[sage-devel] Re: python vs java for scientific computing
Em Sex, 2009-01-23 às 09:49 -0800, rjf escreveu: On Jan 22, 6:26 pm, mhampton hampto...@gmail.com wrote: ... A python/ cython/javascript spreadsheet for the Sage notebook would be great - unfortunately I'm not going to write one. I think it would be hard to do it right. -M. Hampton I have heard that the major competition for Mathematica is not Maple, but Matlab. And that the major competition for Matlab is not Mathematica, but Excel. There are spreadsheet interfaces in Maple. One is so you can use a spreadsheet paradigm within Maple. Implementing a simple spreadsheet in lisp or Maple is not too hard; I assume the same holds for python. I wrote a paper with an undergraduate showing how to call Lisp (and therefore Maxima) from Microsoft's Excel. The range data is transferred to and from Lisp in a standard form. Symbolic data must be strings. We also add to the spreadsheet, commands for lisp evaluation, and lambda expressions. Whether this is the right design or not, it is fairly obvious, and for someone who like Excel (+ Lisp) allows pretty much full access.. Of course Excel can be used generally to allow access to all those numeric libraries in a way which may be comparable in convenience to any of your favorite languages. I suspect that one could mimmick all the scripting of python with an extremely thin layer of python between Excel and whatever, making those libraries appear to be available in Excel directly. Just as I can run MPFR, GMP, from Excel using a very thin layer of Lisp. We found the key was to write a very very thin layer of visual basic.to get to Lisp. Probably VB totally, could link to numerics without lisp or python... There are ways to integrate Python and Excel. I've never seen Excel call python, but surely I've seen Python use Excel. Last month I tried for the first time using comtypes... I've played a little and know that one can even use Excel's text-to-speech features :) Also, one has direct access to cells' contents. By the way, even though I don't like Excel for any number crunching, there are even books around which address the subject of using it for scientific stuff. Hell, I've even seen someone use Excel in some twisted way to make semi-automatic macros for AutoCAD! From what I've seen, those who have tasted R have switched with no regrets (though I've never been one of those, but at least I use numpy). Ronan --~--~-~--~~~---~--~~ To post to this group, send email to sage-devel@googlegroups.com To unsubscribe from this group, send email to sage-devel-unsubscr...@googlegroups.com For more options, visit this group at http://groups.google.com/group/sage-devel URLs: http://www.sagemath.org -~--~~~~--~~--~--~---
[sage-devel] Re: python vs java for scientific computing
I wrote a paper with an undergraduate showing how to call Lisp (and therefore Maxima) from Microsoft's Excel. The range data is transferred to and from Lisp in a standard form. Symbolic data must be strings. We also add to the spreadsheet, commands for lisp evaluation, and lambda expressions. Wow Richard, that's really neat! I'd bet you could write a Google app, and tie Lisp into the Google Documents spreadsheet widget. Upload Maxima into the cloud, and you'll have, with out a doubt, quite the superlative computer algebra system! I'd love to see the Sage notebook on a system as large as Google's network, but I doubt that could happen given the complexity. I'd be curious to see what you and your students could come up with, --~--~-~--~~~---~--~~ To post to this group, send email to sage-devel@googlegroups.com To unsubscribe from this group, send email to sage-devel-unsubscr...@googlegroups.com For more options, visit this group at http://groups.google.com/group/sage-devel URLs: http://www.sagemath.org -~--~~~~--~~--~--~---
[sage-devel] Re: python vs java for scientific computing
