Skip to content
Snippets Groups Projects
Commit bde3336e authored by Paul McCarthy's avatar Paul McCarthy :mountain_bicyclist:
Browse files

WIP: Nearly done with threading prac

parent 0727cba6
No related branches found
No related tags found
No related merge requests found
Hide whitespace changes
Inline Side-by-side
advanced_topics/07_threading.ipynb
+ 75
− 29
View file @ bde3336e
%% Cell type:markdown id: tags: %% Cell type:markdown id: tags:
# Threading and parallel processing # Threading and parallel processing
The Python language has built-in support for multi-threading in the The Python language has built-in support for multi-threading in the
[`threading`](https://docs.python.org/3/library/threading.html) module, and [`threading`](https://docs.python.org/3/library/threading.html) module, and
true parallelism in the true parallelism in the
[`multiprocessing`](https://docs.python.org/3/library/multiprocessing.html) [`multiprocessing`](https://docs.python.org/3/library/multiprocessing.html)
and and
[`concurrent.futures`](https://docs.python.org/3/library/concurrent.futures.html) [`concurrent.futures`](https://docs.python.org/3/library/concurrent.futures.html)
modules. If you want to be impressed, skip straight to the section on modules. If you want to be impressed, skip straight to the section on
[`multiprocessing`](todo). [`multiprocessing`](todo).
> *Note*: If you are familiar with a "real" programming language such as C++ > *Note*: If you are familiar with a "real" programming language such as C++
> or Java, you will be disappointed with the native support for parallelism in > or Java, you will be disappointed with the native support for parallelism in
> Python. Python threads do not run in parallel because of the Global > Python. Python threads do not run in parallel because of the Global
> Interpreter Lock, and if you use `multiprocessing`, be prepared to either > Interpreter Lock, and if you use `multiprocessing`, be prepared to either
> bear the performance hit of copying data between processes, or jump through > bear the performance hit of copying data between processes, or jump through
> hoops order to share data between processes. > hoops order to share data between processes.
> >
> This limitation *might* be solved in a future Python release by way of > This limitation *might* be solved in a future Python release by way of
> [*sub-interpreters*](https://www.python.org/dev/peps/pep-0554/), but the > [*sub-interpreters*](https://www.python.org/dev/peps/pep-0554/), but the
> author of this practical is not holding his breath. > author of this practical is not holding his breath.
## Threading ## Threading
The [`threading`](https://docs.python.org/3/library/threading.html) module The [`threading`](https://docs.python.org/3/library/threading.html) module
provides a traditional multi-threading API that should be familiar to you if provides a traditional multi-threading API that should be familiar to you if
you have worked with threads in other languages. you have worked with threads in other languages.
Running a task in a separate thread in Python is easy - simply create a Running a task in a separate thread in Python is easy - simply create a
`Thread` object, and pass it the function or method that you want it to `Thread` object, and pass it the function or method that you want it to
run. Then call its `start` method: run. Then call its `start` method:
%% Cell type:code id: tags: %% Cell type:code id: tags:
``` ```
import time import time
import threading import threading
def longRunningTask(niters): def longRunningTask(niters):
for i in range(niters): for i in range(niters):
if i % 2 == 0: print('Tick') if i % 2 == 0: print('Tick')
else: print('Tock') else: print('Tock')
time.sleep(0.5) time.sleep(0.5)
t = threading.Thread(target=longRunningTask, args=(8,)) t = threading.Thread(target=longRunningTask, args=(8,))
t.start() t.start()
while t.is_alive(): while t.is_alive():
time.sleep(0.4) time.sleep(0.4)
print('Waiting for thread to finish...') print('Waiting for thread to finish...')
print('Finished!') print('Finished!')
``` ```
%% Cell type:markdown id: tags: %% Cell type:markdown id: tags:
You can also `join` a thread, which will block execution in the current thread You can also `join` a thread, which will block execution in the current thread
until the thread that has been `join`ed has finished: until the thread that has been `join`ed has finished:
%% Cell type:code id: tags: %% Cell type:code id: tags:
``` ```
t = threading.Thread(target=longRunningTask, args=(6, )) t = threading.Thread(target=longRunningTask, args=(6, ))
t.start() t.start()
print('Joining thread ...') print('Joining thread ...')
t.join() t.join()
print('Finished!') print('Finished!')
``` ```
%% Cell type:markdown id: tags: %% Cell type:markdown id: tags:
### Subclassing `Thread` ### Subclassing `Thread`
It is also possible to sub-class the `Thread` class, and override its `run` It is also possible to sub-class the `Thread` class, and override its `run`
method: method:
%% Cell type:code id: tags: %% Cell type:code id: tags:
``` ```
class LongRunningThread(threading.Thread): class LongRunningThread(threading.Thread):
def __init__(self, niters, *args, **kwargs): def __init__(self, niters, *args, **kwargs):
super().__init__(*args, **kwargs) super().__init__(*args, **kwargs)
self.niters = niters self.niters = niters
def run(self): def run(self):
for i in range(self.niters): for i in range(self.niters):
if i % 2 == 0: print('Tick') if i % 2 == 0: print('Tick')
else: print('Tock') else: print('Tock')
time.sleep(0.5) time.sleep(0.5)
t = LongRunningThread(6) t = LongRunningThread(6)
t.start() t.start()
t.join() t.join()
print('Done') print('Done')
``` ```
%% Cell type:markdown id: tags: %% Cell type:markdown id: tags:
### Daemon threads ### Daemon threads
By default, a Python application will not exit until _all_ active threads have By default, a Python application will not exit until _all_ active threads have
finished execution. If you want to run a task in the background for the finished execution. If you want to run a task in the background for the
duration of your application, you can mark it as a `daemon` thread - when all duration of your application, you can mark it as a `daemon` thread - when all
non-daemon threads in a Python application have finished, all daemon threads non-daemon threads in a Python application have finished, all daemon threads
will be halted, and the application will exit. will be halted, and the application will exit.
You can mark a thread as being a daemon by setting an attribute on it after You can mark a thread as being a daemon by setting an attribute on it after
creation: creation:
%% Cell type:code id: tags: %% Cell type:code id: tags:
``` ```
t = threading.Thread(target=longRunningTask) t = threading.Thread(target=longRunningTask)
t.daemon = True t.daemon = True
``` ```
%% Cell type:markdown id: tags: %% Cell type:markdown id: tags:
See the [`Thread` See the [`Thread`
documentation](https://docs.python.org/3/library/threading.html#thread-objects) documentation](https://docs.python.org/3/library/threading.html#thread-objects)
for more details. for more details.
### Thread synchronisation ### Thread synchronisation
The `threading` module provides some useful thread-synchronisation primitives The `threading` module provides some useful thread-synchronisation primitives
- the `Lock`, `RLock` (re-entrant `Lock`), and `Event` classes. The - the `Lock`, `RLock` (re-entrant `Lock`), and `Event` classes. The
`threading` module also provides `Condition` and `Semaphore` classes - refer `threading` module also provides `Condition` and `Semaphore` classes - refer
to the [documentation](https://docs.python.org/3/library/threading.html) for to the [documentation](https://docs.python.org/3/library/threading.html) for
more details. more details.
