Re: JIT compilers for Python, what is the latest news?
On 5 April 2013 19:37, Devin Jeanpierre jeanpierr...@gmail.com wrote: On Fri, Apr 5, 2013 at 4:34 AM, John Ladasky john_lada...@sbcglobal.net wrote: On Thursday, April 4, 2013 7:39:16 PM UTC-7, MRAB wrote: Have you looked at Cython? Not quite the same, but still... I'm already using Numpy, compiled with what is supposed to be a fast LAPACK. I don't think I want to attempt to improve on all the work that has gone into Numpy. There's no reason you can't use both cython and numpy. See: http://docs.cython.org/src/tutorial/numpy.html Don't use this. Use memoryviews: http://docs.cython.org/src/userguide/memoryviews.html. I have no idea why that doc page isn't headed DEPRICATED by now. -- http://mail.python.org/mailman/listinfo/python-list
Re: JIT compilers for Python, what is the latest news?
On 5 April 2013 03:29, John Ladasky john_lada...@sbcglobal.net wrote: I'm revisiting a project that I haven't touched in over a year. It was written in Python 2.6, and executed on 32-bit Ubuntu 10.10. I experienced a 20% performance increase when I used Psyco, because I had a computationally-intensive routine which occupied most of my CPU cycles, and always received the same data type. (Multiprocessing also helped, and I was using that too.) I have now migrated to a 64-bit Ubuntu 12.10.1, and Python 3.3. I would rather not revert to my older configuration. That being said, it would appear from my initial reading that 1) Psyco is considered obsolete and is no longer maintained, 2) Psyco is being superseded by PyPy, 3) PyPy doesn't support Python 3.x, or 64-bit optimizations. Do I understand all that correctly? I guess I can live with the 20% slower execution, but sometimes my code would run for three solid days... If you're not willing to go far, I've heard really, really good things about Numba. I've not used it, but seriously: http://jakevdp.github.io/blog/2012/08/24/numba-vs-cython/. Also, PyPy is fine for 64 bit, even if it doesn't gain much from it. So going back to 2.7 might give you that 20% back for almost free. It depends how complex the code is, though. -- http://mail.python.org/mailman/listinfo/python-list
Re: JIT compilers for Python, what is the latest news?
On Apr 5, 7:29 am, John Ladasky john_lada...@sbcglobal.net wrote: I guess I can live with the 20% slower execution, but sometimes my code would run for three solid days... Oooff! Do you know where your goal-posts are? ie if your code were redone in (top-class) C or Fortran would it go from 3 days to 2 days or 2 hours? [The 'top-class' qualification is needed because it could also go from 3 days to 5!] -- http://mail.python.org/mailman/listinfo/python-list
Re: JIT compilers for Python, what is the latest news?
On 2013-04-05 09:39, John Ladasky wrote: On Friday, April 5, 2013 1:27:40 AM UTC-7, Chris Angelico wrote: 1) Can you optimize your algorithms? Three days of processing is... a LOT. Neural network training. Yes, it takes a long time. Still, it's not the most tedious code I run. I also do molecular-dynamics simulations with GROMACS, those runs can take over a week! 2) Rewrite some key portions in C, possibly using Cython (as MRAB suggested). And as I replied to MRAB, my limiting code is within Numpy. I've taken care to look for ways that I might have been using Numpy itself inefficiently (and I did find a problem once: fixing it tripled my execution speed). But I would like to think that Numpy itself, since it is already a C extension, should be optimal. Well, Psyco obviously wasn't optimizing numpy. I believe the suggestion is to identify the key parts of the code that Psyco was optimizing to get you the 20% performance increase and port those to Cython. -- Robert Kern I have come to believe that the whole world is an enigma, a harmless enigma that is made terrible by our own mad attempt to interpret it as though it had an underlying truth. -- Umberto Eco -- http://mail.python.org/mailman/listinfo/python-list
Re: JIT compilers for Python, what is the latest news?
