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adding readme
updating readme updating title in README updating notebook filled out README noting time of original talk updating readme section titles corrected formatting
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‎.ipynb_checkpoints/benchmark-checkpoint.ipynb‎

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{
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"cells": [
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{
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"cell_type": "code",
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"execution_count": 1,
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"id": "f03c3997-70f0-4196-b909-d84162d8c2c7",
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"metadata": {},
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"outputs": [],
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"source": [
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"import coding_club as cc\n",
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"\n",
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"import numpy as np\n",
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"import time\n",
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"import matplotlib.pyplot as plt"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 2,
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"id": "220a9b52-0091-4b07-b926-5d8311844e5d",
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"metadata": {},
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"outputs": [],
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"source": [
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"def create_random_matrix(n: int):\n",
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" \"\"\"\n",
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" Initializes an n x n matrix with random numbers from 0 to 1.\n",
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"\n",
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" Args:\n",
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" n: The dimension of the square matrix (e.g., for an n x n matrix).\n",
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"\n",
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" Returns:\n",
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" A numpy array representing the n x n matrix with random numbers.\n",
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" \"\"\"\n",
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" matrix = np.random.rand(n, n)\n",
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" return matrix\n",
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"\n",
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"def manual_matrix_multiply(A: list[list[float]], B: list[list[float]]) -> list[list[float]]:\n",
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" \"\"\"\n",
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" Multiplies two matrices A and B using pure Python.\n",
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" Assumes inputs are well-formed (rectangular) lists of lists.\n",
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" \"\"\"\n",
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" # Get dimensions\n",
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" rows_A = len(A)\n",
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" cols_A = len(A[0])\n",
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" rows_B = len(B)\n",
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" cols_B = len(B[0])\n",
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"\n",
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" # Check if multiplication is possible\n",
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" if cols_A != rows_B:\n",
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" raise ValueError(f\"Cannot multiply matrices of shapes ({rows_A}, {cols_A}) and ({rows_B}, {cols_B}). Inner dimensions must match.\")\n",
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"\n",
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" # Initialize the result matrix C with zeros.\n",
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" # Dimensions of C will be (rows_A, cols_B)\n",
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" C = [[0 for _ in range(cols_B)] for _ in range(rows_A)]\n",
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"\n",
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" # Perform the multiplication\n",
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" # Iterate through each row of A\n",
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" for i in range(rows_A):\n",
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" # Iterate through each column of B\n",
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" for j in range(cols_B):\n",
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" # Calculate the dot product of A's row i and B's column j\n",
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" dot_product = 0\n",
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" for k in range(cols_A): # or range(rows_B)\n",
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" dot_product += A[i][k] * B[k][j]\n",
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" C[i][j] = dot_product\n",
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" \n",
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" return C\n",
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"\n",
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"def matrix_power(matrix: np.ndarray[np.ndarray], exponent: int, nrows: int):\n",
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" \"\"\"\n",
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" Multiplies two matrices using the manual Python matrix multiplication function\n",
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" \"\"\"\n",
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"\n",
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" result = np.eye(nrows, dtype = \"f8\")\n",
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" base = matrix\n",
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"\n",
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" while exponent > 0:\n",
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" if exponent % 2 == 1:\n",
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" result = manual_matrix_multiply(result, base)\n",
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" base = manual_matrix_multiply(base, base)\n",
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" exponent //= 2\n",
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" return result\n",
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"\n",
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"def np_matrix_power(matrix: np.ndarray[np.ndarray], exponent: int, nrows: int):\n",
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" \"\"\"\n",
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" Multiplies two matrices using the numpy.dot matrix multiplication function\n",
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" \"\"\"\n",
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"\n",
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" result = np.eye(nrows, dtype = \"f8\")\n",
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" base = matrix\n",
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"\n",
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" while exponent > 0:\n",
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" if exponent % 2 == 1:\n",
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" result = np.dot(result, base)\n",
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" base = np.dot(base, base)\n",
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" exponent //= 2\n",
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" return result"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "5019de12-010f-4d59-9b69-479a485d6b3a",
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"metadata": {},
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"outputs": [],
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"source": [
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"%%time\n",
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"\n",
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"python_times = np.array([])\n",
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"\n",
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"numpy_times = np.array([])\n",
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"\n",
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"py_numpy_times = np.array([])\n",
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"\n",
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"rust_python_times = np.array([])\n",
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"\n",
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"size = [5, 7, 10, 20, 50, 70, 100, 200, 500, 700]\n",
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"\n",
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"iterations = 10\n",
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"\n",
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"for i in size:\n",
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" matrix = create_random_matrix(i)\n",
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"\n",
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" s = time.process_time(); py_result = matrix_power(matrix, iterations, i); e = time.process_time();\n",
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" python_times = np.append(python_times, e-s)\n",
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"\n",
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"\n",
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" s = time.process_time(); numpy_result = np.linalg.matrix_power(matrix, iterations); e = time.process_time();\n",
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" numpy_times = np.append(numpy_times, e-s)\n",
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"\n",
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"\n",
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" s = time.process_time(); rust_result = cc.matrix_power(matrix, iterations); e = time.process_time();\n",
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" rust_python_times = np.append(rust_python_times, e-s)\n",
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"\n",
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" if np.all(np.isclose(py_result, numpy_result)) & np.all(np.isclose(numpy_result, rust_result)) & np.all(np.isclose(py_result, rust_result)):\n",
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" continue\n",
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" else: \n",
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" print(\"Results are inconsistent\")\n",
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" break\n"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "2123601b-fed4-418c-8107-c8b98db516be",
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"metadata": {},
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"outputs": [],
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"source": [
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"plt.plot(size, python_times, label = \"Python\")\n",
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"plt.plot(size, numpy_times, label = \"Numpy\")\n",
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"plt.plot(size, rust_python_times, label = \"Rust-Python\")\n",
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"plt.legend()\n",
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"plt.title(\"Runtime Performance on Matrix Benchmark\")\n",
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"plt.ylabel(\"Runtime (seconds)\")\n",
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"plt.xlabel(\"Matrix size\")"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "314bd286-0c80-45e4-9954-6c91fc93d578",
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"metadata": {},
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"outputs": [],
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"source": [
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"plt.plot(size, python_times, label = \"Python\")\n",
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"plt.plot(size, numpy_times, label = \"Numpy\")\n",
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"plt.plot(size, rust_python_times, label = \"Rust-Python\")\n",
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"plt.xscale(\"log\")\n",
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"plt.yscale(\"log\")\n",
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"plt.legend()\n",
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"plt.title(\"Runtime Performance on Matrix Benchmark (Log-Log)\")\n",
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"plt.ylabel(\"Runtime (Log(seconds))\")\n",
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"plt.xlabel(\"Log(Matrix size)\")\n",
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"plt.savefig('benchmark.png')"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "68a5392b-6bd0-4aa6-94db-763579611feb",
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"metadata": {},
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"outputs": [],
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"source": [
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"rust_python_times/numpy_times"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "c3c9b488-ff19-4d78-8606-2d91a4ab3c98",
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"metadata": {},
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"outputs": [],
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"source": [
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"python_times/rust_python_times"
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]
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}
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],
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"metadata": {
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"kernelspec": {
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"display_name": "Python 3 (ipykernel)",
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"language": "python",
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"name": "python3"
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},
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"language_info": {
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"codemirror_mode": {
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"name": "ipython",
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"version": 3
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},
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"file_extension": ".py",
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"mimetype": "text/x-python",
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.13.4"
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}
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},
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"nbformat": 4,
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"nbformat_minor": 5
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}

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