What about the compiled java and python benchmarks? Is there someone who has experience with compiling a java code to native code on an operating system? On Fri, Jan 23, 2009 at 4:26 AM, mhampton hampto...@gmail.com wrote: On Jan 22, 4:35 pm, Robert Bradshaw rober...@math.washington.edu wrote: I pity those who find themselves trying to do serious scientific programming with excel :). Though there are certainly a lot of rational arguments against it, there may be some out there that are as enthusiastic about Excel as we are about Python or you are about lisp. - Robert For scientific computing, of course we can laugh at Excel. But there are many analysis tasks it is good at, and for which it is _extremely_ popular. One could win a lot of people over with a spreadsheet that was well-integrated with other tools in some language. A python/ cython/javascript spreadsheet for the Sage notebook would be great - unfortunately I'm not going to write one. I think it would be hard to do it right. -M. Hampton --~--~-~--~~~---~--~~ To post to this group, send email to sage-devel@googlegroups.com To unsubscribe from this group, send email to sage-devel-unsubscr...@googlegroups.com For more options, visit this group at http://groups.google.com/group/sage-devel URLs: http://www.sagemath.org -~--~~~~--~~--~--~---
[sage-devel] Re: python vs java for scientific computing
On Jan 23, 10:09 am, ahmet alper parker aapar...@gmail.com wrote: Is there someone who has experience with compiling a java code to native code on an operating system? the standard java virtual machine by sun already compiles java bytecode to native machine code. this is called hotspot compiler and picks those parts at runtime, that are very often used and translates them. This goes even so far, that long running loops are replaced while they are still running - called on stack replacement. If you have a big machine (multicore, = 2 gb ram) then you can run java in the server configuration for additional optimizations. http://java.sun.com/javase/technologies/hotspot/index.jsp http://java.sun.com/javase/6/docs/technotes/guides/vm/server-class.html also notice, that this approach goes some steps further in recent java versions (6 and soon 7), where the sourcecode is analyzed and rewritten to improve performance (things like dynamic inlining, lock elusion, escape analysis ... http://java.sun.com/javase/technologies/hotspot/publications/ ) http://java.sun.com/performance/reference/whitepapers/6_performance.html From my experience, all other options are less stable and tested. Of course, you still have all checks (i.e. there is nothing like in cython where you disable checks) and the virtual machine is also still used (but therefore you get garbage collection with a variaty of different collectors and options) For everything else you should consider JNI (that's the mechanism to call native C code and how all basic java language features are implemented) h --~--~-~--~~~---~--~~ To post to this group, send email to sage-devel@googlegroups.com To unsubscribe from this group, send email to sage-devel-unsubscr...@googlegroups.com For more options, visit this group at http://groups.google.com/group/sage-devel URLs: http://www.sagemath.org -~--~~~~--~~--~--~---
[sage-devel] Re: python vs java for scientific computing
On Fri, Jan 23, 2009 at 5:37 AM, Harald Schilly harald.schi...@gmail.com wrote: For everything else you should consider JNI (that's the mechanism to call native C code and how all basic java language features are implemented) By the way you may consider using JNA to access native libraries. I have used it to access the Win32 api and it works very well. https://jna.dev.java.net/ JNA provides Java programs easy access to native shared libraries (DLLs on Windows) without writing anything but Java code—no JNI or native code is required. This functionality is comparable to Windows' Platform/Invoke and Python's ctypes. Access is dynamic at runtime without code generation --~--~-~--~~~---~--~~ To post to this group, send email to sage-devel@googlegroups.com To unsubscribe from this group, send email to sage-devel-unsubscr...@googlegroups.com For more options, visit this group at http://groups.google.com/group/sage-devel URLs: http://www.sagemath.org -~--~~~~--~~--~--~---
[sage-devel] Re: python vs java for scientific computing
On Jan 23, 3:47 pm, Alfredo Portes doyenatc...@gmail.com wrote: By the way you may consider using JNA to access native libraries. thx, didn't know about that! --~--~-~--~~~---~--~~ To post to this group, send email to sage-devel@googlegroups.com To unsubscribe from this group, send email to sage-devel-unsubscr...@googlegroups.com For more options, visit this group at http://groups.google.com/group/sage-devel URLs: http://www.sagemath.org -~--~~~~--~~--~--~---
[sage-devel] Re: python vs java for scientific computing