Joshua Landau, 06.04.2013 12:27: On 5 April 2013 03:29, John Ladasky wrote: I'm revisiting a project that I haven't touched in over a year. It was written in Python 2.6, and executed on 32-bit Ubuntu 10.10. I experienced a 20% performance increase when I used Psyco, because I had a computationally-intensive routine which occupied most of my CPU cycles, and always received the same data type. (Multiprocessing also helped, and I was using that too.) I have now migrated to a 64-bit Ubuntu 12.10.1, and Python 3.3. I would rather not revert to my older configuration. That being said, it would appear from my initial reading that 1) Psyco is considered obsolete and is no longer maintained, 2) Psyco is being superseded by PyPy, 3) PyPy doesn't support Python 3.x, or 64-bit optimizations. Do I understand all that correctly? I guess I can live with the 20% slower execution, but sometimes my code would run for three solid days... If you're not willing to go far, I've heard really, really good things about Numba. I've not used it, but seriously: http://jakevdp.github.io/blog/2012/08/24/numba-vs-cython/. Also, PyPy is fine for 64 bit, even if it doesn't gain much from it. So going back to 2.7 might give you that 20% back for almost free. It depends how complex the code is, though. I would guess that the main problem is rather that PyPy doesn't support NumPy (it has its own array implementation, but that's about it). John already mentioned that most of the heavy lifting in his code is done by NumPy. Stefan -- http://mail.python.org/mailman/listinfo/python-list
Re: JIT compilers for Python, what is the latest news?
On Fri, Apr 5, 2013 at 1:29 PM, John Ladasky john_lada...@sbcglobal.net wrote: I'm revisiting a project that I haven't touched in over a year. It was written in Python 2.6, and executed on 32-bit Ubuntu 10.10. I experienced a 20% performance increase when I used Psyco, because I had a computationally-intensive routine which occupied most of my CPU cycles, and always received the same data type. (Multiprocessing also helped, and I was using that too.) I guess I can live with the 20% slower execution, but sometimes my code would run for three solid days... Two things to try, in order: 1) Can you optimize your algorithms? Three days of processing is... a LOT. 2) Rewrite some key portions in C, possibly using Cython (as MRAB suggested). You may well find that you don't actually need to make any language-level changes. If there's some critical mathematical function that already exists in C, making use of it might make all the difference you need. ChrisA -- http://mail.python.org/mailman/listinfo/python-list
Re: JIT compilers for Python, what is the latest news?
On Thursday, April 4, 2013 7:39:16 PM UTC-7, MRAB wrote: Have you looked at Cython? Not quite the same, but still... I'm already using Numpy, compiled with what is supposed to be a fast LAPACK. I don't think I want to attempt to improve on all the work that has gone into Numpy. -- http://mail.python.org/mailman/listinfo/python-list
Re: JIT compilers for Python, what is the latest news?
On Friday, April 5, 2013 1:27:40 AM UTC-7, Chris Angelico wrote: 1) Can you optimize your algorithms? Three days of processing is... a LOT. Neural network training. Yes, it takes a long time. Still, it's not the most tedious code I run. I also do molecular-dynamics simulations with GROMACS, those runs can take over a week! 2) Rewrite some key portions in C, possibly using Cython (as MRAB suggested). And as I replied to MRAB, my limiting code is within Numpy. I've taken care to look for ways that I might have been using Numpy itself inefficiently (and I did find a problem once: fixing it tripled my execution speed). But I would like to think that Numpy itself, since it is already a C extension, should be optimal. -- http://mail.python.org/mailman/listinfo/python-list
Re: JIT compilers for Python, what is the latest news?
On Fri, Apr 5, 2013 at 7:39 PM, John Ladasky john_lada...@sbcglobal.net wrote: On Friday, April 5, 2013 1:27:40 AM UTC-7, Chris Angelico wrote: 1) Can you optimize your algorithms? Three days of processing is... a LOT. Neural network training. Yes, it takes a long time. Still, it's not the most tedious code I run. I also do molecular-dynamics simulations with GROMACS, those runs can take over a week! 2) Rewrite some key portions in C, possibly using Cython (as MRAB suggested). And as I replied to MRAB, my limiting code is within Numpy. I've taken care to look for ways that I might have been using Numpy itself inefficiently (and I did find a problem once: fixing it tripled my execution speed). But I would like to think that Numpy itself, since it is already a C extension, should be optimal. Ahh, yeah, that's gonna take a while. Your minimum processing time is likely to remain fairly high. There won't be any stupidly easy improvements to make (like one of my favorite examples from databasing: an overnight job became a three-second run, just by making proper use of a Btrieve file's index). ChrisA -- http://mail.python.org/mailman/listinfo/python-list
Re: JIT compilers for Python, what is the latest news?
Have you looked into numba? I haven't checked to see if it's python 3 compatible. -- http://mail.python.org/mailman/listinfo/python-list
Re: JIT compilers for Python, what is the latest news?
