mirror-github-bspeice/speice.io
1
0
Fork 0
mirror of https://github.com/bspeice/speice.io synced 2024-12-22 00:28:10 -05:00
Code Issues Releases Wiki Activity

Post on releasing the GIL with Python

Browse Source
This commit is contained in:
Bradlee Speice 2019-11-30 12:26:12 -05:00
parent 38201e21a1
commit 7cae766039
1 changed files with 310 additions and 0 deletions
Show Stats Download Patch File Download Diff File Expand all files Collapse all files

310
_posts/2019-12-01-release-the-gil.md Normal file
View File

@ -0,0 +1,310 @@
---
layout: post
title: "Release the GIL"
description: "Strategies for Parallelism in Python"
category:
tags: [python]
---
Complaining about the [Global Interpreter Lock](https://wiki.python.org/moin/GlobalInterpreterLock) seems like a rite of passage for Python developers. It's easy to make fun of a design decision made back before multi-core CPU's were widely available, but in truth, it's generally [Good Enough](https://wiki.c2.com/?PrematureOptimization). Besides, it's not hard to start a [new process](https://docs.python.org/3/library/multiprocessing.html) and use message passing to synchronize. As an API design metaphor, message passing is great.
Still, one often wonders what could be possible if only the GIL wasn't holding them back. The thought of having only a single active interpreter thread seems so old-fashioned in an era where NodeJS and Go allow scheduling $M$ coroutines to $N$ system threads. Why can't Python learn to break free?
Presented below are some strategies for breaking free of the GIL's icy grip. Bear in mind that these are just the tools, and no claim is made about whether it's a good idea to use them. Very often, unlocking the GIL is an [XY problem](https://en.wikipedia.org/wiki/XY_problem); you want application performance, and the GIL seems like an obvious bottleneck. Just remember that you're intentionally breaking Python's memory model, and here be dragons.
```python
%load_ext Cython
from numba import jit
N = 1_000_000_000
```
# Cython
Put simply, [Cython](https://cython.org/) is a programming language that looks a lot like Python, gets translated to C or C++ before compiling, and has a lot of tools to interface with the [CPython](https://en.wikipedia.org/wiki/CPython) API. It's great for building Python wrappers to C and C++ libraries, writing optimized code for numerical processing, and a bunch of other things. As Coffeescript is to Javascript, so is Cython to C.
This article will focus on two specific utilities provided by Cython:
- The `nogil` [function annotation](https://cython.readthedocs.io/en/latest/src/userguide/external_C_code.html#declaring-a-function-as-callable-without-the-gil) marks a function as safe to use without the GIL
- The `with nogil` [context manager](https://cython.readthedocs.io/en/latest/src/userguide/external_C_code.html#releasing-the-gil) explicitly unlocks the CPython GIL while in that block
Whenever Cython code runs inside a `with nogil` block, the main Python process is allowed to continue doing work elsewhere. We'll calculate the Fibonacci sequence to demonstrate this principle in action:
```python
%%cython
# Annotating a function with `nogil` indicates only that it is safe
# to call in a `with nogil` block. It *does not* release the GIL.
cdef unsigned long fibonacci(unsigned long n) nogil:
if n <= 1:
return n
cdef unsigned long a = 0, b = 1, c = 0
c = a + b
for _i in range(2, n):
a = b
b = c
c = a + b
return c
def cython_nogil(unsigned long n):
# Explicitly release the GIL before calling `fibonacci`
with nogil:
value = fibonacci(n)
return value
def cython_gil(unsigned long n):
# Because the GIL is not explicitly released, it implicitly
# remains acquired.
return fibonacci(n)
```
First, let's time how long it takes Cython to calculate the billionth Fibonacci number:
```python
%%time
_ = cython_gil(N);
```
CPU times: user 365 ms, sys: 0 ns, total: 365 ms
Wall time: 372 ms
```python
%%time
_ = cython_nogil(N);
```
CPU times: user 381 ms, sys: 0 ns, total: 381 ms
Wall time: 388 ms
Both versions (with and without GIL) take approximately the same amount of time to run. If we run them in parallel, *even though two threads are used*, we expect the time to double because only one thread can be active at a time:
```python
%%time
from threading import Thread
# Create the two threads to run on
t1 = Thread(target=cython_gil, args=[N])
t2 = Thread(target=cython_gil, args=[N])
# Start the threads
t1.start(); t2.start()
# Wait for the threads to finish
t1.join(); t2.join()
```
CPU times: user 641 ms, sys: 5.62 ms, total: 647 ms
Wall time: 645 ms
However, if one thread releases the GIL, the second thread is free to acquire the GIL and perform its processing in parallel:
```python
%%time
t1 = Thread(target=cython_nogil, args=[N])
t2 = Thread(target=cython_gil, args=[N])
t1.start(); t2.start()
t1.join(); t2.join()
```
CPU times: user 717 ms, sys: 372 µs, total: 718 ms
Wall time: 358 ms
Keep in mind that **the order in which threads are started matters**!
```python
%%time
# Note that the GIL-locked version is started first
t1 = Thread(target=cython_gil, args=[N])
t2 = Thread(target=cython_nogil, args=[N])
t1.start(); t2.start()
t1.join(); t2.join()
```
CPU times: user 667 ms, sys: 0 ns, total: 667 ms
Wall time: 672 ms
Even though the second thread releases the GIL lock, it can't start until the first has completed, thus the overall runtime is double that of the previous example.
Finally, be aware that attempting to unlock the GIL from a thread that doesn't own it will crash the **interpreter**, not just the thread attempting the unlock:
```python
%%cython
cdef int cython_recurse(int n) nogil:
if n <= 0:
return 0