On Jan 22, 6:26 pm, mhampton hampto...@gmail.com wrote: ... A python/ cython/javascript spreadsheet for the Sage notebook would be great - unfortunately I'm not going to write one. I think it would be hard to do it right. -M. Hampton I have heard that the major competition for Mathematica is not Maple, but Matlab. And that the major competition for Matlab is not Mathematica, but Excel. There are spreadsheet interfaces in Maple. One is so you can use a spreadsheet paradigm within Maple. Implementing a simple spreadsheet in lisp or Maple is not too hard; I assume the same holds for python. I wrote a paper with an undergraduate showing how to call Lisp (and therefore Maxima) from Microsoft's Excel. The range data is transferred to and from Lisp in a standard form. Symbolic data must be strings. We also add to the spreadsheet, commands for lisp evaluation, and lambda expressions. Whether this is the right design or not, it is fairly obvious, and for someone who like Excel (+ Lisp) allows pretty much full access.. Of course Excel can be used generally to allow access to all those numeric libraries in a way which may be comparable in convenience to any of your favorite languages. I suspect that one could mimmick all the scripting of python with an extremely thin layer of python between Excel and whatever, making those libraries appear to be available in Excel directly. Just as I can run MPFR, GMP, from Excel using a very thin layer of Lisp. We found the key was to write a very very thin layer of visual basic.to get to Lisp. Probably VB totally, could link to numerics without lisp or python... --~--~-~--~~~---~--~~ To post to this group, send email to sage-devel@googlegroups.com To unsubscribe from this group, send email to sage-devel-unsubscr...@googlegroups.com For more options, visit this group at http://groups.google.com/group/sage-devel URLs: http://www.sagemath.org -~--~~~~--~~--~--~---
[sage-devel] Re: python vs java for scientific computing
On Jan 21, 10:25 pm, Robert Bradshaw rober...@math.washington.edu
wrote:
On Jan 21, 2009, at 3:34 PM, rjf wrote:
On Jan 21, 11:54 am, Robert Bradshaw rober...@math.washington.edu
... snip...
In our case, linear
algebra is done via NumPy, which in turn uses a BLAS (with Sage we
ship ATLAS). On the other hand, if one is doing a problem that isn't
easily phrased as a linear algebra/differential equations/etc.
Sure, calling a library program from Python can be a good way to
achieve efficiency, but that is not an argument that Python is good
for scientific computing. It is an argument that it is good for
scripting, or glue or something along those lines.
question for which there is already an optimized library/code, that's
where Cython comes in, where one can compile code that operates on C
doubles directly (and, because it compiles down to C, is just as
efficient).
This is unconvincing. Maxima code can be translated to Lisp (by a
command called translate).
The Lisp (if Maxima is running in GCL) can be compiled (by a command
called compile), into C
and then into assembler/ binary code. So by your argument, Maxima is
as efficient as C.
(Lisps that are not GCL compile into assembler, so maybe writing in
Lisp is as efficient as assembler??)
It also fits in with the 90-10 philosophy, making it easy
to optimize only those parts one needs to (instead of writing the
whole thing in a more restrictive (depending on your tastes) language
just so your couple of inner loops can be fast enough).
I agree. Which is why I use Lisp:
1. This 90-10 philosophy includes a need for careful profiling to find
out where the important parts are.
At least the lisp I use has superb profiling tools.
2. Optimizing code in Lisp generally can be done by increasing the
details of the (otherwise optional) declarations,
increasing the requested level of optimizing, thereby removing error
checks, etc.
3. Changing a huge system (e.g. Maxima) to use a different version of
function F, which can be interpreted or compiled,
is accomplished by evaluating what can be a single line of code, and
done in a very short time, independent of the size
of the system.
I think Java cannot do this. I have never used Cython.
Getting back to the topic of the thread, I think a reason Python is
nice for scientific programming is that it's easy to learn, easy to
read, and easy to prototype in, and has a rich set of libraries to
work with.
If the only comparison is with Java, who knows. I understand that
Excel is nice, too.
On the other hand, when you need raw number crunching
speed there are good tools out there to interface with or write at a
lower level (e.g. Cython, or all that's been wrapped by SciPy and Sage).
