[Numpy-discussion] eighteen bourbon
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Re: [Numpy-discussion] Any Numeric or numarray users on this list?
On 10.06.2006, at 01:57, Travis Oliphant wrote: You may be interested to note that I just added the RNG interface to numpy for back-wards compatibility. It can be accessed and used by re-placing import RNG with import numpy.random.oldrng as RNG Thanks, that will facilitate the transition. Is this just a compatible interface, or actually the same algorithm as in the original RNG module? Konrad. -- - Konrad Hinsen Centre de Biophysique Moléculaire, CNRS Orléans Synchrotron Soleil - Division Expériences Saint Aubin - BP 48 91192 Gif sur Yvette Cedex, France Tel. +33-1 69 35 97 15 E-Mail: [EMAIL PROTECTED] - ___ Numpy-discussion mailing list Numpy-discussion@lists.sourceforge.net https://lists.sourceforge.net/lists/listinfo/numpy-discussion
Re: [Numpy-discussion] Any Numeric or numarray users on this list?
[EMAIL PROTECTED] wrote: On 10.06.2006, at 01:57, Travis Oliphant wrote: You may be interested to note that I just added the RNG interface to numpy for back-wards compatibility. It can be accessed and used by re-placing import RNG with import numpy.random.oldrng as RNG Thanks, that will facilitate the transition. Is this just a compatible interface, or actually the same algorithm as in the original RNG module? Just the interface. Do you actually want to use the old algorithm, or are you primarily concerned about matching old test results? The old algorithms are not very good, so I really don't want to put them back into numpy. It should be easy to roll out a separate RNG module that simply uses numpy instead of Numeric, though. -- 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 ___ Numpy-discussion mailing list Numpy-discussion@lists.sourceforge.net https://lists.sourceforge.net/lists/listinfo/numpy-discussion
Re: [Numpy-discussion] Back to numexpr
A Dimarts 13 Juny 2006 19:47, Francesc Altet va escriure: - Support for both numpy and numarray (use the flag --force-numarray in setup.py). At first glance this looks like it doesn't make things to messy, so I'm in favor of incorporating this. Yeah. I thing you are right. It's only that we need this for our own things :) Ooops! small correction here. I thought that you were saying that you were *not* in favour of supporting numarray as well, but you clearly was. Sorry about the misunderstanding. Anyway, if David's idea of providing a thin numpy-compatible numarray layer is easy to implement, then great. Cheers, -- 0,0 Francesc Altet http://www.carabos.com/ V V Cárabos Coop. V. Enjoy Data - ___ Numpy-discussion mailing list Numpy-discussion@lists.sourceforge.net https://lists.sourceforge.net/lists/listinfo/numpy-discussion
Re: [Numpy-discussion] Back to numexpr
Francesc Altet wrote: Ei, numexpr seems to be back, wow! :-D A Dimarts 13 Juny 2006 18:56, Tim Hochberg va escriure: I've finally got around to looking at numexpr again. Specifically, I'm looking at Francesc Altet's numexpr-0.2, with the idea of harmonizing the two versions. Let me go through his list of enhancements and comment (my comments are dedented): Well, as David already said, he committed most of my additions some days ago :-) - Enhanced performance for strided and unaligned data, specially for lightweigth computations (e.g. 'a10'). With this and the addition of the boolean type, we can get up to 2x better times than previous versions. Also, most of the supported computations goes faster than with numpy or numarray, even the simplest one. Francesc, if you're out there, can you briefly describe what this support consists of? It's been long enough since I was messing with this that it's going to take me a while to untangle NumExpr_run, where I expect it's lurking, so any hints would be appreciated. This is easy. When dealing with strided or unaligned vectors, instead of copying them completely to well-behaved arrays, they are copied only when the virtual machine needs the appropriate blocks. With this, there is no need to write the well-behaved array back into main memory, which can bring an important bottleneck, specially when dealing with large arrays. This allows a better use of the processor caches because data is catched and used only when the VM needs it. Also, I see that David has added support for byteswapped arrays, which is great! I'm looking at this now. I imagine it will become clear eventually. I've clearly forgotten some stuff over the last few months. Sigh. First I need to get it to compile here. It seems that a few GCCisms have crept back in. [SNIP] rarely used. Uh, I'm afraid that yes. In PyTables, int64, while being a bit bizarre for some users (specially in 32-bit platforms), is a type with the same rights than the others and we would like to give support for it in numexpr. In fact, Ivan Vilata already has implemented this suport in our local copy of numexpr, so perhaps (I say perhaps because we are in the middle of a big project now and are a bit scarce of time resources) we can provide the patch against the latest version of David for your consideration. With this we can solve the problem with int64 support in 32-bit platforms (although addmittedly, the VM gets a bit more complicated, I really think that this is worth the effort) In addition to complexity, I worry that we'll overflow the code cache at some point and slow everything down. To be honest I have no idea at what point that is likely to happen, but I know they worry about it with the Python interpreter mainloop. Also, it becomes much, much slower to compile past a certain number of case statements under VC7, not sure why. That's mostly my problem though. One idea that might be worth trying for int64 is to special case them using functions. That is using OP_FUNC_LL and OP_FUNC_LLL and some casting opcodes. This could support int64 with relatively few new opcodes. There's obviously some exta overhead introduced here by the function call. How much this matters is probably a function of how well the compiler / hardware supports int64 to begin with. That brings up another point. We probably don't want to have casting opcodes from/to everything. Given that there are 8 types on the table now, if we support every casting opcode we're going to have 56(?) opcodes just for casting. I imagine what we'll have to do is write a cast from int16 to float as OP_CAST_Ii; OP_CAST_FI; trading an extra step in these cases for keeping the number of casting opcodes under control. Once again, int64 is problematic since you lose precision casting to int. I guess in this case you could get by with being able to cast back and forth to float and int. No need to cast directly to booleans, etc as two stage casting should suffice for this. -tim ___ Numpy-discussion mailing list Numpy-discussion@lists.sourceforge.net https://lists.sourceforge.net/lists/listinfo/numpy-discussion
