forked from Alexey/DAS_2023_1
71 lines
2.1 KiB
Python
71 lines
2.1 KiB
Python
from flask import Flask, render_template, request
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import numpy as np
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import concurrent.futures
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app = Flask(__name__)
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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("Incompatible matrix dimensions for multiplication")
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result = [[0 for _ in range(len(matrix_b[0]))] for _ in range(len(matrix_a))]
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for i in range(len(matrix_a)):
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for j in range(len(matrix_b[0])):
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for k in range(len(matrix_b)):
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result[i][j] += matrix_a[i][k] * matrix_b[k][j]
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return result
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def multiply_matrices_parallel(matrix_a, matrix_b, num_threads):
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if len(matrix_a[0]) != len(matrix_b):
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raise ValueError("Incompatible matrix dimensions for multiplication")
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result = [[0 for _ in range(len(matrix_b[0]))] for _ in range(len(matrix_a))]
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with concurrent.futures.ThreadPoolExecutor(max_workers=num_threads) as executor:
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futures = []
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for i in range(len(matrix_a)):
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futures.append(executor.submit(_multiply_row, matrix_a, matrix_b, i))
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for i, future in enumerate(concurrent.futures.as_completed(futures)):
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result[i] = future.result()
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return result
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def _multiply_row(matrix_a, matrix_b, i):
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row_result = [0 for _ in range(len(matrix_b[0]))]
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for j in range(len(matrix_b[0])):
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for k in range(len(matrix_b)):
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row_result[j] += matrix_a[i][k] * matrix_b[k][j]
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return row_result
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@app.route('/')
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def index():
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return render_template('index.html')
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@app.route('/multiply', methods=['POST'])
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def multiply():
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n = int(request.form.get('matrix_a'))
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matrix_a = np.random.randint(10, size=(n, n))
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matrix_b = np.random.randint(10, size=(n, n))
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operation_type = request.form.get('operation_type')
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if operation_type == 'sequential':
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result = multiply_matrices(matrix_a, matrix_b)
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elif operation_type == 'parallel':
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result = multiply_matrices_parallel(matrix_a, matrix_b)
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else:
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return "Invalid operation type"
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return render_template('result.html', matrix_a=matrix_a, matrix_b=matrix_b, result=result)
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if __name__ == '__main__':
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app.run(debug=True)
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