#### `Lock` #### `Lock`
The [`Lock`](https://docs.python.org/3/library/threading.html#lock-objects) The [`Lock`](https://docs.python.org/3/library/threading.html#lock-objects)
class (and its re-entrant version, the class (and its re-entrant version, the
[`RLock`](https://docs.python.org/3/library/threading.html#rlock-objects)) [`RLock`](https://docs.python.org/3/library/threading.html#rlock-objects))
prevents a block of code from being accessed by more than one thread at a prevents a block of code from being accessed by more than one thread at a
time. For example, if we have multiple threads running this `task` function, time. For example, if we have multiple threads running this `task` function,
their [outputs](https://www.youtube.com/watch?v=F5fUFnfPpYU) will inevitably their [outputs](https://www.youtube.com/watch?v=F5fUFnfPpYU) will inevitably
become intertwined: become intertwined:
%% Cell type:code id: tags: %% Cell type:code id: tags:
``` ```
def task(): def task():
for i in range(5): for i in range(5):
print('{} Woozle '.format(i), end='') print('{} Woozle '.format(i), end='')
time.sleep(0.1) time.sleep(0.1)
print('Wuzzle') print('Wuzzle')
threads = [threading.Thread(target=task) for i in range(5)] threads = [threading.Thread(target=task) for i in range(5)]
for t in threads: for t in threads:
t.start() t.start()
``` ```
%% Cell type:markdown id: tags: %% Cell type:markdown id: tags:
But if we protect the critical section with a `Lock` object, the output will But if we protect the critical section with a `Lock` object, the output will
look more sensible: look more sensible:
%% Cell type:code id: tags: %% Cell type:code id: tags:
``` ```
lock = threading.Lock() lock = threading.Lock()
def task(): def task():
for i in range(5): for i in range(5):
with lock: with lock:
print('{} Woozle '.format(i), end='') print('{} Woozle '.format(i), end='')
time.sleep(0.1) time.sleep(0.1)
print('Wuzzle') print('Wuzzle')
threads = [threading.Thread(target=task) for i in range(5)] threads = [threading.Thread(target=task) for i in range(5)]
for t in threads: for t in threads:
t.start() t.start()
``` ```
%% Cell type:markdown id: tags: %% Cell type:markdown id: tags:
> Instead of using a `Lock` object in a `with` statement, it is also possible > Instead of using a `Lock` object in a `with` statement, it is also possible
> to manually call its `acquire` and `release` methods: > to manually call its `acquire` and `release` methods:
> >
> def task(): > def task():
> for i in range(5): > for i in range(5):
> lock.acquire() > lock.acquire()
> print('{} Woozle '.format(i), end='') > print('{} Woozle '.format(i), end='')
> time.sleep(0.1) > time.sleep(0.1)
> print('Wuzzle') > print('Wuzzle')
> lock.release() > lock.release()
Python does not have any built-in constructs to implement `Lock`-based mutual Python does not have any built-in constructs to implement `Lock`-based mutual
exclusion across several functions or methods - each function/method must exclusion across several functions or methods - each function/method must
explicitly acquire/release a shared `Lock` instance. However, it is relatively explicitly acquire/release a shared `Lock` instance. However, it is relatively
straightforward to implement a decorator which does this for you: straightforward to implement a decorator which does this for you:
%% Cell type:code id: tags: %% Cell type:code id: tags:
``` ```
def mutex(func, lock): def mutex(func, lock):
def wrapper(*args): def wrapper(*args):
with lock: with lock:
func(*args) func(*args)
return wrapper return wrapper
class MyClass(object): class MyClass(object):
def __init__(self): def __init__(self):
lock = threading.Lock() lock = threading.Lock()
self.safeFunc1 = mutex(self.safeFunc1, lock) self.safeFunc1 = mutex(self.safeFunc1, lock)
self.safeFunc2 = mutex(self.safeFunc2, lock) self.safeFunc2 = mutex(self.safeFunc2, lock)
def safeFunc1(self): def safeFunc1(self):
time.sleep(0.1) time.sleep(0.1)
print('safeFunc1 start') print('safeFunc1 start')
time.sleep(0.2) time.sleep(0.2)
print('safeFunc1 end') print('safeFunc1 end')
def safeFunc2(self): def safeFunc2(self):
time.sleep(0.1) time.sleep(0.1)
print('safeFunc2 start') print('safeFunc2 start')
time.sleep(0.2) time.sleep(0.2)
print('safeFunc2 end') print('safeFunc2 end')
mc = MyClass() mc = MyClass()
f1threads = [threading.Thread(target=mc.safeFunc1) for i in range(4)] f1threads = [threading.Thread(target=mc.safeFunc1) for i in range(4)]
f2threads = [threading.Thread(target=mc.safeFunc2) for i in range(4)] f2threads = [threading.Thread(target=mc.safeFunc2) for i in range(4)]
for t in f1threads + f2threads: for t in f1threads + f2threads:
t.start() t.start()
``` ```
%% Cell type:markdown id: tags: %% Cell type:markdown id: tags:
Try removing the `mutex` lock from the two methods in the above code, and see Try removing the `mutex` lock from the two methods in the above code, and see
what it does to the output. what it does to the output.
#### `Event` #### `Event`
The The
[`Event`](https://docs.python.org/3/library/threading.html#event-objects) [`Event`](https://docs.python.org/3/library/threading.html#event-objects)
class is essentially a boolean [semaphore][semaphore-wiki]. It can be used to class is essentially a boolean [semaphore][semaphore-wiki]. It can be used to
signal events between threads. Threads can `wait` on the event, and be awoken signal events between threads. Threads can `wait` on the event, and be awoken
when the event is `set` by another thread: when the event is `set` by another thread:
[semaphore-wiki]: https://en.wikipedia.org/wiki/Semaphore_(programming) [semaphore-wiki]: https://en.wikipedia.org/wiki/Semaphore_(programming)
%% Cell type:code id: tags: %% Cell type:code id: tags:
``` ```
import numpy as np import numpy as np
processingFinished = threading.Event() processingFinished = threading.Event()
def processData(data): def processData(data):
print('Processing data ...') print('Processing data ...')
time.sleep(2) time.sleep(2)
print('Result: {}'.format(data.mean())) print('Result: {}'.format(data.mean()))
processingFinished.set() processingFinished.set()
data = np.random.randint(1, 100, 100) data = np.random.randint(1, 100, 100)
t = threading.Thread(target=processData, args=(data,)) t = threading.Thread(target=processData, args=(data,))
t.start() t.start()
processingFinished.wait() processingFinished.wait()
print('Processing finished!') print('Processing finished!')
