[marmaduke]

There is no "production" in scientific programs. It runs once correctly to make the figure... more seriously, ontology is often a moving target, so the longer in takes to rewrite significant parts of the data structures, the less time there is to do science.

re: concurrency: I have a script that boots hundreds of IPython workers on hundreds of cores. I then make a client object (in antoher IPython shell), and map my 1e8 parameter configurations on to the cores, all in under a minute. This is much faster than rewritng in C.

I even implemented a special case of the brain simulator we've developed in Python (http://thevirtualbrain.org/) in C w/ unaliased pointer arithmetic etc. It's 50% faster but took more than 50% longer to write; on the other hand the PyCUDA implementation is 80x faster, and didn't take 80x, maybe 10x. Also a win because PyCUDA takes care of the uglier details.

so #2 is a big win

[ynniv]

It's 50% faster but took more than 50% longer to write

At this point it's useful to know how long it takes to run, and how long to write. Is a run days long, months long, or years long? Or another way, is concurrency more expensive than a C re-programmer?

Also a win because PyCUDA takes care of the uglier details.

Is there not an analogous C++ library to take care of ugly details?

(I actually like python a lot, so there's a bit of devil's advocate going on. But, my longest running python programs take less than an hour.)

[marmaduke]

Typical simulations for us take between half a minute and several days, but this can depend because it's typically necessary to do a parameter sweep in several dimensions (leading in extreme cases to runtimes of several months on a cluster).

I believe Thrift (now shipped w/ CUDA SDK) makes things easier, but (since you know Python) nothing like NumPy exists in C++ and PyCUDA maps NumPy seamlessly into GPU computing, which is a big win.

[pwang]

If you are getting good results from CUDA and PyCUDA, you might want to take a look at Numba and NumbaPro: http://numba.pydata.org and http://continuum.io/numbapro. They are still in their early stages but work pretty well on a number of cases. Here is an example of what NumbaPro can do: http://docs.continuum.io/numbapro/generalizedufuncs.html#gen...

Numba is completely open source. NumbaPro is not open source, but it is free for academic users.

[marcosdumay]

> Is there not an analogous C++ library to take care of ugly details?

No. In general, there isn't an analogous library at the more static-explicit languages (it doesn't matter much what library you choose). There are libs that people use when they have similar requisites, but they rarely are analogous.