As a clarification to the above, parallelization of Python code across cores is not unique to Twisted; all Python code has this same limitation.
To use multiple cores with Python code, you need multiple Python processes (as has been pointed out). One way to achieve this is to have the multiple processes talking to each other (using some kind of RPC protocol). Another way is to simply spawn some number of subprocesses (and Twisted has good support for running subprocesses). So, for example, if you write a CLI tool that can be told to run "part of your problem" then your parent Twisted process can simply spawn some number of those with appropriate arguments to split up the problem (e.g. give each process 1 / num_cores of the problem). This will incur some startup penalty as each process starts up (especially if you're using PyPy, which you should be if you care about speed) but is way simpler. Obviously, an RPC-style communication system avoids the startup penalty (but can be more complex). -- meejah _______________________________________________ Twisted-Python mailing list Twisted-Python@twistedmatrix.com https://twistedmatrix.com/cgi-bin/mailman/listinfo/twisted-python