``` ```
%% Cell type:markdown id: tags: %% Cell type:markdown id: tags:
### The Global Interpreter Lock (GIL) ### The Global Interpreter Lock (GIL)
The [*Global Interpreter The [*Global Interpreter
Lock*](https://docs.python.org/3/c-api/init.html#thread-state-and-the-global-interpreter-lock) Lock*](https://docs.python.org/3/c-api/init.html#thread-state-and-the-global-interpreter-lock)
is an implementation detail of [CPython](https://github.com/python/cpython) is an implementation detail of [CPython](https://github.com/python/cpython)
(the official Python interpreter). The GIL means that a multi-threaded (the official Python interpreter). The GIL means that a multi-threaded
program written in pure Python is not able to take advantage of multiple program written in pure Python is not able to take advantage of multiple
cores - this essentially means that only one thread may be executing at any cores - this essentially means that only one thread may be executing at any
point in time. point in time.
The `threading` module does still have its uses though, as this GIL problem The `threading` module does still have its uses though, as this GIL problem
does not affect tasks which involve calls to system or natively compiled does not affect tasks which involve calls to system or natively compiled
libraries (e.g. file and network I/O, Numpy operations, etc.). So you can, libraries (e.g. file and network I/O, Numpy operations, etc.). So you can,
for example, perform some expensive processing on a Numpy array in a thread for example, perform some expensive processing on a Numpy array in a thread
running on one core, whilst having another thread (e.g. user interaction) running on one core, whilst having another thread (e.g. user interaction)
running on another core. running on another core.
## Multiprocessing ## Multiprocessing
For true parallelism, you should check out the For true parallelism, you should check out the
[`multiprocessing`](https://docs.python.org/3/library/multiprocessing.html) [`multiprocessing`](https://docs.python.org/3/library/multiprocessing.html)
module. module.
The `multiprocessing` module spawns sub-processes, rather than threads, and so The `multiprocessing` module spawns sub-processes, rather than threads, and so
is not subject to the GIL constraints that the `threading` module suffers is not subject to the GIL constraints that the `threading` module suffers
from. It provides two APIs - a "traditional" equivalent to that provided by from. It provides two APIs - a "traditional" equivalent to that provided by
the `threading` module, and a powerful higher-level API. the `threading` module, and a powerful higher-level API.
### `threading`-equivalent API ### `threading`-equivalent API
The The
[`Process`](https://docs.python.org/3/library/multiprocessing.html#the-process-class) [`Process`](https://docs.python.org/3/library/multiprocessing.html#the-process-class)
class is the `multiprocessing` equivalent of the class is the `multiprocessing` equivalent of the
[`threading.Thread`](https://docs.python.org/3/library/threading.html#thread-objects) [`threading.Thread`](https://docs.python.org/3/library/threading.html#thread-objects)
class. `multprocessing` also has equivalents of the [`Lock` and `Event` class. `multprocessing` also has equivalents of the [`Lock` and `Event`
classes](https://docs.python.org/3/library/multiprocessing.html#synchronization-between-processes), classes](https://docs.python.org/3/library/multiprocessing.html#synchronization-between-processes),
and the other synchronisation primitives provided by `threading`. and the other synchronisation primitives provided by `threading`.
So you can simply replace `threading.Thread` with `multiprocessing.Process`, So you can simply replace `threading.Thread` with `multiprocessing.Process`,
and you will have true parallelism. and you will have true parallelism.
Because your "threads" are now independent processes, you need to be a little Because your "threads" are now independent processes, you need to be a little
careful about how to share information across them. If you only need to share careful about how to share information across them. If you only need to share
small amounts of data, you can use the [`Queue` and `Pipe` small amounts of data, you can use the [`Queue` and `Pipe`
classes](https://docs.python.org/3/library/multiprocessing.html#exchanging-objects-between-processes), classes](https://docs.python.org/3/library/multiprocessing.html#exchanging-objects-between-processes),
in the `multiprocessing` module. If you are working with large amounts of data in the `multiprocessing` module. If you are working with large amounts of data
where copying between processes is not feasible, things become more where copying between processes is not feasible, things become more
complicated, but read on... complicated, but read on...
### Higher-level API - the `multiprocessing.Pool` ### Higher-level API - the `multiprocessing.Pool`
The real advantages of `multiprocessing` lie in its higher level API, centered The real advantages of `multiprocessing` lie in its higher level API, centered
around the [`Pool` around the [`Pool`
class](https://docs.python.org/3/library/multiprocessing.html#using-a-pool-of-workers). class](https://docs.python.org/3/library/multiprocessing.html#using-a-pool-of-workers).
Essentially, you create a `Pool` of worker processes - you specify the number Essentially, you create a `Pool` of worker processes - you specify the number
of processes when you create the pool. Once you have created a `Pool`, you can of processes when you create the pool. Once you have created a `Pool`, you can
use its methods to automatically parallelise tasks. The most useful are the use its methods to automatically parallelise tasks. The most useful are the
`map`, `starmap` and `apply_async` methods. `map`, `starmap` and `apply_async` methods.
> The best number of processes to use for a `Pool` will depend on the system > The best number of processes to use for a `Pool` will depend on the system
> you are running on (number of cores), and the tasks you are running (e.g. > you are running on (number of cores), and the tasks you are running (e.g.
> I/O bound or CPU bound). > I/O bound or CPU bound).
#### `Pool.map` #### `Pool.map`
The The
[`Pool.map`](https://docs.python.org/3/library/multiprocessing.html#multiprocessing.pool.Pool.map) [`Pool.map`](https://docs.python.org/3/library/multiprocessing.html#multiprocessing.pool.Pool.map)
method is the multiprocessing equivalent of the built-in method is the multiprocessing equivalent of the built-in
[`map`](https://docs.python.org/3/library/functions.html#map) function - it [`map`](https://docs.python.org/3/library/functions.html#map) function - it
is given a function, and a sequence, and it applies the function to each is given a function, and a sequence, and it applies the function to each
element in the sequence. element in the sequence.
%% Cell type:code id: tags: %% Cell type:code id: tags:
``` ```
import time import time
import multiprocessing as mp import multiprocessing as mp
import numpy as np import numpy as np
def crunchImage(imgfile): def crunchImage(imgfile):
# Load a nifti image, do stuff # Load a nifti image, do stuff
# to it. Use your imagination # to it. Use your imagination
# to fill in this function. # to fill in this function.
time.sleep(2) time.sleep(2)
# numpy's random number generator # numpy's random number generator
# will be initialised in the same # will be initialised in the same
# way in each process, so let's # way in each process, so let's
# re-seed it. # re-seed it.
np.random.seed() np.random.seed()
result = np.random.randint(1, 100, 1) result = np.random.randint(1, 100, 1)
print(imgfile, ':', result) print(imgfile, ':', result)
return result return result
imgfiles = ['{:02d}.nii.gz'.format(i) for i in range(20)] imgfiles = ['{:02d}.nii.gz'.format(i) for i in range(20)]
p = mp.Pool(processes=16) p = mp.Pool(processes=16)
print('Crunching images...') print('Crunching images...')
