np vs. loop

np vs. loop.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import timeit\n",
    "from numba import njit\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "@njit\n",
    "def jit_norm_1_np(a):\n",
    "    return np.sum(np.abs(a))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "@njit\n",
    "def jit_norm_1_loop(a):\n",
    "    ret = 0.0\n",
    "    for k in range(a.shape[0]):\n",
    "        ret += abs(a[k])\n",
    "    return ret"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "a = np.arange(10000)\n",
    "aa = [np.arange(10**i) for i in range(10)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1\n",
      "The slowest run took 8.52 times longer than the fastest. This could mean that an intermediate result is being cached.\n",
      "992 ns ± 1.2 µs per loop (mean ± std. dev. of 7 runs, 1 loop each)\n",
      "10\n",
      "371 ns ± 12.3 ns per loop (mean ± std. dev. of 7 runs, 1000000 loops each)\n",
      "100\n",
      "419 ns ± 9.96 ns per loop (mean ± std. dev. of 7 runs, 1000000 loops each)\n",
      "1000\n",
      "488 ns ± 9.16 ns per loop (mean ± std. dev. of 7 runs, 1000000 loops each)\n",
      "10000\n",
      "4.56 µs ± 152 ns per loop (mean ± std. dev. of 7 runs, 100000 loops each)\n",
      "100000\n",
      "44.5 µs ± 973 ns per loop (mean ± std. dev. of 7 runs, 10000 loops each)\n",
      "1000000\n",
      "1.06 ms ± 94.3 µs per loop (mean ± std. dev. of 7 runs, 1000 loops each)\n",
      "10000000\n",
      "24.8 ms ± 1.22 ms per loop (mean ± std. dev. of 7 runs, 10 loops each)\n",
      "100000000\n",
      "575 ms ± 19.1 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)\n",
      "1000000000\n",
      "25.7 s ± 1.07 s per loop (mean ± std. dev. of 7 runs, 1 loop each)\n"
     ]
    }
   ],
   "source": [
    "for aaa in aa:\n",
    "    print(aaa.shape[0])\n",
    "    %timeit jit_norm_1_np(aaa)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1\n",
      "227 ns ± 6.42 ns per loop (mean ± std. dev. of 7 runs, 1000000 loops each)\n",
      "10\n",
      "233 ns ± 3.49 ns per loop (mean ± std. dev. of 7 runs, 1000000 loops each)\n",
      "100\n",
      "325 ns ± 6.15 ns per loop (mean ± std. dev. of 7 runs, 1000000 loops each)\n",
      "1000\n",
      "1.2 µs ± 11.3 ns per loop (mean ± std. dev. of 7 runs, 1000000 loops each)\n",
      "10000\n",
      "10.2 µs ± 244 ns per loop (mean ± std. dev. of 7 runs, 100000 loops each)\n",
      "100000\n",
      "98.5 µs ± 1.56 µs per loop (mean ± std. dev. of 7 runs, 10000 loops each)\n",
      "1000000\n",
      "1.02 ms ± 22.2 µs per loop (mean ± std. dev. of 7 runs, 1000 loops each)\n",
      "10000000\n",
      "10.7 ms ± 491 µs per loop (mean ± std. dev. of 7 runs, 100 loops each)\n",
      "100000000\n",
      "102 ms ± 3.85 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)\n",
      "1000000000\n",
      "1.08 s ± 80.7 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)\n"
     ]
    }
   ],
   "source": [
    "for aaa in aa:\n",
    "    print(aaa.shape[0])\n",
    "    %timeit jit_norm_1_loop(aaa)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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