pool

A builder for ThreadPool.

Enums

CpuPinningPolicydefault-thread-pool

Policy to pin worker threads to CPUs.

RangeStrategyrayon or default-thread-pool

Strategy to distribute ranges of work items among threads.

ThreadCountrayon or default-thread-pool

Number of threads to spawn in a thread pool.