[Numpy-discussion] Accelerate your Python code with parallel processing

[Ronnie Hoogerwerf]

I am an Application Engineer at Interactive Supercomputing and we are  
rolling out a beta version of our Star-P product for Python. We are  
actively looking for computationally intensive Python application to  
port to Star-P. Star-P is a parallel application development platform  
that allows users to tap into the power and memory of supercomputers  
from the comfort of the favorite desktop applications, in this case  
Python.

Star-P is capable of both fine-grained parallel computation and  
embarrassingly parallel computation. The fine-grained mode of our  
Star-P Python implementation has been modeled on the Python NumPy  
package - for example:

x = starp.random.rand(20000,20000)
y = starp.linalg.inv(x)

instead of

x = numpy.random.rand(20000,20000)
y = numpy.linalg.inv(x)

Where the first couple of lines are executed on the Star-P parallel  
server in full C/MPI mode and the last couple of lines are executed  
on the desktop using Python.

The embarrassingly parallel mode is capable of executing any Python  
module, although input and output parameters are currently limited to  
NumPy arrays, scalars, and strings - for example:

y = starp.ppeval(mymodule.dosomething,x)

instead of

for i in range(0,n):
     y[:,:,i] = mymodule.dosomething(x[:,i])

Where again in the former example the iterations are spread out over  
the available CPUs (note the abstraction - user need not worry  
regarding the number of CPUs) on the Star-P server using Python and  
in the latter the looping is doing in serial on the client using Python.

We are looking for real Python application that you would be willing  
to share with us that  we can port to Star-P. We want to use this  
experience as a basis for further improvements and development of our  
Python client.

Thanks,
Ronnie

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