As I pointed out, this not an argument for Python being good for
scientific computing,
but for Python being good for glue. If you wish, you can see how to
glue MPFR or
GMP or quad-double arithmetic to lisp, google{fateman generic.lisp}
Regards
RJF
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[sage-devel] Re: python vs java for scientific computing
On Jan 22, 2009, at 11:11 AM, rjf wrote: On Jan 21, 10:25 pm, Robert Bradshaw rober...@math.washington.edu wrote: On Jan 21, 2009, at 3:34 PM, rjf wrote: On Jan 21, 11:54 am, Robert Bradshaw rober...@math.washington.edu ... snip... In our case, linear algebra is done via NumPy, which in turn uses a BLAS (with Sage we ship ATLAS). On the other hand, if one is doing a problem that isn't easily phrased as a linear algebra/differential equations/etc. Sure, calling a library program from Python can be a good way to achieve efficiency, but that is not an argument that Python is good for scientific computing. It is an argument that it is good for scripting, or glue or something along those lines. Yes. Given the large number of highly optimized, debugged, and widely used scientific libraries already out there, being a good scripting/ glue language is a big plus for doing scientific computing. Perhaps that's not as valuable to you. question for which there is already an optimized library/code, that's where Cython comes in, where one can compile code that operates on C doubles directly (and, because it compiles down to C, is just as efficient). This is unconvincing. Maxima code can be translated to Lisp (by a command called translate). The Lisp (if Maxima is running in GCL) can be compiled (by a command called compile), into C and then into assembler/ binary code. So by your argument, Maxima is as efficient as C. (Lisps that are not GCL compile into assembler, so maybe writing in Lisp is as efficient as assembler??) To complete this argument, one would have to look at how efficient the auto-generated C code is compared to a direct implementation of the algorithm in C, in other words, how much overhead is incurred by the translation. IMHO Cython can do a good job at this, but I'm of course an unbiased judge on that point. Just as you indicate below with lisp, additional declarations result in tighter code. It also fits in with the 90-10 philosophy, making it easy to optimize only those parts one needs to (instead of writing the whole thing in a more restrictive (depending on your tastes) language just so your couple of inner loops can be fast enough). I agree. Which is why I use Lisp: 1. This 90-10 philosophy includes a need for careful profiling to find out where the important parts are. At least the lisp I use has superb profiling tools. 2. Optimizing code in Lisp generally can be done by increasing the details of the (otherwise optional) declarations, increasing the requested level of optimizing, thereby removing error checks, etc. 3. Changing a huge system (e.g. Maxima) to use a different version of function F, which can be interpreted or compiled, is accomplished by evaluating what can be a single line of code, and done in a very short time, independent of the size of the system. I think Java cannot do this. I have never used Cython. Python can do this, though the more one compiles the more gets statically bound. Getting back to the topic of the thread, I think a reason Python is nice for scientific programming is that it's easy to learn, easy to read, and easy to prototype in, and has a rich set of libraries to work with. If the only comparison is with Java, who knows. I understand that Excel is nice, too. I pity those who find themselves trying to do serious scientific programming with excel :). Though there are certainly a lot of rational arguments against it, there may be some out there that are as enthusiastic about Excel as we are about Python or you are about lisp. - Robert --~--~-~--~~~---~--~~ To post to this group, send email to sage-devel@googlegroups.com To unsubscribe from this group, send email to sage-devel-unsubscr...@googlegroups.com For more options, visit this group at http://groups.google.com/group/sage-devel URLs: http://www.sagemath.org -~--~~~~--~~--~--~---
[sage-devel] Re: python vs java for scientific computing
On Jan 22, 4:35 pm, Robert Bradshaw rober...@math.washington.edu wrote: I pity those who find themselves trying to do serious scientific programming with excel :). Though there are certainly a lot of rational arguments against it, there may be some out there that are as enthusiastic about Excel as we are about Python or you are about lisp. - Robert For scientific computing, of course we can laugh at Excel. But there are many analysis tasks it is good at, and for which it is _extremely_ popular. One could win a lot of people over with a spreadsheet that was well-integrated with other tools in some language. A python/ cython/javascript spreadsheet for the Sage notebook would be great - unfortunately I'm not going to write one. I think it would be hard to do it right. -M. Hampton --~--~-~--~~~---~--~~ To post to this group, send email to sage-devel@googlegroups.com To unsubscribe from this group, send email to sage-devel-unsubscr...@googlegroups.com For more options, visit this group at http://groups.google.com/group/sage-devel URLs: http://www.sagemath.org -~--~~~~--~~--~--~---
