> I think you misunderstood codedivine. His point was not that applications that use STM and make use of a single thread are slower than sequential implementations of the same application.
And the experience has been exactly the same in locks-based GIL-removal tests in Python: David Beazley recently "unearthed" and tested a GIL-removal patch from the 1.4 days[0]...
> To test threads, I wrote a small sample that subdivided the work across two worker threads is an embarrassingly parallel manner (note: this code is a little wonky due to the fact that Python-1.4 doesn't implement thread joining--meaning that you have to do it yourself with the included binary-semaphore lock).
> [...]
> If you run this code with the GIL, the execution time is about 2.5 seconds or approximately 1.3 times slower than the single-threaded version (1.9 seconds). Using the GIL-less Python, the execution time is 18.5 seconds or approximately 1.45 times slower than the single-threaded version (12.7 seconds). Just to emphasize, the GIL-less Python running with two-threads is running more than 7 times slower than the version with a GIL.
The main difference being that we have plenty of examples of lock-based code scaling well. Even large, non-trivial systems, such as the Linux kernel. It used to rely solely on the BKL - big kernel lock - for synchronization, but they have moved to finer grained, data structure level locking.
That makes me think that an implementation of Python that uses fine-grained (or at least finer grained) locks that scales is possible. It's just a question of how feasible is it to transform CPython into such an implementation. Beazley's experiment indicates that the changes may have to be fundamental, such as moving away from reference counting.
And the experience has been exactly the same in locks-based GIL-removal tests in Python: David Beazley recently "unearthed" and tested a GIL-removal patch from the 1.4 days[0]...
> To test threads, I wrote a small sample that subdivided the work across two worker threads is an embarrassingly parallel manner (note: this code is a little wonky due to the fact that Python-1.4 doesn't implement thread joining--meaning that you have to do it yourself with the included binary-semaphore lock).
> [...]
> If you run this code with the GIL, the execution time is about 2.5 seconds or approximately 1.3 times slower than the single-threaded version (1.9 seconds). Using the GIL-less Python, the execution time is 18.5 seconds or approximately 1.45 times slower than the single-threaded version (12.7 seconds). Just to emphasize, the GIL-less Python running with two-threads is running more than 7 times slower than the version with a GIL.
[0] http://dabeaz.blogspot.com/2011/08/inside-look-at-gil-remova...