start = time.time() start = time.time()
results = p.map(crunchImage, imgfiles) results = p.map(crunchImage, imgfiles)
end = time.time() end = time.time()
print('Total execution time: {:0.2f} seconds'.format(end - start)) print('Total execution time: {:0.2f} seconds'.format(end - start))
``` ```
%% Cell type:markdown id: tags: %% Cell type:markdown id: tags:
The `Pool.map` method only works with functions that accept one argument, such The `Pool.map` method only works with functions that accept one argument, such
as our `crunchImage` function above. If you have a function which accepts as our `crunchImage` function above. If you have a function which accepts
multiple arguments, use the multiple arguments, use the
[`Pool.starmap`](https://docs.python.org/3/library/multiprocessing.html#multiprocessing.pool.Pool.starmap) [`Pool.starmap`](https://docs.python.org/3/library/multiprocessing.html#multiprocessing.pool.Pool.starmap)
method instead: method instead:
%% Cell type:code id: tags: %% Cell type:code id: tags:
``` ```
def crunchImage(imgfile, modality): def crunchImage(imgfile, modality):
time.sleep(2) time.sleep(2)
np.random.seed() np.random.seed()
if modality == 't1': if modality == 't1':
result = np.random.randint(1, 100, 1) result = np.random.randint(1, 100, 1)
elif modality == 't2': elif modality == 't2':
result = np.random.randint(100, 200, 1) result = np.random.randint(100, 200, 1)
print(imgfile, ': ', result) print(imgfile, ': ', result)
return result return result
imgfiles = ['t1_{:02d}.nii.gz'.format(i) for i in range(10)] + \ imgfiles = ['t1_{:02d}.nii.gz'.format(i) for i in range(10)] + \
['t2_{:02d}.nii.gz'.format(i) for i in range(10)] ['t2_{:02d}.nii.gz'.format(i) for i in range(10)]
modalities = ['t1'] * 10 + ['t2'] * 10 modalities = ['t1'] * 10 + ['t2'] * 10
pool = mp.Pool(processes=16) pool = mp.Pool(processes=16)
args = [(f, m) for f, m in zip(imgfiles, modalities)] args = [(f, m) for f, m in zip(imgfiles, modalities)]
print('Crunching images...') print('Crunching images...')
start = time.time() start = time.time()
results = pool.starmap(crunchImage, args) results = pool.starmap(crunchImage, args)
end = time.time() end = time.time()
print('Total execution time: {:0.2f} seconds'.format(end - start)) print('Total execution time: {:0.2f} seconds'.format(end - start))
``` ```
%% Cell type:markdown id: tags: %% Cell type:markdown id: tags:
The `map` and `starmap` methods also have asynchronous equivalents `map_async` The `map` and `starmap` methods also have asynchronous equivalents `map_async`
and `starmap_async`, which return immediately. Refer to the and `starmap_async`, which return immediately. Refer to the
[`Pool`](https://docs.python.org/3/library/multiprocessing.html#module-multiprocessing.pool) [`Pool`](https://docs.python.org/3/library/multiprocessing.html#module-multiprocessing.pool)
documentation for more details. documentation for more details.
#### `Pool.apply_async` #### `Pool.apply_async`
The The
[`Pool.apply`](https://docs.python.org/3/library/multiprocessing.html#multiprocessing.pool.Pool.apply) [`Pool.apply`](https://docs.python.org/3/library/multiprocessing.html#multiprocessing.pool.Pool.apply)
method will execute a function on one of the processes, and block until it has method will execute a function on one of the processes, and block until it has
finished. The finished. The
[`Pool.apply_async`](https://docs.python.org/3/library/multiprocessing.html#multiprocessing.pool.Pool.apply_async) [`Pool.apply_async`](https://docs.python.org/3/library/multiprocessing.html#multiprocessing.pool.Pool.apply_async)
method returns immediately, and is thus more suited to asynchronously method returns immediately, and is thus more suited to asynchronously
scheduling multiple jobs to run in parallel. scheduling multiple jobs to run in parallel.
`apply_async` returns an object of type `apply_async` returns an object of type
[`AsyncResult`](https://docs.python.org/3/library/multiprocessing.html#multiprocessing.pool.AsyncResult). [`AsyncResult`](https://docs.python.org/3/library/multiprocessing.html#multiprocessing.pool.AsyncResult).
An `AsyncResult` object has `wait` and `get` methods which will block until An `AsyncResult` object has `wait` and `get` methods which will block until
the job has completed. the job has completed.
%% Cell type:code id: tags: %% Cell type:code id: tags:
``` ```
import time import time
import multiprocessing as mp import multiprocessing as mp
import numpy as np import numpy as np
def linear_registration(src, ref): def linear_registration(src, ref):
time.sleep(1) time.sleep(1)
return np.eye(4) return np.eye(4)
def nonlinear_registration(src, ref, affine): def nonlinear_registration(src, ref, affine):
time.sleep(3) time.sleep(3)
# this number represents a non-linear warp # this number represents a non-linear warp
# field - use your imagination people! # field - use your imagination people!
np.random.seed() np.random.seed()
return np.random.randint(1, 100, 1) return np.random.randint(1, 100, 1)
t1s = ['{:02d}_t1.nii.gz'.format(i) for i in range(20)] t1s = ['{:02d}_t1.nii.gz'.format(i) for i in range(20)]
std = 'MNI152_T1_2mm.nii.gz' std = 'MNI152_T1_2mm.nii.gz'
pool = mp.Pool(processes=16) pool = mp.Pool(processes=16)
print('Running structural-to-standard registration ' print('Running structural-to-standard registration '
'on {} subjects...'.format(len(t1s))) 'on {} subjects...'.format(len(t1s)))
# Run linear registration on all the T1s. # Run linear registration on all the T1s.
# #
# We build a list of AsyncResult objects # We build a list of AsyncResult objects
linresults = [pool.apply_async(linear_registration, (t1, std)) linresults = [pool.apply_async(linear_registration, (t1, std))
for t1 in t1s] for t1 in t1s]
# Then we wait for each job to finish, # Then we wait for each job to finish,
# and replace its AsyncResult object # and replace its AsyncResult object
# with the actual result - an affine # with the actual result - an affine
# transformation matrix. # transformation matrix.
start = time.time() start = time.time()
for i, r in enumerate(linresults): for i, r in enumerate(linresults):
linresults[i] = r.get() linresults[i] = r.get()
end = time.time() end = time.time()
print('Linear registrations completed in ' print('Linear registrations completed in '
'{:0.2f} seconds'.format(end - start)) '{:0.2f} seconds'.format(end - start))
