On Tue, Feb 23, 2021 at 5:47 PM Charles R Harris <charlesr.har...@gmail.com>
wrote:
>
>
> On Tue, Feb 23, 2021 at 11:10 AM Neal Becker <ndbeck...@gmail.com> wrote:
>
>> I have code that performs dot product of a 2D matrix of size (on the
>> order of) [1000,16] with a vector of size [1000]. The matrix is
>> float64 and the vector is complex128. I was using numpy.dot but it
>> turned out to be a bottleneck.
>>
>> So I coded dot2x1 in c++ (using xtensor-python just for the
>> interface). No fancy simd was used, unless g++ did it on it's own.
>>
>> On a simple benchmark using timeit I find my hand-coded routine is on
>> the order of 1000x faster than numpy? Here is the test code:
>> My custom c++ code is dot2x1. I'm not copying it here because it has
>> some dependencies. Any idea what is going on?
>>
>> import numpy as np
>>
>> from dot2x1 import dot2x1
>>
>> a = np.ones ((1000,16))
>> b = np.array([ 0.80311816+0.80311816j, 0.80311816-0.80311816j,
>> -0.80311816+0.80311816j, -0.80311816-0.80311816j,
>> 1.09707981+0.29396165j, 1.09707981-0.29396165j,
>> -1.09707981+0.29396165j, -1.09707981-0.29396165j,
>> 0.29396165+1.09707981j, 0.29396165-1.09707981j,
>> -0.29396165+1.09707981j, -0.29396165-1.09707981j,
>> 0.25495815+0.25495815j, 0.25495815-0.25495815j,
>> -0.25495815+0.25495815j, -0.25495815-0.25495815j])
>>
>> def F1():
>> d = dot2x1 (a, b)
>>
>> def F2():
>> d = np.dot (a, b)
>>
>> from timeit import timeit
>> print (timeit ('F1()', globals=globals(), number=1000))
>> print (timeit ('F2()', globals=globals(), number=1000))
>>
>> In [13]: 0.013910860987380147 << 1st timeit
>> 28.608758996007964 << 2nd timeit
>>
>
> I'm going to guess threading, although huge pages can also be a problem on
> a machine under heavy load running other processes. Call overhead may also
> matter for such small matrices.
>
>
What BLAS library are you using. I get much better results using an 8 year
old i5 and ATLAS.
Chuck
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