Parallelism

CPython has the infamous Global Interpreter Lock, which prevents several threads from executing Python bytecode in parallel. This makes threading in Python a bad fit for CPU-bound tasks and often forces developers to accept the overhead of multiprocessing.

In PyO3 parallelism can be easily achieved in Rust-only code. Let's take a look at our word-count example, where we have a search function that utilizes the rayon crate to count words in parallel.

#![allow(dead_code)]
use pyo3::prelude::*;

// These traits let us use `par_lines` and `map`.
use rayon::str::ParallelString;
use rayon::iter::ParallelIterator;

/// Count the occurrences of needle in line, case insensitive
fn count_line(line: &str, needle: &str) -> usize {
    let mut total = 0;
    for word in line.split(' ') {
        if word == needle {
            total += 1;
        }
    }
    total
}

#[pyfunction]
fn search(contents: &str, needle: &str) -> usize {
    contents
        .par_lines()
        .map(|line| count_line(line, needle))
        .sum()
}

But let's assume you have a long running Rust function which you would like to execute several times in parallel. For the sake of example let's take a sequential version of the word count:

#![allow(dead_code)]
fn count_line(line: &str, needle: &str) -> usize {
    let mut total = 0;
    for word in line.split(' ') {
        if word == needle {
            total += 1;
        }
    }
    total
}

fn search_sequential(contents: &str, needle: &str) -> usize {
    contents.lines().map(|line| count_line(line, needle)).sum()
}

To enable parallel execution of this function, the Python::allow_threads method can be used to temporarily release the GIL, thus allowing other Python threads to run. We then have a function exposed to the Python runtime which calls search_sequential inside a closure passed to Python::allow_threads to enable true parallelism:

#![allow(dead_code)]
use pyo3::prelude::*;

fn count_line(line: &str, needle: &str) -> usize {
    let mut total = 0;
    for word in line.split(' ') {
        if word == needle {
            total += 1;
        }
    }
    total
}

fn search_sequential(contents: &str, needle: &str) -> usize {
   contents.lines().map(|line| count_line(line, needle)).sum()
}
#[pyfunction]
fn search_sequential_allow_threads(py: Python<'_>, contents: &str, needle: &str) -> usize {
    py.allow_threads(|| search_sequential(contents, needle))
}

Now Python threads can use more than one CPU core, resolving the limitation which usually makes multi-threading in Python only good for IO-bound tasks:

from concurrent.futures import ThreadPoolExecutor
from word_count import search_sequential_allow_threads

executor = ThreadPoolExecutor(max_workers=2)

future_1 = executor.submit(
    word_count.search_sequential_allow_threads, contents, needle
)
future_2 = executor.submit(
    word_count.search_sequential_allow_threads, contents, needle
)
result_1 = future_1.result()
result_2 = future_2.result()

Benchmark

Let's benchmark the word-count example to verify that we really did unlock parallelism with PyO3.

We are using pytest-benchmark to benchmark four word count functions:

1. Pure Python version
2. Rust parallel version
3. Rust sequential version
4. Rust sequential version executed twice with two Python threads

The benchmark script can be found here, and we can run nox in the word-count folder to benchmark these functions.

While the results of the benchmark of course depend on your machine, the relative results should be similar to this (mid 2020):

-------------------------------------------------------------------------------------------------- benchmark: 4 tests -------------------------------------------------------------------------------------------------
Name (time in ms)                                          Min                Max               Mean            StdDev             Median               IQR            Outliers       OPS            Rounds  Iterations
-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
test_word_count_rust_parallel                           1.7315 (1.0)       4.6495 (1.0)       1.9972 (1.0)      0.4299 (1.0)       1.8142 (1.0)      0.2049 (1.0)         40;46  500.6943 (1.0)         375           1
test_word_count_rust_sequential                         7.3348 (4.24)     10.3556 (2.23)      8.0035 (4.01)     0.7785 (1.81)      7.5597 (4.17)     0.8641 (4.22)         26;5  124.9457 (0.25)        121           1
test_word_count_rust_sequential_twice_with_threads      7.9839 (4.61)     10.3065 (2.22)      8.4511 (4.23)     0.4709 (1.10)      8.2457 (4.55)     0.3927 (1.92)        17;17  118.3274 (0.24)        114           1
test_word_count_python_sequential                      27.3985 (15.82)    45.4527 (9.78)     28.9604 (14.50)    4.1449 (9.64)     27.5781 (15.20)    0.4638 (2.26)          3;5   34.5299 (0.07)         35           1
-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------

You can see that the Python threaded version is not much slower than the Rust sequential version, which means compared to an execution on a single CPU core the speed has doubled.