with nogil:
return cython_recurse(n - 1)
cython_recurse(2)
```
Output:
```text
Fatal Python error: PyEval_SaveThread: NULL tstate
Thread 0x00007f499effd700 (most recent call first):
File "/home/bspeice/.virtualenvs/release-the-gil/lib/python3.7/site-packages/ipykernel/parentpoller.py", line 39 in run
File "/usr/lib/python3.7/threading.py", line 926 in _bootstrap_inner
File "/usr/lib/python3.7/threading.py", line 890 in _bootstrap
```
In practice, it's easy to avoid this problem. While `nogil` functions likely shouldn't be managing the GIL themselves, Cython can also [conditionally acquire/release the GIL](https://cython.readthedocs.io/en/latest/src/userguide/external_C_code.html#conditional-acquiring-releasing-the-gil). Cython's documentation for [external C code](https://cython.readthedocs.io/en/latest/src/userguide/external_C_code.html#acquiring-and-releasing-the-gil) contains plenty of information on how to safely manage the GIL.
To conclude: use Cython's `nogil` annotation to mark functions as safe for calling when the GIL is unlocked, and `with nogil` to actually unlock the GIL. Because Cython refuses to compile code declared `nogil` if it interacts with the CPython API, it is difficult to trigger safety issues at runtime.
# Numba
Like Cython, [Numba](https://numba.pydata.org/) is also a "compiled Python." Where Cython works by compiling a Python-like language to C/C++, Numba compiles Python bytecode *directly to machine code* at runtime. Behavior is controlled using a special `@jit` decorator; calling a decorated function first compiles it to machine code, and then runs it. Calling the function a second time won't trigger recompilation unless the argument types change.
When writing code with Numba, we can unlock the GIL by adding `nogil=True` to the `@jit` decorator. Note that the `nogil` argument is separate from the `nopython` argument; while it is necessary for code to be compiled in [`nopython` mode](http://numba.pydata.org/numba-doc/latest/user/5minguide.html#what-is-nopython-mode) in order to release the GIL, the GIL will remain locked if `nogil=False` (which is the default).
We'll repeat the same Fibonacci experiment, this time using Numba instead of Cython:
```python
# The `int` type annotation is only for humans and is ignored
# by Numba.
@jit(nopython=True, nogil=True)
def numba_nogil(n: int) -> int:
if n <= 1:
return n
a = 0
b = 1
c = a + b
for _i in range(2, n):
a = b
b = c
c = a + b
return c
# We have to implement the algorithm multiple times
# because the annotation only applies to the current
# function, not functions that it calls.
@jit(nopython=True)
def numba_gil(n: int) -> int:
if n <= 1:
return n
a = 0
b = 1
c = a + b
for _i in range(2, n):
a = b
b = c
c = a + b
return c
# Call each function once to force compilation; we don't want
# the timing statistics to include the compilation step.
numba_nogil(N)
numba_gil(N);
```
We'll perform the same tests as Cython; first, figure out how long it takes to run:
```python
%%time
_ = numba_gil(N)
```
CPU times: user 253 ms, sys: 258 µs, total: 253 ms
Wall time: 251 ms
Aside: it's not immediately clear why Numba takes ~20% less time to produce the same result as Cython.
When running two GIL-locked threads in parallel, the result (as expected) takes around twice as long to compute:
```python
%%time
t1 = Thread(target=numba_gil, args=[N])
t2 = Thread(target=numba_gil, args=[N])
t1.start(); t2.start()
t1.join(); t2.join()
```
CPU times: user 541 ms, sys: 3.96 ms, total: 545 ms
Wall time: 541 ms
And when running the GIL-unlocking thread first, we can run threads in parallel:
```python
%%time
t1 = Thread(target=numba_nogil, args=[N])
t2 = Thread(target=numba_gil, args=[N])
t1.start(); t2.start()
t1.join(); t2.join()
```
CPU times: user 551 ms, sys: 7.77 ms, total: 559 ms
Wall time: 279 ms
Just like Cython, starting a GIL-locked thread first leads to overall runtime taking twice as long:
```python
%%time
t1 = Thread(target=numba_gil, args=[N])
t2 = Thread(target=numba_nogil, args=[N])
t1.start(); t2.start()
t1.join(); t2.join()
```
CPU times: user 524 ms, sys: 0 ns, total: 524 ms
Wall time: 522 ms
Finally, unlike Cython, Numba will unlock the GIL if and only if it is currently acquired; recursively calling `@jit(nogil=True)` functions is perfectly safe:
```python
from numba import jit
@jit(nopython=True, nogil=True)
def numba_recurse(n: int) -> int:
if n <= 0:
return 0
return numba_recurse(n - 1)
numba_recurse(2);
```
# Conclusion
While unlocking the GIL is often a solution in search of a problem, both Cython and Numba provide relatively easy means of unlocking the GIL. This enables true parallelism (not just [concurrency](https://stackoverflow.com/a/1050257)) that is impossible in vanilla Python.
Before finishing, it's important to address pain points that will show up applying these techniques to a more realistic project. First, production-ready code in a GIL-free context will likely need to worry about running code with non-trivial data structures; GIL-free functions aren't useful if they're constantly interacting with Python objects. Cython provides [extension types](http://docs.cython.org/en/latest/src/tutorial/cdef_classes.html) to address this, and Numba provides the [`@jitclass`](https://numba.pydata.org/numba-doc/dev/user/jitclass.html) decorator; both are outside the scope of this article. Second, building and distributing applications that make use of Cython/Numba can be complicated. Cython requires running the compiler, linking with external dependencies, and distributing a binary wheel. Because Numba is compiled just-in-time, installation/distribution is far simpler, but errors aren't detected until runtime.