Re: [Numpy-discussion] Back to numexpr
Francesc Altet wrote: A Dimarts 13 Juny 2006 20:46, Tim Hochberg va escriure: Uh, I'm afraid that yes. In PyTables, int64, while being a bit bizarre for some users (specially in 32-bit platforms), is a type with the same rights than the others and we would like to give support for it in numexpr. In fact, Ivan Vilata already has implemented this suport in our local copy of numexpr, so perhaps (I say perhaps because we are in the middle of a big project now and are a bit scarce of time resources) we can provide the patch against the latest version of David for your consideration. With this we can solve the problem with int64 support in 32-bit platforms (although addmittedly, the VM gets a bit more complicated, I really think that this is worth the effort) In addition to complexity, I worry that we'll overflow the code cache at some point and slow everything down. To be honest I have no idea at what point that is likely to happen, but I know they worry about it with the Python interpreter mainloop. That's true. I didn't think about this :-/ Also, it becomes much, much slower to compile past a certain number of case statements under VC7, not sure why. That's mostly my problem though. No, this is a general problem (I'd say much more in GCC, because the optimizer runs so slow). However, this should only affect to poor developers, not users and besides, we should find a solution for int64 in 32-bit platforms. Yeah. This is just me whining. Under VC7, there is a very sudden change when adding more cases where compile times go from seconds to minutes. I think we're already past that now anyway, so slowing that down more isn't going to hurt me. Overflowing the cache is the real thing I worry about. One idea that might be worth trying for int64 is to special case them using functions. That is using OP_FUNC_LL and OP_FUNC_LLL and some casting opcodes. This could support int64 with relatively few new opcodes. There's obviously some exta overhead introduced here by the function call. How much this matters is probably a function of how well the compiler / hardware supports int64 to begin with. Mmm, in my experience int64 operations are reasonable well supported by modern 32-bit processors (IIRC they normally take twice of the time than int32 ops). The problem with using a long for representing ints in numexpr is that we have the duality of being represented differently in 32/64-bit platforms and that could a headache in the long term (int64 support in 32-bit platforms is only one issue, but there should be more). IMHO, it is much better to assign the role for ints in numexpr to a unique datatype, and this should be int64, for the sake of wide int64 support, but also for future (and present!) 64-bit processors. The problem would be that operations with 32-bit ints in 32-bit processors can be slowed-down by a factor 2x (or more, because there is a casting now), but in exchange, whe have full portable code and int64 support. This certainly makes things simpler. I think that this would be fine with me since I mostly use float and complex, so the speed issue wouldn't hit me much. But that's 'cause I'm selfish that way ;-) In case we consider entering this way, we have two options here: keep VM simple and advertise that int32 arithmetic in numexpr in 32-bit platforms will be sub-optimal, or, as we already have done, add the proper machinery to support both integer separately (at the expense of making the VM more complex). Or perhaps David can come with a better solution (vmgen from gforth? no idea what this is, but the name sounds sexy;-) Yeah! That brings up another point. We probably don't want to have casting opcodes from/to everything. Given that there are 8 types on the table now, if we support every casting opcode we're going to have 56(?) opcodes just for casting. I imagine what we'll have to do is write a cast from int16 to float as OP_CAST_Ii; OP_CAST_FI; trading an extra step in these cases for keeping the number of casting opcodes under control. Once again, int64 is problematic since you lose precision casting to int. I guess in this case you could get by with being able to cast back and forth to float and int. No need to cast directly to booleans, etc as two stage casting should suffice for this. Well, we already thought about this. Not only you can't safely cast an int64 to an int32 without loosing precistion, but what is worse, you can't even cast it to any other commonly available datatype (casting to a float64 will also loose precision). And, although you can afford loosing precision when dealing with floating data in some scenarios (but not certainly with a general-purpose library like numexpr tries to be), it is by any means unacceptable loosing 'precision' in ints. So, to my mind, the only solution is completely avoiding casting int64 to any type. I forgot that the various OP_CAST_xy opcodes only do safe
Re: [Numpy-discussion] Atlas missing dgeev
Mathew Yeates wrote: I finally got things linked with libg2c but now I get import linalg - failed: ld.so.1: python: fatal: relocation error: file /u/fuego0/myeates/lib/python2.4/site-packages/numpy/linalg/lapack_lite.so: symbol dgeev_: referenced symbol not found I looked all through my ATLAS source and I see no dgeenv anywhere.No file of that name and no refernces to that function. Anybody know what up with this? ATLAS itself only provides optimized versions of some LAPACK routines. You need to combine it with the full LAPACK to get full coverage. Please read the ATLAS FAQ for instructions: http://math-atlas.sourceforge.net/errata.html#completelp -- 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 ___ Numpy-discussion mailing list Numpy-discussion@lists.sourceforge.net https://lists.sourceforge.net/lists/listinfo/numpy-discussion
