[Numpy-discussion] Numpy Benchmarking

Travis Oliphant oliphant.travis at ieee.org
Tue Jun 27 20:24:23 EDT 2006

Dennis V. Perepelitsa wrote:
> Hi, all.
> I've run some benchmarks comparing the performance of scipy, numpy, 
> Numeric and numarray vs. MATLAB. There's also the beginnings of a 
> benchmark framework included. The results are online at:
>      http://web.mit.edu/jonas/www/bench/
> They were produced on a Thinkpad T42 with an Intel Pentium M 1.7GHz 
> processor running Ubuntu Dapper Drake (6.06). All the languages/packages 
> were built from source, and, in the case of numpy and scipy, linked to 
> ATLAS. Each datapoint represents the arithmetic mean of ten trials.
I agree with Robert that a minimum would be a better way to aggregate 

> The results have some interesting implications. For example, numpy and 
> scipy perform approximately the same except when it comes to matrix 
> inversion, MATLAB beats out all the Python packages when it comes to 
> matrix addition, and numpy seems to be beaten by its predecessors in some 
> cases. Why is this the case? 
In terms of creating zeros matrices, you are creating double-precision 
matrices for NumPy but only single-precision for Numeric and numarray.

Try using numpy.float32 or 'f' when creating numpy arrays.  The float is 
the Python type-object and represents a double-precision number.   Or, 
if you are trying to use double precision for all cases (say for 
comparison to MATLAB) then use 'd' in numarray and Numeric.

For comparing numpy with numarray and Numeric there are some benchmarks 
in the SVN tree of NumPy under benchmarks. 

These benchmarks have been helpful in the past in pointing out areas 
where we could improve the code of NumPy, so I'm grateful for your efforts.


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