Re: [Tutor] BLAS Implementation on Python
Thank you all for your replies. I wonder why I din't get any updates from the mailing list. Now I feel I have more reasons to proceed with the implementation. Only that I don't know where to start :) Thanks, Mahesh Narayanamurthi ___ Tutor maillist - Tutor@python.org To unsubscribe or change subscription options: http://mail.python.org/mailman/listinfo/tutor
Re: [Tutor] BLAS Implementation on Python
Knacktus knack...@googlemail.com wrote He doesn't have to write it, as it is very obvious, that no Python code on earth (even written by Guido himself ;-)) stands a chance compared to Fortran or C. Look at this: There is one big proviso. The C or Fortran needs to be well written in the first place. It's quite possible to write code in Python that outperforms badly written C. And it is quite easy to write bad C! This is similar to the arguments in the 1960's/70's when languages like C first appeared. The assembler programmers all said you'd never replace assembler with C because assembler was an order of magnitude faster (and smaller because size mattered then!). But that was only true of well optimised assembler. Nowadays speed is hardly ever used as a reason for using assembler. Now the case between interpreted and compiled languages is slightly different, but as python compilers emerge the gap will continue to narrow. Will Python ever challenge C/assembler? Probably not in the low level domain but where we are talking about implementing complex algorithms it may well do. But its a long way off just now and if you need speed selectively rewriting in C is still the best bet. The skill is in selecting the bits to optimise. But a pure Python implementation will benefit from any improvements that are made going forward without significant rework. HTH, -- Alan Gauld Author of the Learn to Program web site http://www.alan-g.me.uk/ ___ Tutor maillist - Tutor@python.org To unsubscribe or change subscription options: http://mail.python.org/mailman/listinfo/tutor
Re: [Tutor] BLAS Implementation on Python
Alan Gauld, 08.03.2011 09:51: Knacktus wrote He doesn't have to write it, as it is very obvious, that no Python code on earth (even written by Guido himself ;-)) stands a chance compared to Fortran or C. Look at this: There is one big proviso. The C or Fortran needs to be well written in the first place. It's quite possible to write code in Python that outperforms badly written C. And it is quite easy to write bad C! It's seriously hard to write computational Python code that is faster than C code, though. It shifts a bit if you can take advantage of Python's dynamic container types, especially dicts, but that's rarely the case for mathematic computation code, which would usually deploy NumPy etc. in Python. Writing that in pure Python is bound to be incredibly slow, and likely still several hundred times slower than even a simple approach in C. There's a reason people use NumPy, C, Fortran and Cython for these things. Remember that the OP's topic was a BLAS implementation. The BLAS libraries are incredibly well optimised for all sorts of platforms, including GPUs. They are building blocks that C programmers can basically just plug into their code to run hugely fast computations. There is no way Python code will ever be able to get any close to that. Stefan ___ Tutor maillist - Tutor@python.org To unsubscribe or change subscription options: http://mail.python.org/mailman/listinfo/tutor
Re: [Tutor] BLAS Implementation on Python
Stefan Behnel stefan...@behnel.de wrote He doesn't have to write it, as it is very obvious, that no Python code on earth (even written by Guido himself ;-)) stands a chance compared to Fortran or C. Look at this: There is one big proviso. The C or Fortran needs to be well written It's seriously hard to write computational Python code that is faster than C code, though. Agreed, it was the assertion that no Python code on earth...stands a chance... that I was objecting to. There are many areas where the C code would have to be pathologically bad to be beaten by Python, but there are plenty of places where only slightly inefficient C code can be competitive relatie to Python. Alan G. ___ Tutor maillist - Tutor@python.org To unsubscribe or change subscription options: http://mail.python.org/mailman/listinfo/tutor
Re: [Tutor] BLAS Implementation on Python
Am 08.03.2011 20:00, schrieb Alan Gauld: Stefan Behnel stefan...@behnel.de wrote He doesn't have to write it, as it is very obvious, that no Python code on earth (even written by Guido himself ;-)) stands a chance compared to Fortran or C. Look at this: There is one big proviso. The C or Fortran needs to be well written It's seriously hard to write computational Python code that is faster than C code, though. Agreed, it was the assertion that no Python code on earth...stands a chance... that I was objecting to. There are many areas where the C code would have to be pathologically bad to be beaten by Python, but there are plenty of places where only slightly inefficient C code can be competitive relatie to Python. Maybe there're those areas, but the top posters question is about reimplementing BLAS, which is a highly optimized package for linear algebra. And in this area no pure Python code on earth on any currently available Python implementation stands a chance performance wise to one of the C or Fortran implementations. Alan G. ___ Tutor maillist - Tutor@python.org To unsubscribe or change subscription options: http://mail.python.org/mailman/listinfo/tutor ___ Tutor maillist - Tutor@python.org To unsubscribe or change subscription options: http://mail.python.org/mailman/listinfo/tutor
