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Issue No.02 - March/April (2011 vol.13)
pp: 22-30
Stéfan van der Walt , Stellenbosch University
S. Chris Colbert , Enthought, Inc.
<p>In the Python world, NumPy arrays are the standard representation for numerical data and enable efficient implementation of numerical computations in a high-level language. As this effort shows, NumPy performance can be improved through three techniques: vectorizing calculations, avoiding copying data in memory, and minimizing operation counts.</p>
Python, NumPy, scientific programming, numerical computations, programming libraries
Stéfan van der Walt, S. Chris Colbert, Gaël Varoquaux, "The NumPy Array: A Structure for Efficient Numerical Computation", Computing in Science & Engineering, vol.13, no. 2, pp. 22-30, March/April 2011, doi:10.1109/MCSE.2011.37
1. F. Perez and B.E. Granger, "IPython: A System for Interactive Scientific Computing," Computing in Science & Eng., vol. 9, no. 3, 2007, pp. 21–29.
2. J. Bergstra, "Optimized Symbolic Expressions and GPU Metaprogramming with Theano," Proc. 9th Python in Science Conf. (SciPy2010); forthcoming; http://conference.scipy.orgproceedings.html .
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