[sage-devel] Re: python vs java for scientific computing
On Jan 21, 6:21 am, rjf fate...@gmail.com wrote: In my experience, people doing scientific calculations for a living will not tolerate a language implementation X whose programs are substantially slower than equivalent ones in a language implementation Y. I don't know any details, but I think there are valid cases where one wants calculation to act the same on all platforms. One possible example are spacecrafts, where the calculations in space should act the same way as they were developed on ground. This is part of robust calculations, interval analysis and enclosures, and much more like this. So, I think the developer/user has to think about the actual goals, speed vs. reproducibility, and if possible control what happens. Also, since you can call C for a function from java or python pretty easily, you can always gain more control about the behaviour. h --~--~-~--~~~---~--~~ To post to this group, send email to sage-devel@googlegroups.com To unsubscribe from this group, send email to sage-devel-unsubscr...@googlegroups.com For more options, visit this group at http://groups.google.com/group/sage-devel URLs: http://www.sagemath.org -~--~~~~--~~--~--~---
[sage-devel] Re: python vs java for scientific computing
On Jan 21, 2:44 am, Harald Schilly harald.schi...@gmail.com wrote: On Jan 21, 6:21 am, rjf fate...@gmail.com wrote: In my experience, people doing scientific calculations for a living will not tolerate a language implementation X whose programs are substantially slower than equivalent ones in a language implementation Y. I don't know any details, but I think there are valid cases where one wants calculation to act the same on all platforms. Sure. One possible example are spacecrafts, where the calculations in space should act the same way as they were developed on ground. Sure. I can't imagine any engineer debugging a life-critical system on a Mac and then running it on a PC, putting one of them on a spacecraft and leaving one on the ground. Surely your supposition that this is a justification for anything has no support in reality. This is part of robust calculations, interval analysis and enclosures, and much more like this. The reproducibility of results on one computer with one language, but at different times, is already hard enough. What are you willing to sacrifice for your concept of robustness? Will you, for example, freeze the version of the operating system forever? That would actually be a really good move for some applications, including spacecraft. So, I think the developer/user has to think about the actual goals, speed vs. reproducibility, and if possible control what happens. I am sure that some Sage people have thought about such things, but probably not enough. Which is why I try to poke holes in some of these comments! Also, since you can call C for a function from java or python pretty easily, you can always gain more control about the behaviour. C does not run the same on every computer, and C does not provide handles for every feature of every computer in a uniform fashion. You can (I think) also call assembler from Java or Python, and that might be preferable. Of course the assembler would depend on the machine more obviously than the C depends on the machine. It would be much simpler for Sage if only one computer and operating system were supported. But the topic of this thread is supposed to be something about comparing 2 languages for scientific computing. Whatever that might be. R. h --~--~-~--~~~---~--~~ To post to this group, send email to sage-devel@googlegroups.com To unsubscribe from this group, send email to sage-devel-unsubscr...@googlegroups.com For more options, visit this group at http://groups.google.com/group/sage-devel URLs: http://www.sagemath.org -~--~~~~--~~--~--~---
[sage-devel] Re: python vs java for scientific computing