Re: [Tutor] BLAS Implementation on Python
Mahesh Narayanamurthi, 07.03.2011 01:50: I am thinking of implementing a BLAS package in pure python. I am wondering if this is a good idea. My design goals are: [1] Efficient manipulation of Matrices and Vectors using pure python objects and python code. [2] Targetted to run on Python3 [3] Extensive use of defensive programming style [4] To serve as a reference design for future High Performance Code in Python [5] To serve as a reference material in classroom courses on numerical computing or for hobbyist programmers First question that comes to my mind: who would use this? The available packages are commonly not written in Python, but they are fast, which is why they are being used by Python programs. A quick web search brought up Tokyo, for example, which wraps BLAS in Cython: https://github.com/tokyo/tokyo I can see that it would be easier to teach the concepts based on code written in Python than based on the existing implementations. Even if there likely won't be a real use case, it may still be nice to have a pure Python implementation available, if it could serve as a drop-in replacement for faster implementations. I.e., you could present the inner workings based on the Python code, and then switch to a 'real' implementation for the actual computations. However, even if you say efficient above (and the algorithms may well be efficient), don't expect it to be fast. Python is great for orchestrating high performance computations. It's less great for doing them in plain Python code. Stefan ___ Tutor maillist - Tutor@python.org To unsubscribe or change subscription options: http://mail.python.org/mailman/listinfo/tutor
Re: [Tutor] BLAS Implementation on Python
Am 07.03.2011 01:50, schrieb Mahesh Narayanamurthi: Hello, I am thinking of implementing a BLAS package in pure python. I am wondering if this is a good idea. I don't think so. Usually people write extensions to the CPython implementation (when talking about performance, we need to talk about Python implementations like CPython, Jython or PyPy) in C to do high performance stuff. Pure CPython is (depeneding on the problem) magnitudes slower than C. Also, there's NumPy SciPy. Check those out. More comments below ... My design goals are: [1] Efficient manipulation of Matrices and Vectors using pure python objects and python code. No, not efficient in terms of performance. [2] Targetted to run on Python3 Good idea. NumPy and SciPy will be (are already?) ported. [3] Extensive use of defensive programming style [4] To serve as a reference design for future High Performance Code in Python The future of High Performance Python is probably PyPy. [5] To serve as a reference material in classroom courses on numerical computing or for hobbyist programmers Good idea, as Python is clear and nice to read. Thanks, Mahesh Narayanamurthi ___ Tutor maillist - Tutor@python.org To unsubscribe or change subscription options: http://mail.python.org/mailman/listinfo/tutor ___ Tutor maillist - Tutor@python.org To unsubscribe or change subscription options: http://mail.python.org/mailman/listinfo/tutor
Re: [Tutor] BLAS Implementation on Python
Knacktus, 07.03.2011 14:28: Am 07.03.2011 01:50, schrieb Mahesh Narayanamurthi: Hello, I am thinking of implementing a BLAS package in pure python. I am wondering if this is a good idea. My design goals are: [2] Targetted to run on Python3 Good idea. NumPy and SciPy will be (are already?) ported. Yes, NumPy has been ported as of version 1.5. Not sure about the overall status of SciPy, their FAQ isn't up to date. [4] To serve as a reference design for future High Performance Code in Python The future of High Performance Python is probably PyPy. PyPy has been the future ever since they started ;-). They have gotten faster, but it's is still far from being suitable for any kind of numerical high performance computing. That will continue to be the domain of C, Fortran and Cython for a while, and potentially including LuaJIT2 for certain use cases (usable from Python through Lupa, BTW). Stefan ___ Tutor maillist - Tutor@python.org To unsubscribe or change subscription options: http://mail.python.org/mailman/listinfo/tutor
Re: [Tutor] BLAS Implementation on Python
