DAS_2023_1/antonov_dmitry_lab_5/simple_app.py

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import multiprocessing
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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):
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if len(matrix_a[0]) != len(matrix_b):
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raise ValueError("матрицы имеют разную длину")
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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
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def multiply_row(args):
matrix_a, matrix_b, i = args
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, i
def multiply_matrices_parallel(matrix_a, matrix_b, threads):
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if len(matrix_a[0]) != len(matrix_b):
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raise ValueError("матрицы имеют разную длину")
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result = [[0 for _ in range(len(matrix_b[0]))] for _ in range(len(matrix_a))]
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with multiprocessing.Pool(processes=threads) as pool:
args_list = [(matrix_a, matrix_b, i) for i in range(len(matrix_a))]
rows_results = pool.map(multiply_row, args_list)
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for row_result, row_index in rows_results:
result[row_index] = row_result
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return 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':
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result = multiply_matrices_parallel(matrix_a, matrix_b, 16)
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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)