Merge pull request '[Л/Р 6] Никифорова Татьяна' (#141) from tanya_nik16/distributed-computing:Nikiforova-TS-Lab-6 into main

Reviewed-on: http://student.git.athene.tech/v.moiseev/distributed-computing/pulls/141

tasks/nikiforova-ts/Lab_6/.gitignore

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tasks/nikiforova-ts/Lab_6/Readme.md

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+# Отчёт по лабораторной работе №6
+
+Выполнила: студентка гр. ИСЭбд-41 Никифорова Татьяна Сергеевна.
+
+## Создание приложения
+
+Выбрала язык Python.
+
+Запустим алгоритм с матрицей 3x3 c различными алгоритмами.
+
+![](picture/1.png) 
+![](picture/2.png)
+
+## Бенчмарки
+
+Протекстируем обычный и паралелльный с различными размерами матриц.
+
+Матрица 5х5
+
+![](picture/31.png) 
+![](picture/41.png)
+
+Матрица 8х8
+
+![](picture/3.png) 
+![](picture/4.png)
+
+
+Вывод: Скоростные возможности параллельного алгоритма проявляются только при значительном количестве операций. В случае, если операций меньше, стандартный алгоритм может оказаться более быстрым.
+
+
+
+
+

tasks/nikiforova-ts/Lab_6/work/Lab6.py

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+import tkinter as tk
+from tkinter import ttk
+import numpy as np
+from multiprocessing import cpu_count, Pool
+import time
+
+class DeterminantCalculator:
+    def __init__(self, root):
+        self.root = root
+        self.root.title("Determinant Calculator")
+
+        self.matrix_size_label = ttk.Label(root, text="Matrix Size:")
+        self.matrix_size_label.grid(row=0, column=0, padx=10, pady=10)
+
+        self.matrix_size_entry = ttk.Entry(root)
+        self.matrix_size_entry.grid(row=0, column=1, padx=10, pady=10)
+
+        self.algorithm_label = ttk.Label(root, text="Algorithm:")
+        self.algorithm_label.grid(row=1, column=0, padx=10, pady=10)
+
+        self.algorithm_var = tk.StringVar()
+        self.algorithm_var.set("Normal")
+        self.algorithm_menu = ttk.Combobox(root, textvariable=self.algorithm_var, values=["Normal", "Parallel"])
+        self.algorithm_menu.grid(row=1, column=1, padx=10, pady=10)
+
+        self.num_threads_label = ttk.Label(root, text="Number of Threads:")
+        self.num_threads_label.grid(row=2, column=0, padx=10, pady=10)
+
+        self.num_threads_entry = ttk.Entry(root)
+        self.num_threads_entry.insert(0, str(cpu_count()))
+        self.num_threads_entry.grid(row=2, column=1, padx=10, pady=10)
+
+        self.generate_and_calculate_button = ttk.Button(root, text="Generate Matrix and Calculate Determinant", command=self.generate_and_calculate_determinant)
+        self.generate_and_calculate_button.grid(row=3, column=0, columnspan=2, pady=10)
+
+        self.result_label = ttk.Label(root, text="Result:")
+        self.result_label.grid(row=4, column=0, padx=10, pady=10)
+
+        self.time_label = ttk.Label(root, text="Time (s):")
+        self.time_label.grid(row=5, column=0, padx=10, pady=10)
+
+        self.matrix_label = ttk.Label(root, text="")
+        self.matrix_label.grid(row=6, column=0, columnspan=2, pady=10)
+
+        self.det_result = None  # Глобальная переменная для хранения результата
+
+    def generate_matrix(self, size):
+        matrix = np.random.randint(1, 10, size=(size, size))
+        return matrix
+
+    def show_random_matrix(self, matrix):
+        self.matrix_label.config(text=f"Random Matrix:\n{matrix}")
+
+    def calculate_determinant_parallel(self, matrix, num_threads):
+        size = len(matrix)
+
+        if size == 1:
+            return matrix[0, 0], 0
+
+        result = RawArray('d', [0.0])  # Разделяемый массив для хранения результата
+        lock_object = np.ones(1)  # Использование numpy для создания объекта блокировки
+
+        def calculate_submatrix(i):
+            nonlocal result
+            sub_matrix = self.get_submatrix(matrix, i)
+            sub_determinant = matrix[0, i] * self.calculate_determinant(sub_matrix)
+
+            with lock_object.get_lock():
+                result[0] += (-1) ** i * sub_determinant
+
+        with Pool(processes=num_threads) as pool:
+            start_time = time.time()
+            pool.map(calculate_submatrix, range(size))
+            end_time = time.time()
+
+        return result[0], end_time - start_time
+
+    # ваш существующий код
+
+    def get_submatrix(self, matrix, column_index):
+        size = len(matrix)
+        sub_matrix = np.zeros((size - 1, size - 1))
+
+        for i in range(1, size):
+            for j in range(size):
+                if j < column_index:
+                    sub_matrix[i - 1, j] = matrix[i, j]
+                elif j > column_index:
+                    sub_matrix[i - 1, j - 1] = matrix[i, j]
+
+        return sub_matrix
+
+    def calculate_determinant_group(self, matrix, row, col):
+        sign = (-1) ** (row + col)
+        minor = np.delete(np.delete(np.array(matrix), row, axis=0), col, axis=1)
+        det_minor = self.calculate_determinant(minor)
+        return sign * matrix[row][col] * det_minor
+
+    def calculate_determinant(self, matrix=None):
+        if matrix is None:
+            matrix_size = int(self.matrix_size_entry.get())
+            matrix = self.generate_matrix(matrix_size)
+
+        size = len(matrix)
+        if size == 1:
+            return matrix[0][0]
+        elif size == 2:
+            return matrix[0][0] * matrix[1][1] - matrix[0][1] * matrix[1][0]
+        else:
+            det = 0
+            for col in range(size):
+                sign = (-1) ** col
+                minor = np.delete(np.array(matrix), 0, axis=0)
+                minor = np.delete(minor, col, axis=1)
+                det += sign * matrix[0][col] * self.calculate_determinant(minor)
+            return det
+
+    def generate_and_calculate_determinant(self):
+        matrix_size = int(self.matrix_size_entry.get())
+        algorithm = self.algorithm_var.get()
+        num_threads = int(self.num_threads_entry.get())
+
+        random_matrix = self.generate_matrix(matrix_size)
+        self.show_random_matrix(random_matrix)
+
+        if algorithm == "Parallel":
+            det, time_taken = self.calculate_determinant_parallel(random_matrix, num_threads)
+        else:
+            start_time = time.time()
+            det = self.calculate_determinant(random_matrix)
+            time_taken = time.time() - start_time
+
+        result_str = f"Determinant: {det}"
+        self.result_label.config(text=result_str)
+
+        time_str = f"Time (s): {time_taken:.6f}"
+        self.time_label.config(text=time_str)
+
+        # Используем глобальную переменную для отображения результата в методе generate_and_calculate_determinant
+        self.det_result = det
+
+if __name__ == "__main__":
+    root = tk.Tk()
+    app = DeterminantCalculator(root)
+    root.mainloop()