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@ -4,4 +4,3 @@ _site/
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.jekyll-metadata
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.bundle/
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vendor/
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.vscode/
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12
Gemfile.lock
12
Gemfile.lock
@ -9,12 +9,12 @@ GEM
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eventmachine (>= 0.12.9)
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http_parser.rb (~> 0.6.0)
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eventmachine (1.2.7)
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ffi (1.13.1)
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ffi (1.12.2)
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forwardable-extended (2.6.0)
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http_parser.rb (0.6.0)
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i18n (0.9.5)
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concurrent-ruby (~> 1.0)
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jekyll (3.8.7)
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jekyll (3.8.6)
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addressable (~> 2.4)
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colorator (~> 1.0)
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em-websocket (~> 0.5)
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@ -48,11 +48,11 @@ GEM
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mercenary (0.3.6)
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pathutil (0.16.2)
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forwardable-extended (~> 2.6)
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public_suffix (4.0.5)
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rb-fsevent (0.10.4)
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public_suffix (4.0.4)
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rb-fsevent (0.10.3)
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rb-inotify (0.10.1)
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ffi (~> 1.0)
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rouge (3.20.0)
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rouge (3.17.0)
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rubyzip (2.3.0)
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safe_yaml (1.0.5)
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sass (3.7.4)
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@ -75,4 +75,4 @@ DEPENDENCIES
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tzinfo-data
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BUNDLED WITH
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2.1.4
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1.17.3
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@ -1,276 +0,0 @@
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---
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layout: post
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title: "Release the GIL: Pybind11, PyO3"
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description: "More Python Parallelism"
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category:
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tags: [python, rust, c++]
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---
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I've been continuing experiments with parallelism in Python; while these techniques are a bit niche,
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it's still fun to push the performance envelope. In addition to tools like
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[Cython](https://cython.org/) and [Numba](https://numba.pydata.org/) (covered
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[here](//2019/12/release-the-gil.html)) that attempt to stay as close to Python as possible, other
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projects are available that act as a bridge between Python and other languages. The goal is to make
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cooperation simple without compromising independence.
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In practice, this "cooperation" between languages is important for performance reasons. Code written
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in C++ shouldn't have to care about the Python GIL. However, unless the GIL is explicitly unlocked,
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it will remain implicitly held; though the Python interpreter _could_ be making progress on a
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separate thread, it will be stuck waiting on the current operation to complete. We'll look at some
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techniques below for managing the GIL in a Python extension.
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# Pybind11
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The motto of [Pybind11](https://github.com/pybind/pybind11) is "seamless operability between C++11
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and Python", and they certainly deliver on that. Setting up a hybrid project where C++ (using CMake)
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and Python (using setuptools) could coexist was straight-forward, and the repository also works as
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[a template](https://github.com/speice-io/release-the-gil-pybind11/settings) for future projects.
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There's a great deal of overlap between Pybind11 and Cython. Where Pybind11 makes it easy for C++ to
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interact with the interpreter, Cython uses a Python-like language to facilitate interaction with
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C++. Another way of thinking about is like this: Pybind11 is for C++ developers who want to interact
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with Python, and Cython is for Python developers who want to interact with C++.
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Just like the previous post, we'll examine a simple Fibonacci sequence implementation to demonstrate
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how Python's threading model interacts with Pybind11:
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```c++
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#include <cstdint>
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#include <pybind11/pybind.h>
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inline std::uint64_t fibonacci(std::uint64_t n) {
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if (n <= 1) {
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return n;
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}
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std::uint64_t a = 0;
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std::uint64_t b = 1;
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std::uint64_t c = a + b;
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for (std::uint64_t _i = 2; _i < n; _i++) {
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a = b;
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b = c;
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c = a + b;
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}
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return c;
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}
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std::uint64_t fibonacci_gil(std::uint64_t n) {
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// The GIL is held by default when entering C++ from Python, so we need no
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// manipulation here. Interestingly enough, re-acquiring a held GIL is a safe
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// operation (within the same thread), so feel free to scatter
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// `py::gil_scoped_acquire` throughout the code.
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return fibonacci(n);
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}
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std::uint64_t fibonacci_nogil(std::uint64_t n) {
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// Because the GIL is held by default, we need to explicitly release it here
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// to run in parallel.
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// WARNING: Releasing the lock multiple times will crash the process.
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py::gil_scoped_release release;
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return fibonacci(n);
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}
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PYBIND11_MODULE(speiceio_pybind11, m) {
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m.def("fibonacci_gil", &fibonacci_gil, R"pbdoc(
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Calculate the Nth Fibonacci number while implicitly holding the GIL
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)pbdoc");
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m.def("fibonacci_nogil", &fibonacci_nogil,
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R"pbdoc(
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Calculate the Nth Fibonacci number after explicitly unlocking the GIL
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)pbdoc");
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#ifdef VERSION_INFO
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m.attr("__version__") = VERSION_INFO;
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#else
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m.attr("__version__") = "dev";
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#endif
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}
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```
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After building the C++ module, those functions can be used to demonstrate the effect of unlocking
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the GIL.
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```python
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# The billionth Fibonacci number overflows `std::uint64_t`, but that's OK;
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# our purpose is keeping the CPU busy, not getting the correct result.