# Run non-linear registration on all the T1s, # Run non-linear registration on all the T1s,
# using the linear registrations to initialise. # using the linear registrations to initialise.
nlinresults = [pool.apply_async(nonlinear_registration, (t1, std, aff)) nlinresults = [pool.apply_async(nonlinear_registration, (t1, std, aff))
for (t1, aff) in zip(t1s, linresults)] for (t1, aff) in zip(t1s, linresults)]
# Wait for each non-linear reg to finish, # Wait for each non-linear reg to finish,
# and store the resulting warp field. # and store the resulting warp field.
start = time.time() start = time.time()
for i, r in enumerate(nlinresults): for i, r in enumerate(nlinresults):
nlinresults[i] = r.get() nlinresults[i] = r.get()
end = time.time() end = time.time()
print('Non-linear registrations completed in ' print('Non-linear registrations completed in '
'{:0.2f} seconds'.format(end - start)) '{:0.2f} seconds'.format(end - start))
print('Non linear registrations:') print('Non linear registrations:')
for t1, result in zip(t1s, nlinresults): for t1, result in zip(t1s, nlinresults):
print(t1, ':', result) print(t1, ':', result)
``` ```
%% Cell type:markdown id: tags: %% Cell type:markdown id: tags:
### Sharing data between processes ## Sharing data between processes
When you use the `Pool.map` method (or any of the other methods we have shown) When you use the `Pool.map` method (or any of the other methods we have shown)
to run a function on a sequence of items, those items must be copied into the to run a function on a sequence of items, those items must be copied into the
memory of each of the child processes. When the child processes are finished, memory of each of the child processes. When the child processes are finished,
the data that they return then has to be copied back to the parent process. the data that they return then has to be copied back to the parent process.
Any items which you wish to pass to a function that is executed by a `Pool` Any items which you wish to pass to a function that is executed by a `Pool`
must be *pickleable*<sup>1</sup> - the built-in must be *pickleable*<sup>1</sup> - the built-in
[`pickle`](https://docs.python.org/3/library/pickle.html) module is used by [`pickle`](https://docs.python.org/3/library/pickle.html) module is used by
`multiprocessing` to serialise and de-serialise the data passed to and `multiprocessing` to serialise and de-serialise the data passed to and
returned from a child process. The majority of standard Python types (`list`, returned from a child process. The majority of standard Python types (`list`,
`dict`, `str` etc), and Numpy arrays can be pickled and unpickled, so you only `dict`, `str` etc), and Numpy arrays can be pickled and unpickled, so you only
need to worry about this detail if you are passing objects of a custom type need to worry about this detail if you are passing objects of a custom type
(e.g. instances of classes that you have written, or that are defined in some (e.g. instances of classes that you have written, or that are defined in some
third-party library). third-party library).
> <sup>1</sup>*Pickleable* is the term used in the Python world to refer to > <sup>1</sup>*Pickleable* is the term used in the Python world to refer to
> something that is *serialisable* - basically, the process of converting an > something that is *serialisable* - basically, the process of converting an
> in-memory object into a binary form that can be stored and/or transmitted. > in-memory object into a binary form that can be stored and/or transmitted.
There is obviously some overhead in copying data back and forth between the There is obviously some overhead in copying data back and forth between the
main process and the worker processes; this may or may not be a problem. For main process and the worker processes; this may or may not be a problem. For
most computationally intensive tasks, this communication overhead is not most computationally intensive tasks, this communication overhead is not
important - the performance bottleneck is typically going to be the important - the performance bottleneck is typically going to be the
computation time, rather than I/O between the parent and child processes. computation time, rather than I/O between the parent and child processes.
However, if you are working with a large dataset, you have determined that However, if you are working with a large dataset, you have determined that
copying data between processes is having a substantial impact on your copying data between processes is having a substantial impact on your
performance, and instead wish to *share* a single copy of the data between performance, and instead wish to *share* a single copy of the data between
the processes, you will need to: the processes, you will need to:
1. Structure your code so that the data you want to share is accessible at 1. Structure your code so that the data you want to share is accessible at
the *module level*. the *module level*.
2. Define/create/load the data *before* creating the `Pool`. 2. Define/create/load the data *before* creating the `Pool`.
This is because, when you create a `Pool`, what actually happens is that the This is because, when you create a `Pool`, what actually happens is that the
process your Pythonn script is running in will [**fork**](wiki-fork) itself - process your Pythonn script is running in will [**fork**](wiki-fork) itself -
the child processes that are created are used as the worker processes by the the child processes that are created are used as the worker processes by the
`Pool`. And if you create/load your data in your main process *before* this `Pool`. And if you create/load your data in your main process *before* this
fork occurs, all of the child processes will inherit the memory space of the fork occurs, all of the child processes will inherit the memory space of the
main process, and will therefore have (read-only) access to the data, without main process, and will therefore have (read-only) access to the data, without
any copying required. any copying required.
[wiki-fork]: https://en.wikipedia.org/wiki/Fork_(system_call) [wiki-fork]: https://en.wikipedia.org/wiki/Fork_(system_call)
### Read-only sharing
Let's see this in action with a simple example. We'll start by defining a Let's see this in action with a simple example. We'll start by defining a
little helper function which allows us to track the total memory usage, using little helper function which allows us to track the total memory usage, using
the unix `free` command: the unix `free` command:
%% Cell type:code id: tags: %% Cell type:code id: tags:
``` ```
# todo mac version # todo mac version
import subprocess as sp import subprocess as sp
def memusage(msg): def memusage(msg):
stdout = sp.run(['free', '--mega'], capture_output=True).stdout.decode() stdout = sp.run(['free', '--mega'], capture_output=True).stdout.decode()
stdout = stdout.split('\n')[1].split() stdout = stdout.split('\n')[1].split()
total = stdout[1] total = stdout[1]
used = stdout[2] used = stdout[2]
print('Memory usage {}: {} / {} MB'.format(msg, used, total)) print('Memory usage {}: {} / {} MB'.format(msg, used, total))
``` ```
%% Cell type:markdown id: tags: %% Cell type:markdown id: tags:
Now our task is simply to calculate the sum of a large array of numbers. We're Now our task is simply to calculate the sum of a large array of numbers. We're
going to create a big chunk of data, and process it in chunks, keeping track going to create a big chunk of data, and process it in chunks, keeping track
of memory usage as the task progresses: of memory usage as the task progresses:
%% Cell type:code id: tags: %% Cell type:code id: tags:
``` ```
import time import time
memusage('before creating data') memusage('before creating data')
# allocate 500MB of data # allocate 500MB of data
data = np.random.random(500 * (1048576 // 8)) data = np.random.random(500 * (1048576 // 8))
# Assign nelems values to each worker # Assign nelems values to each worker
# process (hard-coded so we need 12 # process (hard-coded so we need 12
# jobs to complete the task) # jobs to complete the task)
nelems = len(data) // 12 nelems = len(data) // 12
memusage('after creating data') memusage('after creating data')
# Each job process nelems values, # Each job process nelems values,
# starting from the specified offset # starting from the specified offset
def process_chunk(offset): def process_chunk(offset):
time.sleep(1) time.sleep(1)
return data[offset:offset + nelems].mean() return data[offset:offset + nelems].sum()
# Create our worker process pool # Create our worker process pool
pool = mp.Pool(4) pool = mp.Pool(4)
# Generate an offset into the data for each # Generate an offset into the data for each
# job, and call process_chunk for each offset # job, and call process_chunk for each offset
offsets = range(0, len(data), nelems) offsets = range(0, len(data), nelems)
results = pool.map_async(process_chunk, offsets) results = pool.map_async(process_chunk, offsets)
# Wait for all of the jobs to finish # Wait for all of the jobs to finish
elapsed = 0 elapsed = 0
while not results.ready(): while not results.ready():
memusage('after {} seconds'.format(elapsed)) memusage('after {} seconds'.format(elapsed))
time.sleep(1) time.sleep(1)
elapsed += 1 elapsed += 1
results = results.get() results = results.get()
print('Total sum:', sum(results)) print('Total sum: ', sum(results))
print('Sanity check:', data.sum())
``` ```
%% Cell type:markdown id: tags: %% Cell type:markdown id: tags:
You should be able to see that only one copy of `data` is created, and is You should be able to see that only one copy of `data` is created, and is
shared by all of the worker processes without any copying taking place. shared by all of the worker processes without any copying taking place.
