04 - Memory Layout and Performance Concerns.ipynb

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    "import numpy as np"
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    "Remember from the previous notebooks:\n",
    "\n",
    "### In row-major layout of multi-dimensional arrays, the last index is the fastest changing\n",
    "\n",
    "### Row Major (or 'C' ordering)\n",
    "![row_major](images/row-major-2D.png)\n",
    "\n",
    "For performant code, it is important to remember that we:    \n",
    "### Always try to traverse data in the order it is laid out in memory.\n",
    "\n",
    "Let's see an example."
   ]
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   "cell_type": "code",
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    "x = np.random.rand(10_000,10_000)"
   ]
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    "x.flags"
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   "source": [
    "### Exercise\n",
    "\n",
    "Before executing the cells below, think about this - which cell do you expect to execute faster? Why?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "%timeit np.sum(x, axis=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "%timeit np.sum(x, axis=1)"
   ]
  }
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