good start2

antonov_dmitry_lab_5/benchmark.py

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+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

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+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

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+<!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

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+<!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>