On Jan 21, 11:54 am, Robert Bradshaw rober...@math.washington.edu wrote: I am sure that some Sage people have thought about such things, but probably not enough. Which is why I try to poke holes in some of these comments! Sage has thought about this--we have models for both: RDF -- The real double field with provides wrappers around the machine's native (double) floating point arithmetic, fast but possibly machine-dependent RealField(n) -- n-bit mentissa real numbers, based on mpfr. Slower, but every operation follows strict and reproducible rounding rules. There is also support for real interval fields, and the (now deprecated for reasons very similar to the start of topic of this thread) quad-double model, and even real lazy fields whose entries are computed on the fly to whatever precision is needed. RDF, which appears to be a slow version of machine arithmetic, presumably even slower when accessed through whatever layers python has, is the competition for Java, which in this case would not be strictfp. Neither seems likely to be very favorable for traditional scientific computing, but perhaps you can provide an example with some timing? A comparison with the same program/hardware in C might be helpful too. - Robert --~--~-~--~~~---~--~~ To post to this group, send email to sage-devel@googlegroups.com To unsubscribe from this group, send email to sage-devel-unsubscr...@googlegroups.com For more options, visit this group at http://groups.google.com/group/sage-devel URLs: http://www.sagemath.org -~--~~~~--~~--~--~---
[sage-devel] Re: python vs java for scientific computing
On Jan 21, 2009, at 3:34 PM, rjf wrote: On Jan 21, 11:54 am, Robert Bradshaw rober...@math.washington.edu wrote: I am sure that some Sage people have thought about such things, but probably not enough. Which is why I try to poke holes in some of these comments! Sage has thought about this--we have models for both: RDF -- The real double field with provides wrappers around the machine's native (double) floating point arithmetic, fast but possibly machine-dependent RealField(n) -- n-bit mentissa real numbers, based on mpfr. Slower, but every operation follows strict and reproducible rounding rules. There is also support for real interval fields, and the (now deprecated for reasons very similar to the start of topic of this thread) quad-double model, and even real lazy fields whose entries are computed on the fly to whatever precision is needed. RDF, which appears to be a slow version of machine arithmetic, Not sure what you mean here, unless you're comparing to 32-bit C floats. presumably even slower when accessed through whatever layers python has, is the competition for Java, which in this case would not be strictfp. Neither seems likely to be very favorable for traditional scientific computing, but perhaps you can provide an example with some timing? A comparison with the same program/hardware in C might be helpful too. Yes, manipulating individual C doubles wrapped as Python objects will be slow, no matter how efficient the wrapping. But if you're manipulating enough individual elements to worry about the speed, then chances are there's a higher-level structure involved. For example, if you make a matrix over RDF, the entries are not stored individually wrapped, but as a single double*. In our case, linear algebra is done via NumPy, which in turn uses a BLAS (with Sage we ship ATLAS). On the other hand, if one is doing a problem that isn't easily phrased as a linear algebra/differential equations/etc. question for which there is already an optimized library/code, that's where Cython comes in, where one can compile code that operates on C doubles directly (and, because it compiles down to C, is just as efficient). It also fits in with the 90-10 philosophy, making it easy to optimize only those parts one needs to (instead of writing the whole thing in a more restrictive (depending on your tastes) language just so your couple of inner loops can be fast enough). Getting back to the topic of the thread, I think a reason Python is nice for scientific programming is that it's easy to learn, easy to read, and easy to prototype in, and has a rich set of libraries to work with. On the other hand, when you need raw number crunching speed there are good tools out there to interface with or write at a lower level (e.g. Cython, or all that's been wrapped by SciPy and Sage). - Robert --~--~-~--~~~---~--~~ To post to this group, send email to sage-devel@googlegroups.com To unsubscribe from this group, send email to sage-devel-unsubscr...@googlegroups.com For more options, visit this group at http://groups.google.com/group/sage-devel URLs: http://www.sagemath.org -~--~~~~--~~--~--~---
[sage-devel] Re: python vs java for scientific computing
