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 -~----------~----~----~----~------~----~------~--~---
