submitting cpu-bound tasks to processes and io-bound tasks to threads
Project description
Convert a classic sequential program into a parallel one.
Why?
It runs faster.
What if not?
Don’t use it.
How?
Like this:
for image in images:
create_thumbnail(image) # original
for image in images:
fork(create_thumbnail, image) # parallelized explicitly
for image in images:
create_thumbnail(image) # parallelized implictly (Caution: Magic)
What about return values?
result = fork(my_func, *args, **kwargs)
And what is this result?
A future that behaves almost exactly as if it were the return value of my_func. That in turn means, as soon as you access the result and it is not ready yet, the main thread blocks.
Speaking of threads …
and processes? That depends on whether your function is @io_bound or @cpu_bound. Not decorated means @cpu_bound.
If necessary, decorate your functions:
@io_bound
def call_remote_webservice():
# implementation
@cpu_bound
def fib(n):
# naive implementation of Fibonacci numbers
@unsafe # don't fork; run sequentially
def weird_side_effects(*args, **kwargs):
# implementation
Something else?
Sure. If you don’t like the fork calling syntax, try those decorators:
@io_bound_fork
def create_thumbnail_by_webservice(image):
# implementation
@cpu_bound_fork
def create_thumbnail_by_bare_processing_power(image):
# implementation
# the following two lines spawn two forks
create_thumbnail_by_webservice()
create_thumbnail_by_bare_processing_power()
Conclusion
Good
easy way back and forth (from sequential to parallel and vice versa)
cascading possible (thread-safe)
Python 3 (out of the box)
Python 2 (via pip install futures)
Bad
weird calling syntax (no syntax support)
type(result) == BlockingFuture
not working with coroutines (asyncio) yet
future is not contagious yet
not working with lambdas due to PickleError
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