2013/6/19 Trent Nelson <tr...@snakebite.org>: > > The new memory API discussions (and PEP) warrant a quick pyparallel > update: a couple of weeks after PyCon, I came up with a solution for > the biggest show-stopper that has been plaguing pyparallel since its > inception: being able to detect the modification of "main thread" > Python objects from within a parallel context. > > For example, `data.append(4)` in the example below will generate an > AssignmentError exception, because data is a main thread object, and > `data.append(4)` gets executed from within a parallel context:: > > data = [ 1, 2, 3 ] > > def work(): > data.append(4) > > async.submit_work(work) > > The solution turned out to be deceptively simple: > > 1. Prior to running parallel threads, lock all "main thread" > memory pages as read-only (via VirtualProtect on Windows, > mprotect on POSIX). > > 2. Detect attempts to write to main thread pages during parallel > thread execution (via SEH on Windows or a SIGSEGV trap on POSIX), > and raise an exception instead (detection is done in the ceval > frame exec loop).
Quick stupid question: because of refcounts, the pages will be written to even in case of read-only access. How do you deal with this? cf _______________________________________________ Python-Dev mailing list Python-Dev@python.org http://mail.python.org/mailman/listinfo/python-dev Unsubscribe: http://mail.python.org/mailman/options/python-dev/archive%40mail-archive.com
