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+---
+layout: post
+title: "Release the GIL: Part 2 - Pybind11, PyO3"
+description: "More Python Parallelism"
+category:
+tags: [python]
+---
+
+I've been continuing experiments with parallelism in Python; while these techniques are a bit niche,
+it's still fun to push the performance envelope. In addition to tools like
+[Cython](https://cython.org/) and [Numba](https://numba.pydata.org/) (covered
+[here](//2019/12/release-the-gil.html)) that attempt to stay as close to Python as possible, other
+projects are available that act as a bridge between Python and other languages. The goal is to make
+cooperation simple without compromising independence.
+
+In practice, this "cooperation" between languages is important for performance reasons. Code written
+in C++ shouldn't have to care about the Python GIL. However, unless the GIL is explicitly unlocked,
+it will remain implicitly held; though the Python interpreter _could_ be making progress on a
+separate thread, it will be stuck waiting on the current operation to complete. We'll look at some
+techniques below for managing the GIL in a Python extension.
+
+# Pybind11
+
+The motto of [Pybind11](https://github.com/pybind/pybind11) is "seamless operability between C++11
+and Python", and they certainly deliver on that. My experience was that it was relatively simple to
+set up a hybrid project where C++ (using CMake) and Python (using setuptools) were able to
+peacefully coexist. We'll examine a simple Fibonacci sequence implementation to demonstrate how
+Python's threading model interacts with Pybind11.
+
+The C++ implementation is very simple:
+
+```c++
+#include <cstdint>
+
+inline std::uint64_t fibonacci(std::uint64_t n) {
+  if (n <= 1) {
+    return n;
+  }
+
+  std::uint64_t a = 0;
+  std::uint64_t b = 1;
+  std::uint64_t c = 0;
+
+  c = a + b;
+  for (std::uint64_t _i = 2; _i < n; _i++) {
+    a = b;
+    b = c;
+    c = a + b;
+  }
+
+  return c;
+}
+
+std::uint64_t fibonacci_gil(std::uint64_t n) {
+  // The GIL is held by default when entering C++ from Python, so we need no
+  // manipulation here. Interestingly enough, re-acquiring a held GIL is a safe
+  // operation (within the same thread), so feel free to scatter
+  // `py::gil_scoped_acquire` throughout the code.
+  return fibonacci(n);
+}
+
+std::uint64_t fibonacci_nogil(std::uint64_t n) {
+  // Because the GIL is held by default, we need to explicitly release it here.
+  // Note that like Cython, releasing the lock multiple times will crash the
+  // interpreter.
+
+  py::gil_scoped_release release;
+  return fibonacci(n);
+}
+```
+
+Admittedly, the project setup is significantly more involved than Cython or Numba. I've omitted
+those steps here, but the full project is available at [INSERT LINK HERE].
+
+```python
+# This number will overflow, but that's OK; our purpose isn't to get an accurate result,
+# it's simply to keep the processor busy.
+N = 1_000_000_000;
+
+from fibonacci import fibonacci_gil, fibonacci_nogil
+```
+
+We'll first run each function independently:
+
+```python
+%%time
+_ = fibonacci_gil(N);
+```
+
+> <pre>
+> CPU times: user 350 ms, sys: 3.54 ms, total: 354 ms
+> Wall time: 355 ms
+> </pre>
+
+```python
+%%time
+_ = fibonacci_nogil(N);
+```
+
+> <pre>
+> CPU times: user 385 ms, sys: 0 ns, total: 385 ms
+> Wall time: 384 ms
+> </pre>
+
+There's some minor variation in how long it takes to run the code, but not a material difference.
+When running the same function in multiple threads, we expect the run time to double; even though
+there are multiple threads, they effectively run in serial because of the GIL:
+
+```python
+%%time
+from threading import Thread
+
+# Create the two threads to run on
+t1 = Thread(target=fibonacci_gil, args=[N])
+t2 = Thread(target=fibonacci_gil, args=[N])
+# Start the threads
+t1.start(); t2.start()
+# Wait for the threads to finish
+t1.join(); t2.join()
+```
+
+> <pre>
+> CPU times: user 709 ms, sys: 0 ns, total: 709 ms
+> Wall time: 705 ms
+> </pre>
+
+However, if one thread unlocks the GIL first, then the threads will execute in parallel:
+
+```python
+%%time
+
+t1 = Thread(target=fibonacci_nogil, args=[N])
+t2 = Thread(target=fibonacci_gil, args=[N])
+t1.start(); t2.start()
+t1.join(); t2.join()
+```
+
+> <pre>
+> CPU times: user 734 ms, sys: 7.89 ms, total: 742 ms
+> Wall time: 372 ms
+> </pre>
+
+While it takes the same amount of CPU time to compute the result ("user" time), the run time ("wall"
+time) is cut in half because the code is now running in parallel.
+
+```python
+%%time
+
+# Note that the GIL-locked version is started first
+t1 = Thread(target=fibonacci_gil, args=[N])
+t2 = Thread(target=fibonacci_nogil, args=[N])
+t1.start(); t2.start()
+t1.join(); t2.join()
+```
+
+> <pre>
+> CPU times: user 736 ms, sys: 0 ns, total: 736 ms
+> Wall time: 734 ms
+> </pre>
+
+Finally, it's import to note that scheduling matters; in this example, threads run in serial because
+the GIL-locked thread is started first.