On 05/04/13 03:29, John Ladasky wrote: I'm revisiting a project that I haven't touched in over a year. It was written in Python 2.6, and executed on 32-bit Ubuntu 10.10. I experienced a 20% performance increase when I used Psyco, because I had a computationally-intensive routine which occupied most of my CPU cycles, and always received the same data type. (Multiprocessing also helped, and I was using that too.) I have now migrated to a 64-bit Ubuntu 12.10.1, and Python 3.3. I would rather not revert to my older configuration. That being said, it would appear from my initial reading that 1) Psyco is considered obsolete and is no longer maintained, 2) Psyco is being superseded by PyPy, 3) PyPy doesn't support Python 3.x, or 64-bit optimizations. Do I understand all that correctly? I guess I can live with the 20% slower execution, but sometimes my code would run for three solid days... Pypy is working on porting to python 3. They are accepting donations: http://pypy.org/py3donate.html Regards, Ian F -- http://mail.python.org/mailman/listinfo/python-list
Re: JIT compilers for Python, what is the latest news?
On Fri, Apr 5, 2013 at 2:39 AM, John Ladasky john_lada...@sbcglobal.net wrote: 2) Rewrite some key portions in C, possibly using Cython (as MRAB suggested). And as I replied to MRAB, my limiting code is within Numpy. I've taken care to look for ways that I might have been using Numpy itself inefficiently (and I did find a problem once: fixing it tripled my execution speed). But I would like to think that Numpy itself, since it is already a C extension, should be optimal. That doesn't seem to follow from your original post. Because Numpy is a C extension, its performance would not be improved by psyco at all. The 20% performance increase that you reported must have been a result of the JIT compiling of some Python code, and if you can identify that and rewrite it in C, then you may be able to see the same sort of boost you had from psyco. -- http://mail.python.org/mailman/listinfo/python-list
Re: JIT compilers for Python, what is the latest news?
On Friday, April 5, 2013 10:32:21 AM UTC-7, Ian wrote: That doesn't seem to follow from your original post. Because Numpy is a C extension, its performance would not be improved by psyco at all. What about the fact that Numpy accommodates Python's dynamic typing? You can pass arrays of integers, floats, bytes, or even PyObjects. I don't know exactly how all that is implemented. In my case, I was always passing floats. So what I assumed that psyco was doing for me was compiling a neural network class that always expected floats. -- http://mail.python.org/mailman/listinfo/python-list
Re: JIT compilers for Python, what is the latest news?
On Fri, Apr 5, 2013 at 4:34 AM, John Ladasky john_lada...@sbcglobal.net wrote: On Thursday, April 4, 2013 7:39:16 PM UTC-7, MRAB wrote: Have you looked at Cython? Not quite the same, but still... I'm already using Numpy, compiled with what is supposed to be a fast LAPACK. I don't think I want to attempt to improve on all the work that has gone into Numpy. There's no reason you can't use both cython and numpy. See: http://docs.cython.org/src/tutorial/numpy.html -- Devin -- http://mail.python.org/mailman/listinfo/python-list
Re: JIT compilers for Python, what is the latest news?
On Fri, Apr 5, 2013 at 12:13 PM, John Ladasky john_lada...@sbcglobal.net wrote: On Friday, April 5, 2013 10:32:21 AM UTC-7, Ian wrote: That doesn't seem to follow from your original post. Because Numpy is a C extension, its performance would not be improved by psyco at all. What about the fact that Numpy accommodates Python's dynamic typing? You can pass arrays of integers, floats, bytes, or even PyObjects. I don't know exactly how all that is implemented. I don't know exactly either, but psyco JIT compiles Python, not C. In the PyObject case you might see some benefit if numpy ends up calling back into methods that are implemented in Python. In my case, I was always passing floats. So what I assumed that psyco was doing for me was compiling a neural network class that always expected floats. Right, so if you take that routine and rewrite it as a C function that expects floats and handles them internally as such, I would think that you might see a similar improvement. -- http://mail.python.org/mailman/listinfo/python-list
Re: JIT compilers for Python, what is the latest news?
On 05/04/2013 03:29, John Ladasky wrote: I'm revisiting a project that I haven't touched in over a year. It was written in Python 2.6, and executed on 32-bit Ubuntu 10.10. I experienced a 20% performance increase when I used Psyco, because I had a computationally-intensive routine which occupied most of my CPU cycles, and always received the same data type. (Multiprocessing also helped, and I was using that too.) I have now migrated to a 64-bit Ubuntu 12.10.1, and Python 3.3. I would rather not revert to my older configuration. That being said, it would appear from my initial reading that 1) Psyco is considered obsolete and is no longer maintained, 2) Psyco is being superseded by PyPy, 3) PyPy doesn't support Python 3.x, or 64-bit optimizations. Do I understand all that correctly? I guess I can live with the 20% slower execution, but sometimes my code would run for three solid days... Have you looked at Cython? Not quite the same, but still... -- http://mail.python.org/mailman/listinfo/python-list
