antonov_dmitry_lab_5/benchmark.py

@@ -1,81 +0,0 @@
-import numpy as np
-import time
-import concurrent.futures
-
-
-def multiply_matrices(matrix_a, matrix_b):
-    if len(matrix_a[0]) != len(matrix_b):
-        raise ValueError("Incompatible matrix dimensions for multiplication")
-
-    result = [[0 for _ in range(len(matrix_b[0]))] for _ in range(len(matrix_a))]
-
-    for i in range(len(matrix_a)):
-        for j in range(len(matrix_b[0])):
-            for k in range(len(matrix_b)):
-                result[i][j] += matrix_a[i][k] * matrix_b[k][j]
-
-    return result
-
-
-def multiply_matrices_parallel(matrix_a, matrix_b, num_threads):
-    if len(matrix_a[0]) != len(matrix_b):
-        raise ValueError("Incompatible matrix dimensions for multiplication")
-
-    result = [[0 for _ in range(len(matrix_b[0]))] for _ in range(len(matrix_a))]
-
-    with concurrent.futures.ThreadPoolExecutor(max_workers=num_threads) as executor:
-        futures = []
-        for i in range(len(matrix_a)):
-            futures.append(executor.submit(_multiply_row, matrix_a, matrix_b, i))
-
-        for i, future in enumerate(concurrent.futures.as_completed(futures)):
-            result[i] = future.result()
-
-    return result
-
-
-def _multiply_row(matrix_a, matrix_b, i):
-    row_result = [0 for _ in range(len(matrix_b[0]))]
-    for j in range(len(matrix_b[0])):
-        for k in range(len(matrix_b)):
-            row_result[j] += matrix_a[i][k] * matrix_b[k][j]
-    return row_result
-
-
-def benchmark_sequential(size):
-    matrix_a = np.random.rand(size, size)
-    matrix_b = np.random.rand(size, size)
-
-    start_time = time.time()
-    multiply_matrices(matrix_a, matrix_b)
-    end_time = time.time()
-
-    return end_time - start_time
-
-
-def benchmark_parallel(size, num_threads):
-    matrix_a = np.random.rand(size, size)
-    matrix_b = np.random.rand(size, size)
-
-    start_time = time.time()
-    multiply_matrices_parallel(matrix_a, matrix_b, num_threads)
-    end_time = time.time()
-
-    return end_time - start_time
-
-
-if __name__ == "__main__":
-    sizes = [300]
-    threads = [2, 8]
-
-    for size in sizes:
-        sequential_time = benchmark_sequential(size)
-        print(f"Время обычное: {sequential_time} с")
-        print(f"Размер матрицы: {size}x{size}")
-
-    for thread in threads:
-        for size in sizes:
-            parallel_time = benchmark_parallel(size, thread)
-            print(f"Размер матрицы: {size}x{size}")
-            print(f"Время параллельное: {parallel_time} с")
-            print(f"Потоков:  {thread}")

antonov_dmitry_lab_5/simple_app.py

@@ -1,70 +0,0 @@
-from flask import Flask, render_template, request
-import numpy as np
-import concurrent.futures
-
-app = Flask(__name__)
-
-
-def multiply_matrices(matrix_a, matrix_b):
-    if len(matrix_a[0]) != len(matrix_b):
-        raise ValueError("Incompatible matrix dimensions for multiplication")
-
-    result = [[0 for _ in range(len(matrix_b[0]))] for _ in range(len(matrix_a))]
-
-    for i in range(len(matrix_a)):
-        for j in range(len(matrix_b[0])):
-            for k in range(len(matrix_b)):
-                result[i][j] += matrix_a[i][k] * matrix_b[k][j]
-
-    return result
-
-def multiply_matrices_parallel(matrix_a, matrix_b, num_threads):
-    if len(matrix_a[0]) != len(matrix_b):
-        raise ValueError("Incompatible matrix dimensions for multiplication")
-
-    result = [[0 for _ in range(len(matrix_b[0]))] for _ in range(len(matrix_a))]
-
-    with concurrent.futures.ThreadPoolExecutor(max_workers=num_threads) as executor:
-        futures = []
-        for i in range(len(matrix_a)):
-            futures.append(executor.submit(_multiply_row, matrix_a, matrix_b, i))
-
-        for i, future in enumerate(concurrent.futures.as_completed(futures)):
-            result[i] = future.result()
-
-    return result
-
-def _multiply_row(matrix_a, matrix_b, i):
-    row_result = [0 for _ in range(len(matrix_b[0]))]
-    for j in range(len(matrix_b[0])):
-        for k in range(len(matrix_b)):
-            row_result[j] += matrix_a[i][k] * matrix_b[k][j]
-    return row_result
-
-
-
-@app.route('/')
-def index():
-    return render_template('index.html')
-
-
-@app.route('/multiply', methods=['POST'])
-def multiply():
-    n = int(request.form.get('matrix_a'))
-    matrix_a = np.random.randint(10, size=(n, n))
-    matrix_b = np.random.randint(10, size=(n, n))
-
-    operation_type = request.form.get('operation_type')
-
-    if operation_type == 'sequential':
-        result = multiply_matrices(matrix_a, matrix_b)
-    elif operation_type == 'parallel':
-        result = multiply_matrices_parallel(matrix_a, matrix_b)
-    else:
-        return "Invalid operation type"
-
-    return render_template('result.html', matrix_a=matrix_a, matrix_b=matrix_b, result=result)
-
-
-if __name__ == '__main__':
-    app.run(debug=True)

antonov_dmitry_lab_5/templates/index.html

@@ -1,24 +0,0 @@
-<!DOCTYPE html>
-<html lang="en">
-<head>
-    <meta charset="UTF-8">
-    <meta http-equiv="X-UA-Compatible" content="IE=edge">
-    <meta name="viewport" content="width=device-width, initial-scale=1.0">
-    <title>Matrix Multiplication</title>
-</head>
-<body>
-    <h1>Matrix Multiplication</h1>
-    <form action="/multiply" method="post">
-        <label for="matrix_a">Matrix размер:</label>
-        <input type="text" name="matrix_a" required><br>
-
-        <label for="operation_type">Как умножить:</label>
-        <select name="operation_type">
-            <option value="sequential">Последовательно</option>
-            <option value="parallel">Параллельно</option>
-        </select><br>
-
-        <button type="submit">Умножение матриц</button>
-    </form>
-</body>
-</html>

antonov_dmitry_lab_5/templates/result.html

@@ -1,19 +0,0 @@
-<!DOCTYPE html>
-<html lang="en">
-<head>
-    <meta charset="UTF-8">
-    <meta http-equiv="X-UA-Compatible" content="IE=edge">
-    <meta name="viewport" content="width=device-width, initial-scale=1.0">
-    <title>Matrix Multiplication Result</title>
-</head>
-<body>
-    <h1>Результат</h1>
-    <p>Matrix A:</p>
-    <pre>{{ matrix_a }}</pre>
-    <p>Matrix B:</p>
-    <pre>{{ matrix_b }}</pre>
-    <p>Result:</p>
-    <pre>{{ result }}</pre>
-    <a href="/">Назад</a>
-</body>
-</html>