[IPython-User] optimize parallel optimizations code?

Skipper Seabold jsseabold@gmail....
Thu Nov 18 14:02:06 CST 2010


For posterity's sake.  The objective function needs to be negated for
the results to make sense.

On Thu, Nov 18, 2010 at 2:43 PM, Skipper Seabold <jsseabold@gmail.com> wrote:
> Hi Brian,
>
> Thanks for having a look.  I think I figured out that it is an issue
> of overhead and the parallel version wins as I scale it up.
>
> On Tue, Nov 16, 2010 at 5:16 PM, Brian Granger <ellisonbg@gmail.com> wrote:
>> Skipper,
>> I looked through your code and have a few questions and thoughts:
>> * What data are you sending back and forth between the client and the
>> engines.  It looks like that is going on here:
>
> These are different starting values for the optimizer.  In this case
> is 4 different float64 arrays of shape (2,)
>
>>        mec.push(dict(start_params=start_params1), targets=0)
>>        mec.push(dict(start_params=start_params2), targets=1)
>>        mec.push(dict(start_params=start_params3), targets=2)
>>        mec.push(dict(start_params=start_params4), targets=3)
>>        mec.execute("""
>> params = start_params
>> ret = optimize.fmin_tnc(obj, params, approx_grad=True,
>>                             eta=eta, maxfun=3000, args=(Y,X),
>> messages=0)""")
>>        ret1 = mec.pull('ret', targets=0)[0]
>>        ret2 = mec.pull('ret', targets=1)[0]
>>        ret3 = mec.pull('ret', targets=2)[0]
>>        ret4 = mec.pull('ret', targets=3)[0]
>
> ret is a tuple of len 3 returned by the tnc optimizer.  It contains a
> shape (2,) array, a float, and an int.
>
>> and:
>>    mec.push(dict(np=np, optimize=optimize, Y=Y,X=X,eta=eta,dot=dot))
>>
>
> I'm wondering if pushing the whole numpy and scipy.optimize namespace
> could cause a performance hit.
>
> Y and X are arrays of shape (100,1) and (100,2) respectively.
>
>> One way of getting a sense of the data movement overhead is to run the code
>> with just the data movement and no computation.
>> * Can you time the parallel and serial code to get a better sense of where
>> it is taking a long time.
>> * Can you estimate the amount of time spend in the part of the code you are
>> parallelizing.  Or, how much time is spent in the part of the code you
>> didn't parallelize.
>> Let's start there..
>> Cheers,
>> Brian
>>
>
> Comes back later...  It seems that it is indeed a matter of overhead.
> I've attached a standalone script if anyone is interested that times
> only the relevant parts that I want to speed up.  As I make the size
> of the arrays bigger, the parallel versions starts to win handily.
>
> In [18]: run parallel_optimize.py
> <snip>
> T = 100
> 0.105112075806 seconds for parallel optimization
> 0.0321190357208 seconds for serial optimization
>
> In [19]: run parallel_optimize.py
> <snip>
> T = 1000
> 0.117481946945 seconds for parallel optimization
> 0.062824010849 seconds for serial optimization
>
> In [20]: run parallel_optimize.py
> <snip>
> T = 10000
> 0.262063980103 seconds for parallel optimization
> 0.464597940445 seconds for serial optimization
>
> In [21]: run parallel_optimize.py
> <snip>
> T = 100000
> 1.49375987053 seconds for parallel optimization
> 4.25988888741 seconds for serial optimization
>
> In my case, T will be 10, 50, and 500.  Since what I'm really doing is
> running this code as monte carlo simulations many times.  What I could
> do is leave the individual fitting in serial and break the many times
> over my cores I suppose.  Surely that will give a performance boost.
> We'll see.
>
> Skipper
>
> PS.  It seems that the new syntax for ipcluster is
>
> ipcluster start -n #
>
> with the most recent git cloned.  Should the nightly build docs reflect this?
>
>>
>>
>> On Sun, Nov 14, 2010 at 9:07 PM, Skipper Seabold <jsseabold@gmail.com>
>> wrote:
>>>
>>> I have a class that does some optimizations multiple times using
>>> different starting values in order to check for convergence problems
>>> due to some "bad" data.  My code was taking about an hour, then I
>>> added the multiple calls to scipy.optimize, and it went up to about 4
>>> hours.  I am new to this, but I tried to parallelize this code,
>>> thinking it could speed things up.  It looks like it's much actually
>>> slower.  Is there anything I can do to optimize this or remove some
>>> overhead?  A test case is inlined and attached.
>>>
>>>
>>> In [3]: timeit mod.fit()
>>> 10 loops, best of 3: 39.2 ms per loop
>>>
>>> In [4]: timeit pmod.fit()
>>> 1 loops, best of 3: 255 ms per loop
>>>
>>>
>>> import numpy as np
>>> from numpy import log, dot, array