So if you only need read-only acess ... So things are reasonably straightforward if you only need read-only acess to
your data. But what if your worker processes need to be able to modify the
data? Go back to the code block above and:
1. Modify the `process_chunk` function so that it modifies every element of
its assigned portion of the data before calculating and returning the sum.
For example:
> ```
> data[offset:offset + nelems] += 1
> ```
But what if your worker processes need ... 2. Re-run the code block, and watch what happens to the memory usage.
Go back to the code block above and ...
What happened? Well, you are seeing [copy-on-write](wiki-copy-on-write) in
action. When the `process_chunk` is invoked, it is given a reference to the
original data array in the memory space of the parent process. But as soon as
an attempt is made to modify it, a copy of the data, in the memory space of
the child process, is created. The modifications are then applied to this
child process, and not to the original copy. So the total memory usage has
blown out to twice as much as before, and the changes made by each child
process are being lost!
[wiki-copy-on-write]: https://en.wikipedia.org/wiki/Copy-on-write
### Read/write sharing
> If you have worked with a real programming language with true parallelism > If you have worked with a real programming language with true parallelism
> and shared memory via within-process multi-threading, feel free to take a > and shared memory via within-process multi-threading, feel free to take a
> break at this point. Breathe. Relax. Go punch a hole in a wall. I've been > break at this point. Breathe. Relax. Go punch a hole in a wall. I've been
> coding in Python for years, and this still makes me angry. Sometimes > coding in Python for years, and this still makes me angry. Sometimes
> ... don't tell anyone I said this ... I even find myself wishing I were > ... don't tell anyone I said this ... I even find myself wishing I were
> coding in *Java* instead of Python. Ugh. I need to take a shower. > coding in *Java* instead of Python. Ugh. I need to take a shower.
In order to truly share memory between multiple processes, the
`multiprocessing` module provides the [`Value`, `Array`, and `RawArray`
The `multiprocessing` module provides the [`Value`, `Array`, and `RawArray`
classes](https://docs.python.org/3/library/multiprocessing.html#shared-ctypes-objects), classes](https://docs.python.org/3/library/multiprocessing.html#shared-ctypes-objects),
which allow you to share individual values, or arrays of values, respectively. which allow you to share individual values, or arrays of values, respectively.
The `Array` and `RawArray` classes essentially wrap a typed pointer (from the The `Array` and `RawArray` classes essentially wrap a typed pointer (from the
built-in [`ctypes`](https://docs.python.org/3/library/ctypes.html) module) built-in [`ctypes`](https://docs.python.org/3/library/ctypes.html) module) to
to a block of memory. We can use the `Array` or `RawArray` class to share a a block of memory. We can use the `Array` or `RawArray` class to share a Numpy
Numpy array between our worker processes. The difference between an `Array` array between our worker processes. The difference between an `Array` and a
and a `RawArray` is that the former offers synchronised (i.e. process-safe) `RawArray` is that the former offers low-level synchronised
access to the shared memory. This is necessary if your child processes will be (i.e. process-safe) access to the shared memory. This is necessary if your
modifying the same parts of your data. child processes will be modifying the same parts of your data.
Due to the way that shared memory works, in order to share a Numpy array > If you need fine-grained control over synchronising access to shared data by
between different processes you need to structure your code so that the > multiple processes, all of the [synchronisation
array(s) you want to share are accessible at the _module level_. Furthermore, > primitives](https://docs.python.org/3/library/multiprocessing.html#synchronization-between-processes)
we need to make sure that our input and output arrays are located in shared > from the `multiprocessing` module are at your disposal.
memory - we can do this via the `Array` or `RawArray`.
The requirements for sharing memory between processes still apply here - we
need to make our data accessible at the *module level*, and we need to create
our data before creating the `Pool`. And to achieve read and write capability,
we also need to make sure that our input and output arrays are located in
shared memory - we can do this via the `Array` or `RawArray`.
As an example, let's say we want to parallelise processing of an image by As an example, let's say we want to parallelise processing of an image by
having each worker process perform calculations on a chunk of the image. having each worker process perform calculations on a chunk of the image.
First, let's define a function which does the calculation on a specified set First, let's define a function which does the calculation on a specified set
of image coordinates: of image coordinates:
%% Cell type:code id: tags: %% Cell type:code id: tags:
``` ```
import multiprocessing as mp import multiprocessing as mp
import ctypes import ctypes
import numpy as np import numpy as np
np.set_printoptions(suppress=True) np.set_printoptions(suppress=True)
def process_chunk(shape, idxs): def process_chunk(shape, idxs):
# Get references to our # Get references to our
# input/output data, and # input/output data, and
# create Numpy array views # create Numpy array views
# into them. # into them.
sindata = process_chunk.input_data sindata = process_chunk.input_data
soutdata = process_chunk.output_data soutdata = process_chunk.output_data
indata = np.ctypeslib.as_array(sindata) .reshape(shape) indata = np.ctypeslib.as_array(sindata) .reshape(shape)
outdata = np.ctypeslib.as_array(soutdata).reshape(shape) outdata = np.ctypeslib.as_array(soutdata).reshape(shape)
# Do the calculation on # Do the calculation on
# the specified voxels # the specified voxels
outdata[idxs] = indata[idxs] ** 2 outdata[idxs] = indata[idxs] ** 2
``` ```
%% Cell type:markdown id: tags: %% Cell type:markdown id: tags:
Rather than passing the input and output data arrays in as arguments to the Rather than passing the input and output data arrays in as arguments to the
`process_chunk` function, we set them as attributes of the `process_chunk` `process_chunk` function, we set them as attributes of the `process_chunk`
function. This makes the input/output data accessible at the module level, function. This makes the input/output data accessible at the module level,
which is required in order to share the data between the main process and the which is required in order to share the data between the main process and the
child processes. child processes.