On Tue, 8 Mar 2011 12:28:56 am Knacktus wrote: Am 07.03.2011 01:50, schrieb Mahesh Narayanamurthi: Hello, I am thinking of implementing a BLAS package in pure python. I am wondering if this is a good idea. Sure, why not? Even if nobody uses it, it looks good on a resume and you will hopefully learn a lot. I don't think so. Usually people write extensions to the CPython implementation (when talking about performance, we need to talk about Python implementations like CPython, Jython or PyPy) in C to do high performance stuff. Pure CPython is (depeneding on the problem) magnitudes slower than C. Typical pure Python code is between 10 and 100 times slower than C, but talking about implementations, it is the aim of the PyPy project to use Just In Time compilation and clever optimizations to meet *and exceed* the speed of static C compilers. In just a few years, with a handful of people working on it, and a tiny grant from the EU, they have already doubled the speed of CPython. If PyPy could get *half* the love and attention that CPython gets, who knows what they could accomplish? (If only Google had thrown some work into PyPy, instead of wasting time with Unladen Swallow that never went anywhere useful...) But the beauty of Python is that if a piece of code is too slow, you can rip it out into an extension written in C or Fortran while keeping the rest of the library in Python. See, for example, the re module, which has a front end written in Python and the regex engine in C. The itertools module is another good example. Using a few primitives written in C, itertools allows Python code to run efficiently and quickly. Also, there's NumPy SciPy. Check those out. More comments below ... My design goals are: [1] Efficient manipulation of Matrices and Vectors using pure python objects and python code. No, not efficient in terms of performance. How do you know how efficient his code will be before he's even written it? [...] The future of High Performance Python is probably PyPy. Exactly why a pure-Python library *may* be useful. Today it will be slow, running under CPython, but in five (unlikely) or ten years (possibly), it may be fast, running under PyPy. Or not. Who can tell what the future will bring? -- Steven D'Aprano ___ Tutor maillist - Tutor@python.org To unsubscribe or change subscription options: http://mail.python.org/mailman/listinfo/tutor
Re: [Tutor] BLAS Implementation on Python
Am 07.03.2011 22:44, schrieb Steven D'Aprano: On Tue, 8 Mar 2011 12:28:56 am Knacktus wrote: Am 07.03.2011 01:50, schrieb Mahesh Narayanamurthi: Hello, I am thinking of implementing a BLAS package in pure python. I am wondering if this is a good idea. Sure, why not? Even if nobody uses it, it looks good on a resume and you will hopefully learn a lot. I don't think so. Usually people write extensions to the CPython implementation (when talking about performance, we need to talk about Python implementations like CPython, Jython or PyPy) in C to do high performance stuff. Pure CPython is (depeneding on the problem) magnitudes slower than C. Typical pure Python code is between 10 and 100 times slower than C, but talking about implementations, it is the aim of the PyPy project to use Just In Time compilation and clever optimizations to meet *and exceed* the speed of static C compilers. In just a few years, with a handful of people working on it, and a tiny grant from the EU, they have already doubled the speed of CPython. If PyPy could get *half* the love and attention that CPython gets, who knows what they could accomplish? (If only Google had thrown some work into PyPy, instead of wasting time with Unladen Swallow that never went anywhere useful...) But the beauty of Python is that if a piece of code is too slow, you can rip it out into an extension written in C or Fortran while keeping the rest of the library in Python. See, for example, the re module, which has a front end written in Python and the regex engine in C. The itertools module is another good example. Using a few primitives written in C, itertools allows Python code to run efficiently and quickly. Also, there's NumPy SciPy. Check those out. More comments below ... My design goals are: [1] Efficient manipulation of Matrices and Vectors using pure python objects and python code. No, not efficient in terms of performance. How do you know how efficient his code will be before he's even written it? He doesn't have to write it, as it is very obvious, that no Python code on earth (even written by Guido himself ;-)) stands a chance compared to Fortran or C. Look at this: http://shootout.alioth.debian.org/u32/performance.php?test=spectralnorm CPython implementation runs 12 mins compared to 8 secs of a Fortran implementation. PyPy is about 100 secs, which is remarkable but still far off. [...] The future of High Performance Python is probably PyPy. Exactly why a pure-Python library *may* be useful. Today it will be slow, running under CPython, but in five (unlikely) or ten years (possibly), it may be fast, running under PyPy. Or not. Who can tell what the future will bring? ___ Tutor maillist - Tutor@python.org To unsubscribe or change subscription options: http://mail.python.org/mailman/listinfo/tutor
