Simpler Python multiprocess coding. Persistent workers, memory maps for minimum overhead.
A module that makes it easy to parallelize Python code.
- Install or add multicore to your Python path.
Python supports multi-threading but the global interpreter lock (GIL) prevents us from utilising all CPU cores for CPU heavy tasks. The recommended approach is to use Python’s multiprocessing library to work around the GIL, but that has its own set of challenges, notably the ability to share data between sub-processes is limited.
The goal of the multicore library is to make it as simple as possible to parallelize code while incurring the least amount of overhead.
- Persistent pool of workers enabling persistent database connections.
- Memory maps for inter process communication. Much faster than multiprocess’s own IPC or even pipes.
- Can take system load average into account to decide whether parallelization is worth it at any given time.
Python Multicore is effectively an in-memory queue that is processed by a fixed set of workers. It uses memory mapping to avoid the latency imposed by using a queing system such as celery.
Let’s render 100 users. Always break a large task into smaller tasks, but not too small! If the ranges are too small then tasks aren’t worth the effort because the overhead becomes too much.:
import time from multicore import initialize, shutdown, Task from multicore.utils import ranges # Note the scoping of the "items" variable and the functions items = range(100) def as_string(item): return str(item) def expensive_as_string(item): time.sleep(0.01) return str(item) def multi_expensive_as_string(start, end): return ",".join([expensive_as_string(item) for item in items[start:end]]) if __name__ == "__main__": # Needs to be called only once for lifetime of process initialize() # Example 1: trivial (and slightly pointless) usage task = Task() for i in range(20): task.run(as_string, i) print(", ".join(task.get())) # Example 2: divide job optimally using ranges function task = Task() for start, end in ranges(items): # Note we don't pass items because pickling is expensive and defeats # the purpose of the exercize. task.run(multi_expensive_as_string, start, end) print(", ".join(task.get())) # Stop the multicore workers shutdown()
The Task constructor accepts an optional parameter max_load_average. If the load average for the last minute is larger than a defined threshold then None is returned and your code must cater for the sequential code path. Note that the threshold is specified as for a single core machine, so typically less than one.
The run method accepts an optional parameter serialization_format with value pickle (the default), json or string. Pickle is slow and safe. If you know what type of data you have (you should!) set this as appropriate.
The run method also accepts an optional parameter use_dill with default value False. Dill is a library that can often pickle things that can’t be pickled by the standard pickler but it is slightly slower.
No. The worker pool has a fixed size and can only execute number-of-cores tasks in parallel. You may also set max_load_average as a further guard.
It just has too many issues with eg. Django when it comes to scoping. Even pipes and sockets introduce too much overhead, so memory mapping is used.
No, because this is just an interface, not a collection of parallel code.
In general the code scales nearly linearly if you don’t access the database. Multicore itself adds about 5 milliseconds overhead on my machine.
A future version will address this through dynamic memory map scaling.
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