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

Tim Hochberg tim.hochberg at cox.net
Tue Jun 13 12:56:37 EDT 2006

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):

- Addition of a boolean type. This allows better array copying times
for large arrays (lightweight computations ara typically bounded by
memory bandwidth).

Adding this to numexpr looks like a no brainer. Behaviour of booleans are different than integers, so in addition to being more memory efficient, this enables boolean &, |, ~, etc to work properly.

- 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 NumExpr_run, where I expect it's lurking, so any hints would be appreciated.

- Addition of ~, & and | operators (a la numarray.where)

Sounds good.

- 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.

- Added a new benchmark for testing boolean expressions and
strided/unaligned arrays: boolean_timing.py

Benchmarks are always good.

Things that I want to address in the future:

- Add tests on strided and unaligned data (currently only tested
manually)

Yep! Tests are good.

- 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 OP_CAST_Ffs followed by an OP_ADD_FFF, and then a OP_CAST_fF. 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.

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.

Regards,

-tim

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