>>> from scipy import optimize
>>> from IPython.kernel import client
>>>
>>>
>>> def pobj(params, Y,X):
>>>    """
>>>    Normal log-likelihood
>>>    """
>>>    nobs2 = len(X)/2.
>>>    resid = Y - dot(X,params[:,None])
>>>    return -(- nobs2*np.log(2*np.pi)-nobs2*np.log(1/(2*nobs2) *\
>>>        dot(resid.T, resid)) - nobs2)
>>>
>>>
>>> class PModel(object):
>>>    def __init__(self, Y,X):
>>>        self.X = X
>>>        self.Y = Y
>>>
>>>    def fit(self, start_params=[0,0], eta=1e-8):
>>>        start_params = np.asarray(start_params)
>>>        Y = self.Y
>>>        X = self.X
>>>
>>>        start_params1 = start_params.copy()
>>>        start_params2 = start_params + 5
>>>        start_params3 = start_params - 5
>>>        start_params4 = np.random.randint(-10,10,size=2)
>>>        mec.push(dict(start_params=start_params1), targets=0)
>>>        mec.push(dict(start_params=start_params2), targets=1)
>>>        mec.push(dict(start_params=start_params3), targets=2)
>>>        mec.push(dict(start_params=start_params4), targets=3)
>>>        mec.execute("""
>>> params = start_params
>>> ret = optimize.fmin_tnc(obj, params, approx_grad=True,
>>>                             eta=eta, maxfun=3000, args=(Y,X),
>>> messages=0)""")
>>>        ret1 = mec.pull('ret', targets=0)[0]
>>>        ret2 = mec.pull('ret', targets=1)[0]
>>>        ret3 = mec.pull('ret', targets=2)[0]
>>>        ret4 = mec.pull('ret', targets=3)[0]
>>>        self.results = ret1
>>>        # check results
>>>        try:
>>>            np.testing.assert_almost_equal(ret1[0], ret2[0], 4)
>>>            np.testing.assert_almost_equal(ret1[0], ret3[0], 4)
>>>            np.testing.assert_almost_equal(ret1[0], ret4[0], 4)
>>>            self.converged = str(ret1[-1])+":
>>> "+optimize.tnc.RCSTRINGS[ret1[-1]]
>>>        except:
>>>            self.converged = "9: Results sensitive to starting values"
>>>
>>> class Model(object):
>>>    def __init__(self, Y,X):
>>>        self.X = X
>>>        self.Y = Y
>>>
>>>    def obj(self, params, Y, X):
>>>        """
>>>        Normal log-likelihood
>>>        """
>>>        X = self.X
>>>        Y = self.Y
>>>        nobs2 = len(X)/2.
>>>        resid = Y - np.dot(X,params[:,None])
>>>        return - nobs2*np.log(2*np.pi)-nobs2*np.log(1/(2*nobs2) *\
>>>                np.dot(resid.T,resid)) - nobs2
>>>
>>>    def fit(self, start_params=[0,0], eta=1e-8):
>>>        start_params = np.asarray(start_params)
>>>        obj = self.obj
>>>        Y = self.Y
>>>        X = self.X
>>>
>>>        start_params1 = start_params.copy()
>>>        start_params2 = start_params + 5
>>>        start_params3 = start_params - 5
>>>        start_params4 = np.random.randint(-10,10,size=2)
>>>        ret1 = optimize.fmin_tnc(obj, start_params1, approx_grad=True,
>>>                             eta=eta, maxfun=3000, args=(Y,X), messages=0)
>>>        ret2 = optimize.fmin_tnc(obj, start_params2, approx_grad=True,
>>>                             eta=eta, maxfun=3000, args=(Y,X), messages=0)
>>>        ret3 = optimize.fmin_tnc(obj, start_params3, approx_grad=True,
>>>                             eta=eta, maxfun=3000, args=(Y,X), messages=0)
>>>        ret4 = optimize.fmin_tnc(obj, start_params4, approx_grad=True,
>>>                             eta=eta, maxfun=3000, args=(Y,X), messages=0)
>>>
>>>        self.results = ret1
>>>        # check results
>>>        try:
>>>            np.testing.assert_almost_equal(ret1[0], ret2[0], 4)
>>>            np.testing.assert_almost_equal(ret1[0], ret3[0], 4)
>>>            np.testing.assert_almost_equal(ret1[0], ret4[0], 4)
>>>            self.converged = str(ret1[-1])+":
>>> "+optimize.tnc.RCSTRINGS[ret1[-1]]
>>>        except:
>>>            self.converged = "9: Results sensitive to starting values"
>>>
>>>
>>> if __name__ == "__main__":
>>>    np.random.seed(12345)
>>>    X = np.random.uniform(0,10,size=(10000,2))
>>>    beta = np.array([3.5,-3.5])
>>>    Y = np.dot(X,beta[:,None]) + np.random.normal(size=(10000,1))
>>>
>>>    eta = 1e-8
>>>
>>>    mec = client.MultiEngineClient()
>>>    mec.push_function(dict(obj=pobj))
>>>    mec.push(dict(np=np, optimize=optimize, Y=Y,X=X,eta=eta,dot=dot))
>>>    pmod = PModel(Y,X)
>>>    pmod.fit()
>>>
>>>    mod = Model(Y,X)
>>>    mod.fit()
>>>
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>>>
>>
>>
>>
>> --
>> Brian E. Granger, Ph.D.
>> Assistant Professor of Physics
>> Cal Poly State University, San Luis Obispo
>> bgranger@calpoly.edu
>> ellisonbg@gmail.com
>>
>


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