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N = 1_000_000_000;
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from speiceio_pybind11 import fibonacci_gil, fibonacci_nogil
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```
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In the first example, even though two threads are used, the GIL constrains code to run in serial:
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```python
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%%time
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from threading import Thread
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# Create the two threads to run on
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t1 = Thread(target=fibonacci_gil, args=[N])
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t2 = Thread(target=fibonacci_gil, args=[N])
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# Start the threads
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t1.start(); t2.start()
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# Wait for the threads to finish
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t1.join(); t2.join()
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```
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> <pre>
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> CPU times: user 709 ms, sys: 0 ns, total: 709 ms
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> Wall time: 705 ms
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> </pre>
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Because the elapsed ("wall") time is effectively the same as the time spent executing on the CPU
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("user"), there was no benefit to using multiple threads.
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However, if one thread unlocks the GIL first, the Python interpreter is allowed to execute the
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second thread in parallel:
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```python
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%%time
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t1 = Thread(target=fibonacci_nogil, args=[N])
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t2 = Thread(target=fibonacci_gil, args=[N])
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t1.start(); t2.start()
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t1.join(); t2.join()
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```
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> <pre>
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> CPU times: user 734 ms, sys: 7.89 ms, total: 742 ms
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> Wall time: 372 ms
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> </pre>
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The CPU time ("user") hasn't changed much, but the elapsed time ("wall") is effectively cut in half.
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Caution is advised though; attempting to unlock the GIL when it isn't locked will terminate the
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current process:
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```c++
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void recurse_unlock() {
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py::gil_scoped_release release;
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return recurse_unlock();
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}
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```
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> <pre>
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> Python 3.8.2 (default, Apr 27 2020, 15:53:34)
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> [GCC 9.3.0] on linux
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> Type "help", "copyright", "credits" or "license" for more information.
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> >>> from speiceio_pybind11 import recurse_unlock
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> >>> recurse_unlock()
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> Fatal Python error: PyEval_SaveThread: NULL tstate
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> Python runtime state: initialized
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>
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> Current thread 0x00007f213a627740 (most recent call first):
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> File "<stdin>", line 1 in <module>
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> [1] 34943 abort (core dumped) python
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> </pre>
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# PyO3
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Now that pyo3 is stable, represents a great candidate for bridge.
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```rust
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use pyo3::prelude::*;
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use pyo3::wrap_pyfunction;
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fn fibonacci_impl(n: u64) -> u64 {
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if n <= 1 {
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return n;
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}
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let mut a: u64 = 0;
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let mut b: u64 = 1;
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let mut c: u64 = a + b;
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for _i in 2..n {
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a = b;
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b = c;
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// We're not particularly concerned about the actual result, just in keeping the
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// processor busy.
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c = a.overflowing_add(b).0;
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}
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c
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}
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#[pyfunction]
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fn fibonacci_gil(n: u64) -> PyResult<u64> {
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// The GIL is implicitly held here
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Ok(fibonacci_impl(n))
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}
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#[pyfunction]
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fn fibonacci_nogil(py: Python, n: u64) -> PyResult<u64> {
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// Explicitly release the GIL
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py.allow_threads(|| Ok(fibonacci_impl(n)))
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}
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#[pymodule]
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fn speiceio_pyo3(_py: Python, m: &PyModule) -> PyResult<()> {
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m.add_wrapped(wrap_pyfunction!(fibonacci_gil))?;
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m.add_wrapped(wrap_pyfunction!(fibonacci_nogil))?;
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Ok(())
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}
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```
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```python
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N = 1_000_000_000;
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from speiceio_pyo3 import fibonacci_gil, fibonacci_nogil
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```
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```python
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%%time
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from threading import Thread
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# Create the two threads to run on
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t1 = Thread(target=fibonacci_gil, args=[N])
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t2 = Thread(target=fibonacci_gil, args=[N])
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# Start the threads
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t1.start(); t2.start()
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# Wait for the threads to finish
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t1.join(); t2.join()
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```
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> <pre>
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> CPU times: user 503 ms, sys: 3.83 ms, total: 507 ms
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> Wall time: 506 ms
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> </pre>
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```python
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%%time
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t1 = Thread(target=fibonacci_nogil, args=[N])
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t2 = Thread(target=fibonacci_gil, args=[N])
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t1.start(); t2.start()
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t1.join(); t2.join()
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```
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> <pre>
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> CPU times: user 501 ms, sys: 3.96 ms, total: 505 ms
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> Wall time: 252 ms
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> </pre>
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Interestingly enough, Rust's borrow rules actually _prevent_ double-unlocking because the GIL handle
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can't be transferred across threads:
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```rust
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fn recursive_unlock(py: Python) -> PyResult<()> {
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py.allow_threads(|| recursive_unlock(py))
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}
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```
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> <pre>
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> error[E0277]: `std::rc::Rc<()>` cannot be shared between threads safely
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> --> src/lib.rs:38:8
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> |
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> 38 | py.allow_threads(|| recursive_unlock(py))
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> | ^^^^^^^^^^^^^ `std::rc::Rc<()>` cannot be shared between threads safely
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> |
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> = help: within `pyo3::python::Python<'_>`, the trait `std::marker::Sync` is not implemented for `std::rc::Rc<()>`
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> </pre>
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