You might like to read How Java’s floating-point hurts everyone everywhere (1998) by W Kahan, J D Darcy and other papers (like Darcy MS thesis). http://www.cs.berkeley.edu/~wkahan/JAVAhurt.pdf for arguments that suggest Java has certain problems with floating- point . On the other hand, it may be the case that Python's floating-point spec is worse, and the same arguments hold against Python. (I haven't studied it). Certainly if you are concerned with the comparison you should be aware of the paper by Kahan. As I recall, some of the arguments by Kahan/Dancy may have been ameliorated somewhat by further Java refinements. If the argument is whether python or java makes a better language for scripting the access to scientific libraries, perhaps the subject of this thread should be something else, and the comparison should not be between python and java. My favorite scripting language is still Lisp, but I have used visual basic, tcl, jscript, and some others. And if you want to look at languages widely used for scientific computing, there are several, some with the name FORTRAN, like Fortran 90. and also Fortress (from Sun). Or C. On Jan 20, 3:35 am, David Joyner wdjoy...@gmail.com wrote: Hi: This is not Sage-specific, but there is an interesting thread on the scipy-users list that some people on this list may like to follow:http://projects.scipy.org/pipermail/scipy-user/2009-January/019440.html In particular, this pagehttp://sites.google.com/site/almarklein/python-3 (on python vs matlab) and this pagehttp://sites.google.com/site/almarklein/quest (on Python as the best for a scientific platform) might be interesting to some on this list. This last article says, The most important argument is that in order to do scientific computing, you need an interpreted programming language. Still, I enjoyed coding C# and think it is a great language that allows making applications in a very short amount of time. This is quite interesting, but so far as I can tell, entirely unsupported by anything in the article. It also suggests that the writer is kind of ignorant of programming language technology. Perhaps he means interactive rather than interpreted? Or perhaps he means that for the last 50+ years since 1956, all those people who thought they were doing scientific computing in FORTRAN uh, really couldn't have been doing that at all? RJF - David JOyner --~--~-~--~~~---~--~~ To post to this group, send email to sage-devel@googlegroups.com To unsubscribe from this group, send email to sage-devel-unsubscr...@googlegroups.com For more options, visit this group at http://groups.google.com/group/sage-devel URLs: http://www.sagemath.org -~--~~~~--~~--~--~---
[sage-devel] Re: python vs java for scientific computing
On Jan 21, 12:29 am, rjf fate...@gmail.com wrote: You might like to read How Java’s floating-point hurts everyone everywhere (1998) by W Kahan, J D Darcy and other papers (like Darcy MS thesis).http://www.cs.berkeley.edu/~wkahan/JAVAhurt.pdf for arguments that suggest Java has certain problems with floating- point . Hello, I don't have time to read those 81 pages, but it is from 1998 and i can't find the strictfp keyword. This might be due to the release of java 1.2 in dec. 98, where this keyword was introduced. Have a look at http://en.wikipedia.org/wiki/Strictfp Things have changed till then... Aside that, there are other libraries for numerical maths that can be used from java. Also, since 98 there was a lot of progress around java. Therefore, I think such a publication from 98 must be adopted to reflect all the changes up to the current version 6 or upcoming version 7 to be valid. h --~--~-~--~~~---~--~~ To post to this group, send email to sage-devel@googlegroups.com To unsubscribe from this group, send email to sage-devel-unsubscr...@googlegroups.com For more options, visit this group at http://groups.google.com/group/sage-devel URLs: http://www.sagemath.org -~--~~~~--~~--~--~---
[sage-devel] Re: python vs java for scientific computing
My understanding is that, since Python is based on C, it is IEEE-754 compliant (as long as the CPU is, which I think is true for all the modern CPUs). Jython might be a different story. (Which, I just learned, is alive and has a new version just released.) Also, I think the Java changed its floating-point support a while ago. From what I remember, the problem was that they wanted Java code to produce exactly the same results in all possible platforms, and in doing so, their floating-point became non-compliant. I guess now they have alternative classes of floating-point routines. On Tue, Jan 20, 2009 at 6:29 PM, rjf fate...@gmail.com wrote: You might like to read How Java's floating-point hurts everyone everywhere (1998) by W Kahan, J D Darcy and other papers (like Darcy MS thesis). http://www.cs.berkeley.edu/~wkahan/JAVAhurt.pdf for arguments that suggest Java has certain problems with floating- point . On the other hand, it may be the case that Python's floating-point spec is worse, and the same arguments hold against Python. (I haven't studied it). Certainly if you are concerned with the comparison you should be aware of the paper by Kahan. As I recall, some of the arguments by Kahan/Dancy may have been ameliorated somewhat by further Java refinements. If the argument is whether python or java makes a better language for scripting the access to scientific libraries, perhaps the subject of this thread should be something else, and the comparison should not be between python and java. My favorite scripting language is still Lisp, but I have used visual basic, tcl, jscript, and some others. And if you want to look at languages widely used for scientific computing, there are several, some with the name FORTRAN, like Fortran 90. and also Fortress (from Sun). Or C. On Jan 20, 3:35 am, David Joyner wdjoy...