Hi,
Is there a comparison with parallel libraries that can be branched on numpy
like MKL ? (and IPP for random number ?)
Matthieu
2007/6/29, Ronnie Hoogerwerf <[EMAIL PROTECTED]>:
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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