Now let's define a second function which process an entire image. It does the Now let's define a second function which process an entire image. It does the
following: following:
1. Initialises shared memory areas to store the input and output data. 1. Initialises shared memory areas to store the input and output data.
2. Copies the input data into shared memory. 2. Copies the input data into shared memory.
3. Sets the input and output data as attributes of the `process_chunk` function. 3. Sets the input and output data as attributes of the `process_chunk` function.
4. Creates sets of indices into the input data which, for each worker process, 4. Creates sets of indices into the input data which, for each worker process,
specifies the portion of the data that it is responsible for. specifies the portion of the data that it is responsible for.
5. Creates a worker pool, and runs the `process_chunk` function for each set 5. Creates a worker pool, and runs the `process_chunk` function for each set
of indices. of indices.
%% Cell type:code id: tags: %% Cell type:code id: tags:
``` ```
def process_dataset(data): def process_dataset(data):
nprocs = 8 nprocs = 8
origData = data origData = data
# Create arrays to store the # Create arrays to store the
# input and output data # input and output data
sindata = mp.RawArray(ctypes.c_double, data.size) sindata = mp.RawArray(ctypes.c_double, data.size)
soutdata = mp.RawArray(ctypes.c_double, data.size) soutdata = mp.RawArray(ctypes.c_double, data.size)
data = np.ctypeslib.as_array(sindata).reshape(data.shape) data = np.ctypeslib.as_array(sindata).reshape(data.shape)
outdata = np.ctypeslib.as_array(soutdata).reshape(data.shape) outdata = np.ctypeslib.as_array(soutdata).reshape(data.shape)
# Copy the input data # Copy the input data
# into shared memory # into shared memory
data[:] = origData data[:] = origData
# Make the input/output data # Make the input/output data
# accessible to the process_chunk # accessible to the process_chunk
# function. This must be done # function. This must be done
# *before* the worker pool is created. # *before* the worker pool is
# created - even though we are
# doing things differently to the
# read-only example, we are still
# making the data arrays accessible
# at the *module* level, so the
# memory they are stored in can be
# shared with the child processes.
process_chunk.input_data = sindata process_chunk.input_data = sindata
process_chunk.output_data = soutdata process_chunk.output_data = soutdata
# number of boxels to be computed # number of voxels to be computed
# by each worker process. # by each worker process.
nvox = int(data.size / nprocs) nvox = int(data.size / nprocs)
# Generate coordinates for # Generate coordinates for
# every voxel in the image # every voxel in the image
xlen, ylen, zlen = data.shape xlen, ylen, zlen = data.shape
xs, ys, zs = np.meshgrid(np.arange(xlen), xs, ys, zs = np.meshgrid(np.arange(xlen),
np.arange(ylen), np.arange(ylen),
np.arange(zlen)) np.arange(zlen))
xs = xs.flatten() xs = xs.flatten()
ys = ys.flatten() ys = ys.flatten()
zs = zs.flatten() zs = zs.flatten()
# We're going to pass each worker # We're going to pass each worker
# process a list of indices, which # process a list of indices, which
# specify the data items which that # specify the data items which that
# worker process needs to compute. # worker process needs to compute.
xs = [xs[nvox * i:nvox * i + nvox] for i in range(nprocs)] + \ xs = [xs[nvox * i:nvox * i + nvox] for i in range(nprocs)] + [xs[nvox * nprocs:]]
[xs[nvox * nprocs:]] ys = [ys[nvox * i:nvox * i + nvox] for i in range(nprocs)] + [ys[nvox * nprocs:]]
ys = [ys[nvox * i:nvox * i + nvox] for i in range(nprocs)] + \ zs = [zs[nvox * i:nvox * i + nvox] for i in range(nprocs)] + [zs[nvox * nprocs:]]
[ys[nvox * nprocs:]]
zs = [zs[nvox * i:nvox * i + nvox] for i in range(nprocs)] + \
[zs[nvox * nprocs:]]
# Build the argument lists for # Build the argument lists for
# each worker process. # each worker process.
args = [(data.shape, (x, y, z)) for x, y, z in zip(xs, ys, zs)] args = [(data.shape, (x, y, z)) for x, y, z in zip(xs, ys, zs)]
# Create a pool of worker # Create a pool of worker
# processes and run the jobs. # processes and run the jobs.
pool = mp.Pool(processes=nprocs) pool = mp.Pool(processes=nprocs)
pool.starmap(process_chunk, args) pool.starmap(process_chunk, args)
return outdata return outdata
``` ```
%% Cell type:markdown id: tags: %% Cell type:markdown id: tags:
Now we can call our `process_data` function just like any other function: Now we can call our `process_data` function just like any other function:
%% Cell type:code id: tags: %% Cell type:code id: tags:
``` ```
data = np.array(np.arange(64).reshape((4, 4, 4)), dtype=np.float64) data = np.array(np.arange(64).reshape((4, 4, 4)), dtype=np.float64)
outdata = process_dataset(data) outdata = process_dataset(data)
print('Input') print('Input')
print(data) print(data)
print('Output') print('Output')
print(outdata) print(outdata)
``` ```
%% Cell type:markdown id: tags:
## The `concurrent.futures` library
The `concurrent.futures` module provides a simpler alternative API to the
`multiprocessing` module - it focuses specifically on asynchronous execution
of tasks, so can be used instead of the `Pool.map_async` and
`Pool.apply_async` methods.
......
advanced_topics/07_threading.md
+ 71
− 29
View file @ bde3336e
...@@ -526,7 +526,7 @@ for t1, result in zip(t1s, nlinresults): ...@@ -526,7 +526,7 @@ for t1, result in zip(t1s, nlinresults):
``` ```
### Sharing data between processes ## Sharing data between processes
When you use the `Pool.map` method (or any of the other methods we have shown) When you use the `Pool.map` method (or any of the other methods we have shown)
...@@ -580,6 +580,9 @@ any copying required. ...@@ -580,6 +580,9 @@ any copying required.
[wiki-fork]: https://en.wikipedia.org/wiki/Fork_(system_call) [wiki-fork]: https://en.wikipedia.org/wiki/Fork_(system_call)
### Read-only sharing
Let's see this in action with a simple example. We'll start by defining a Let's see this in action with a simple example. We'll start by defining a
little helper function which allows us to track the total memory usage, using little helper function which allows us to track the total memory usage, using
the unix `free` command: the unix `free` command:
...@@ -621,7 +624,7 @@ memusage('after creating data') ...@@ -621,7 +624,7 @@ memusage('after creating data')
# starting from the specified offset # starting from the specified offset
def process_chunk(offset): def process_chunk(offset):
time.sleep(1) time.sleep(1)
return data[offset:offset + nelems].mean() return data[offset:offset + nelems].sum()
# Create our worker process pool # Create our worker process pool
pool = mp.Pool(4) pool = mp.Pool(4)
...@@ -639,23 +642,44 @@ while not results.ready(): ...@@ -639,23 +642,44 @@ while not results.ready():
elapsed += 1 elapsed += 1
results = results.get() results = results.get()
print('Total sum:', sum(results)) print('Total sum: ', sum(results))
print('Sanity check:', data.sum())
``` ```
You should be able to see that only one copy of `data` is created, and is You should be able to see that only one copy of `data` is created, and is
shared by all of the worker processes without any copying taking place. shared by all of the worker processes without any copying taking place.