@gmail.com wrote: Hi: This is not Sage-specific, but there is an interesting thread on the scipy-users list that some people on this list may like to follow:http://projects.scipy.org/pipermail/scipy-user/2009-January/019440.html In particular, this pagehttp://sites.google.com/site/almarklein/python-3 (on python vs matlab) and this pagehttp://sites.google.com/site/almarklein/quest (on Python as the best for a scientific platform) might be interesting to some on this list. This last article says, The most important argument is that in order to do scientific computing, you need an interpreted programming language. Still, I enjoyed coding C# and think it is a great language that allows making applications in a very short amount of time. This is quite interesting, but so far as I can tell, entirely unsupported by anything in the article. It also suggests that the writer is kind of ignorant of programming language technology. Perhaps he means interactive rather than interpreted? Or perhaps he means that for the last 50+ years since 1956, all those people who thought they were doing scientific computing in FORTRAN uh, really couldn't have been doing that at all? RJF - David JOyner -- The main things which seem to me important on their own account, and not merely as means to other things, are knowledge, art, instinctive happiness, and relations of friendship or affection. -Bertrand Russell L. Felipe Martins Department of Mathematics Cleveland State University luizfelipe.mart...@gmail.com --~--~-~--~~~---~--~~ To post to this group, send email to sage-devel@googlegroups.com To unsubscribe from this group, send email to sage-devel-unsubscr...@googlegroups.com For more options, visit this group at http://groups.google.com/group/sage-devel URLs: http://www.sagemath.org -~--~~~~--~~--~--~---
[sage-devel] Re: python vs java for scientific computing
On Jan 20, 5:15 pm, Luiz Felipe Martins luizfelipe.mart...@gmail.com wrote: My understanding is that, since Python is based on C, it is IEEE-754 compliant (as long as the CPU is, which I think is true for all the modern CPUs). There are many ways of being compliant with IEEE-754. If Sage is supposed to get the same answers on any computer it runs on, then IEEE-754 compliant is no assurance of anything. Indeed, if Sage must get the same floating point answers on every computer, it will run substantially slower than it should, on some. And get answers that are less accurate. Jython might be a different story. (Which, I just learned, is alive and has a new version just released.) Also, I think the Java changed its floating-point support a while ago. I mentioned that in my earlier note. From what I remember, the problem was that they wanted Java code to produce exactly the same results in all possible platforms, and in doing so, their floating-point became non-compliant. Actually this is quite well described in Kahan/Darcy, and I think the principal result is sort of the opposite. That is, you can make programs that produce exactly the same result, except it will slow down the fastest computers, produce less accurate results than would otherwise be obtained, and reduce various parts of the environment to the least powerful. That's what you get for strictfp, and was the default behavior prior to JVM 1.2 (according to wikipedia article). Running strictfp makes some (ordinarily much faster) Intel floating point processors just as slow as Sun processors, and also makes the results less accurate on Intel than would be ordinarily. Both Sun and Intel are compliant in various senses, but note that the IEEE-754 specifies the minimum number of bits in registers for double-float exponents and fractions, not the maximum. It may seem odd, but a programmer might want to have the best possible results for each computer, even if it means DIFFERENT results on different computers. This was what W. Kahan convinced Bill Joy was the right thing to do. I do not know if strictfp totally satisfied Kahan, or if it conforms to Darcy's design of the Borneo language. In my experience, people doing scientific calculations for a living will not tolerate a language implementation X whose programs are substantially slower than equivalent ones in a language implementation Y. (note that X and Y may be the same language but different implementations!) Comparing Java to Python but leaving out C is therefore not terribly relevant. (Except in scripting, GUI development, etc.) --~--~-~--~~~---~--~~ To post to this group, send email to sage-devel@googlegroups.com To unsubscribe from this group, send email to sage-devel-unsubscr...@googlegroups.com For more options, visit this group at http://groups.google.com/group/sage-devel URLs: http://www.sagemath.org -~--~~~~--~~--~--~---
