update

2025.03.09_matrix_running_time_for_different_num_of_cpu_cores/code_2/make_qsub_files.py

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+import guan  # https://py.guanjihuan.com | install: pip install --upgrade guan
+import numpy as np
+import os
+
+cpu_num_array = np.arange(1, 65)
+
+py_filename='matrix_running_time_for_different_num_of_cpu_cores_writing_into_files'
+current_directory = os.getcwd()
+
+for cpu_num in cpu_num_array:
+    guan.make_directory(f'./task_{cpu_num}')
+    os.system(f'cp ./{py_filename}.py ./task_{cpu_num}/{py_filename}.py')
+    os.system(f'cd {current_directory}/task_{cpu_num}')
+    guan.make_sh_file_for_qsub(sh_filename=f'./task_{cpu_num}/task_{cpu_num}', command_line=f'python {py_filename}.py', cpu_num=cpu_num, task_name=f'test_{cpu_num}', cd_dir=0)

2025.03.09_matrix_running_time_for_different_num_of_cpu_cores/code_2/matrix_running_time_for_different_num_of_cpu_cores_writing_into_files.py

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+"""
+This code is supported by the website: https://www.guanjihuan.com
+The newest version of this code is on the web page: https://www.guanjihuan.com/archives/45324
+"""
+
+import numpy as np
+import time
+import pickle
+
+n = 1000
+
+A = np.random.rand(n, n)
+B = np.random.rand(n, n)
+
+test_times = 20
+
+# 矩阵乘法
+start_time = time.time()
+for _ in range(test_times):
+    C = np.dot(A, B)
+multiply_time = (time.time() - start_time)/test_times
+with open(f'multiply_time_n={n}.pkl', 'wb') as f:
+    pickle.dump(multiply_time, f)
+
+# 矩阵求逆
+start_time = time.time()
+for _ in range(test_times):
+    inv_A = np.linalg.inv(A)
+inv_time = (time.time() - start_time)/test_times
+with open(f'inv_time_n={n}.pkl', 'wb') as f:
+    pickle.dump(inv_time, f)
+
+# 矩阵的特征值和特征向量
+start_time = time.time()
+for _ in range(test_times):
+    eigenvalues_A, eigenvector_A = np.linalg.eig(A)
+eigen_time = (time.time() - start_time)/test_times
+with open(f'eigen_time_n={n}.pkl', 'wb') as f:
+    pickle.dump(eigen_time, f)

2025.03.09_matrix_running_time_for_different_num_of_cpu_cores/code_2/plot_result_of_running_time_by_reading_files.py

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+import matplotlib.pyplot as plt
+# from matplotlib.ticker import MultipleLocator
+import numpy as np
+import pickle
+
+cpu_num_array = np.arange(1, 65)
+
+n = 1000
+
+time_array_1 = []
+for cpu_num in cpu_num_array:
+    with open(f'./task_{cpu_num}/multiply_time_n={n}.pkl', 'rb') as f:
+        data = pickle.load(f)
+        time_array_1.append(data)
+fig, ax = plt.subplots()
+ax.set_title('np.dot()')
+ax.set_xlabel('Number of CPU cores')
+ax.set_ylabel('Time (s)')
+# ax.xaxis.set_major_locator(MultipleLocator(1))
+plt.plot(cpu_num_array, time_array_1, '-o', )
+plt.savefig(f'multiply_time_n={n}.jpg')
+# plt.show()
+
+time_0 = time_array_1[0]
+for i0 in range(len(time_array_1)):
+    time_array_1[i0] = time_0/time_array_1[i0]
+fig, ax = plt.subplots()
+ax.set_title('np.dot()')
+ax.set_xlabel('Number of CPU cores')
+ax.set_ylabel('Ratio')
+# ax.xaxis.set_major_locator(MultipleLocator(1))
+plt.plot(cpu_num_array, time_array_1, '-o', )
+plt.plot(cpu_num_array, cpu_num_array, '--r')
+plt.savefig(f'multiply_time_ratio_n={n}.jpg')
+# plt.show()
+
+
+time_array_2 = []
+for cpu_num in cpu_num_array:
+    with open(f'./task_{cpu_num}/inv_time_n={n}.pkl', 'rb') as f:
+        data = pickle.load(f)
+        time_array_2.append(data)
+fig, ax = plt.subplots()
+ax.set_title('np.linalg.inv()')
+ax.set_xlabel('Number of CPU cores')
+ax.set_ylabel('Time (s)')
+# ax.xaxis.set_major_locator(MultipleLocator(1))
+plt.plot(cpu_num_array, time_array_2, '-o', )
+plt.savefig(f'inv_time_n={n}.jpg')
+# plt.show()
+
+time_0 = time_array_2[0]
+for i0 in range(len(time_array_2)):
+    time_array_2[i0] = time_0/time_array_2[i0]
+fig, ax = plt.subplots()
+ax.set_title('np.linalg.inv()')
+ax.set_xlabel('Number of CPU cores')
+ax.set_ylabel('Ratio')
+# ax.xaxis.set_major_locator(MultipleLocator(1))
+plt.plot(cpu_num_array, time_array_2, '-o', )
+plt.plot(cpu_num_array, cpu_num_array, '--r')
+plt.savefig(f'inv_time_ratio_n={n}.jpg')
+# plt.show()
+
+
+time_array_3 = []
+for cpu_num in cpu_num_array:
+    with open(f'./task_{cpu_num}/eigen_time_n={n}.pkl', 'rb') as f:
+        data = pickle.load(f)
+        time_array_3.append(data)
+fig, ax = plt.subplots()
+ax.set_title('np.linalg.eig()')
+ax.set_xlabel('Number of CPU cores')
+ax.set_ylabel('Time (s)')
+# ax.xaxis.set_major_locator(MultipleLocator(1))
+plt.plot(cpu_num_array, time_array_3, '-o', )
+plt.savefig(f'eigen_time_n={n}.jpg')
+# plt.show()
+
+time_0 = time_array_3[0]
+for i0 in range(len(time_array_3)):
+    time_array_3[i0] = time_0/time_array_3[i0]
+fig, ax = plt.subplots()
+ax.set_title('np.linalg.eig()')
+ax.set_xlabel('Number of CPU cores')
+ax.set_ylabel('Ratio')
+# ax.xaxis.set_major_locator(MultipleLocator(1))
+plt.plot(cpu_num_array, time_array_3, '-o', )
+plt.plot(cpu_num_array, cpu_num_array, '--r')
+plt.savefig(f'eigen_time_ratio_n={n}.jpg')
+# plt.show()

2025.03.09_matrix_running_time_for_different_num_of_cpu_cores/code_2/plot_task.sh

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+#!/bin/sh
+#PBS -N plot
+#PBS -l nodes=1:ppn=1
+python plot_result_of_running_time_by_reading_files.py

2025.03.09_matrix_running_time_for_different_num_of_cpu_cores/code_2/qsub_task.py

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+import numpy as np
+import os
+
+cpu_num_array = np.arange(1, 65)
+
+current_directory = os.getcwd()
+
+for cpu_num in cpu_num_array:
+    os.system(f'cd {current_directory}/task_{cpu_num} && qsub {current_directory}/task_{cpu_num}/task_{cpu_num}.sh')