So if you only need read-only acess ... So things are reasonably straightforward if you only need read-only acess to
your data. But what if your worker processes need to be able to modify the
data? Go back to the code block above and:
1. Modify the `process_chunk` function so that it modifies every element of
its assigned portion of the data before calculating and returning the sum.
For example:
> ```
> data[offset:offset + nelems] += 1
> ```
But what if your worker processes need ... 2. Re-run the code block, and watch what happens to the memory usage.
Go back to the code block above and ...
What happened? Well, you are seeing [copy-on-write](wiki-copy-on-write) in
action. When the `process_chunk` is invoked, it is given a reference to the
original data array in the memory space of the parent process. But as soon as
an attempt is made to modify it, a copy of the data, in the memory space of
the child process, is created. The modifications are then applied to this
child process, and not to the original copy. So the total memory usage has
blown out to twice as much as before, and the changes made by each child
process are being lost!
[wiki-copy-on-write]: https://en.wikipedia.org/wiki/Copy-on-write
### Read/write sharing
> If you have worked with a real programming language with true parallelism > If you have worked with a real programming language with true parallelism
> and shared memory via within-process multi-threading, feel free to take a > and shared memory via within-process multi-threading, feel free to take a
...@@ -665,27 +689,32 @@ Go back to the code block above and ... ...@@ -665,27 +689,32 @@ Go back to the code block above and ...
> coding in *Java* instead of Python. Ugh. I need to take a shower. > coding in *Java* instead of Python. Ugh. I need to take a shower.
In order to truly share memory between multiple processes, the
`multiprocessing` module provides the [`Value`, `Array`, and `RawArray`
The `multiprocessing` module provides the [`Value`, `Array`, and `RawArray`
classes](https://docs.python.org/3/library/multiprocessing.html#shared-ctypes-objects), classes](https://docs.python.org/3/library/multiprocessing.html#shared-ctypes-objects),
which allow you to share individual values, or arrays of values, respectively. which allow you to share individual values, or arrays of values, respectively.
The `Array` and `RawArray` classes essentially wrap a typed pointer (from the The `Array` and `RawArray` classes essentially wrap a typed pointer (from the
built-in [`ctypes`](https://docs.python.org/3/library/ctypes.html) module) built-in [`ctypes`](https://docs.python.org/3/library/ctypes.html) module) to
to a block of memory. We can use the `Array` or `RawArray` class to share a a block of memory. We can use the `Array` or `RawArray` class to share a Numpy
Numpy array between our worker processes. The difference between an `Array` array between our worker processes. The difference between an `Array` and a
and a `RawArray` is that the former offers synchronised (i.e. process-safe) `RawArray` is that the former offers low-level synchronised
access to the shared memory. This is necessary if your child processes will be (i.e. process-safe) access to the shared memory. This is necessary if your
modifying the same parts of your data. child processes will be modifying the same parts of your data.
Due to the way that shared memory works, in order to share a Numpy array > If you need fine-grained control over synchronising access to shared data by
between different processes you need to structure your code so that the > multiple processes, all of the [synchronisation
array(s) you want to share are accessible at the _module level_. Furthermore, > primitives](https://docs.python.org/3/library/multiprocessing.html#synchronization-between-processes)
we need to make sure that our input and output arrays are located in shared > from the `multiprocessing` module are at your disposal.
memory - we can do this via the `Array` or `RawArray`.
The requirements for sharing memory between processes still apply here - we
need to make our data accessible at the *module level*, and we need to create
our data before creating the `Pool`. And to achieve read and write capability,
we also need to make sure that our input and output arrays are located in
shared memory - we can do this via the `Array` or `RawArray`.
As an example, let's say we want to parallelise processing of an image by As an example, let's say we want to parallelise processing of an image by
...@@ -758,11 +787,18 @@ def process_dataset(data): ...@@ -758,11 +787,18 @@ def process_dataset(data):
# Make the input/output data # Make the input/output data
# accessible to the process_chunk # accessible to the process_chunk
# function. This must be done # function. This must be done
# *before* the worker pool is created. # *before* the worker pool is
# created - even though we are
# doing things differently to the
# read-only example, we are still
# making the data arrays accessible
# at the *module* level, so the
# memory they are stored in can be
# shared with the child processes.
process_chunk.input_data = sindata process_chunk.input_data = sindata
process_chunk.output_data = soutdata process_chunk.output_data = soutdata
# number of boxels to be computed # number of voxels to be computed
# by each worker process. # by each worker process.
nvox = int(data.size / nprocs) nvox = int(data.size / nprocs)
...@@ -781,12 +817,9 @@ def process_dataset(data): ...@@ -781,12 +817,9 @@ def process_dataset(data):
# process a list of indices, which # process a list of indices, which
# specify the data items which that # specify the data items which that
# worker process needs to compute. # worker process needs to compute.
xs = [xs[nvox * i:nvox * i + nvox] for i in range(nprocs)] + \ xs = [xs[nvox * i:nvox * i + nvox] for i in range(nprocs)] + [xs[nvox * nprocs:]]
[xs[nvox * nprocs:]] ys = [ys[nvox * i:nvox * i + nvox] for i in range(nprocs)] + [ys[nvox * nprocs:]]
ys = [ys[nvox * i:nvox * i + nvox] for i in range(nprocs)] + \ zs = [zs[nvox * i:nvox * i + nvox] for i in range(nprocs)] + [zs[nvox * nprocs:]]
[ys[nvox * nprocs:]]
zs = [zs[nvox * i:nvox * i + nvox] for i in range(nprocs)] + \
[zs[nvox * nprocs:]]
# Build the argument lists for # Build the argument lists for
# each worker process. # each worker process.
...@@ -794,7 +827,7 @@ def process_dataset(data): ...@@ -794,7 +827,7 @@ def process_dataset(data):
# Create a pool of worker # Create a pool of worker
# processes and run the jobs. # processes and run the jobs.
pool = mp.Pool(processes=nprocs) pool = mp.Pool(processes=nprocs)
pool.starmap(process_chunk, args) pool.starmap(process_chunk, args)
...@@ -816,3 +849,12 @@ print(data) ...@@ -816,3 +849,12 @@ print(data)
print('Output') print('Output')
print(outdata) print(outdata)
``` ```
## The `concurrent.futures` library
The `concurrent.futures` module provides a simpler alternative API to the
`multiprocessing` module - it focuses specifically on asynchronous execution
of tasks, so can be used instead of the `Pool.map_async` and
`Pool.apply_async` methods.
0% or .
You are about to add 0 people to the discussion. Proceed with caution.
Finish editing this message first!
Please register or to comment