[Numpy-discussion] Back to numexpr (original) (raw)

Francesc Altet faltet at carabos.com
Tue Jun 13 13:47:35 EDT 2006

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. 'a>10'). 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 NumExprrun, 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!

- 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 :)

- Add types for int16, int64 (in 32-bit platforms), float32, complex64 (simple prec.)

I have some specific ideas about how this should be accomplished. Basically, I don't think we want to support every type in the same way, since this is going to make the case statement blow up to an enormous size. This may slow things down and at a minimum it will make things less comprehensible. My thinking is that we only add casts for the extra types and do the computations at high precision. Thus adding two int16 numbers compiles to two OPCASTFfs followed by an OPADDFFF, and then a OPCASTfF. The details are left as an excercise to the reader ;-). So, adding int16, float32, complex64 should only require the addition of 6 casting opcodes plus appropriate modifications to the compiler. For large arrays, this should have most of the benfits of giving each type it's own opcode, since the memory bandwidth is still small, while keeping the interpreter relatively simple.

Yes, I like the idea as well.

Unfortunately, int64 doesn't fit under this scheme; is it used enough to matter? I hate pile a whole pile of new opcodes on for something that's 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).

Cheers,

--

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