update

2025.03.09_matrix_running_time_for_different_num_of_cpu_cores/make_qsub_files.py

@@ -1,7 +1,7 @@
 import guan  # https://py.guanjihuan.com | install: pip install --upgrade guan
 import numpy as np
 
-cpu_num_array = np.arange(1, 11)
+cpu_num_array = np.arange(1, 17)
 
 sh_filename = 'task'
 task_name = 'test'

2025.03.09_matrix_running_time_for_different_num_of_cpu_cores/matrix_running_time_for_different_num_of_cpu_cores.py

@@ -12,42 +12,42 @@ B = np.random.rand(n, n)
 
 # 矩阵行列式
 start_time = time.time()
-for _ in range(10):
+for _ in range(20):
     det_A = np.linalg.det(A)
-det_time = (time.time() - start_time)/10
+det_time = (time.time() - start_time)/20
 print(f"矩阵行列式时间: {det_time:.3f} 秒")
 
 # 矩阵乘法
 start_time = time.time()
-for _ in range(10):
+for _ in range(20):
     C = np.dot(A, B)
-multiply_time = (time.time() - start_time)/10
+multiply_time = (time.time() - start_time)/20
 print(f"矩阵乘法时间: {multiply_time:.3f} 秒")
 
 # 矩阵求逆
 start_time = time.time()
-for _ in range(10):
+for _ in range(20):
     inv_A = np.linalg.inv(A)
-inv_time = (time.time() - start_time)/10
+inv_time = (time.time() - start_time)/20
 print(f"矩阵求逆时间: {inv_time:.3f} 秒")
 
 # 矩阵的秩
 start_time = time.time()
-for _ in range(10):
+for _ in range(20):
     rank_A = np.linalg.matrix_rank(A)
-rank_time = (time.time() - start_time)/10
+rank_time = (time.time() - start_time)/20
 print(f"矩阵的秩时间: {rank_time:.3f} 秒")
 
 # 矩阵的特征值
 start_time = time.time()
-for _ in range(10):
+for _ in range(20):
     eigenvalues_A = np.linalg.eigvals(A)
-eigen_time = (time.time() - start_time)/10
+eigen_time = (time.time() - start_time)/20
 print(f"矩阵特征值时间: {eigen_time:.3f} 秒")
 
 # 矩阵的特征值和特征向量
 start_time = time.time()
-for _ in range(10):
+for _ in range(20):
     eigenvalues_A, eigenvector_A = np.linalg.eig(A)
-eigen_time = (time.time() - start_time)/10
+eigen_time = (time.time() - start_time)/20
 print(f"矩阵特征值和特征向量时间: {eigen_time:.3f} 秒")

2025.03.09_matrix_running_time_for_different_num_of_cpu_cores/plot_result_of_running_time.py

@@ -0,0 +1,32 @@
+import matplotlib.pyplot as plt
+from matplotlib.ticker import MultipleLocator
+import numpy as np
+
+cpu_num_array = np.arange(1, 17)
+
+time_array_1 = [3.999, 1.679, 1.257, 0.985, 0.699, 0.562, 0.534, 0.525, 0.510, 0.424, 0.409, 0.380, 0.372, 0.339, 0.334, 0.293]
+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.show()
+
+time_array_2 = [6.059, 2.723, 2.190, 1.577, 1.169, 0.939, 1.231, 0.958, 1.070, 0.793, 1.208, 0.760, 0.709, 0.677, 0.671, 0.906]
+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.show()
+
+time_array_3 = [95.896, 49.107, 41.703, 38.915, 32.468, 25.804, 40.716, 31.072, 40.045, 26.397, 38.557, 28.684, 28.267, 26.840, 27.381, 34.494]
+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.show()

2025.03.09_matrix_running_time_for_different_num_of_cpu_cores/qsub_task.py

@@ -1,7 +1,7 @@
 import numpy as np
 import os
 
-cpu_num_array = np.arange(1, 11)
+cpu_num_array = np.arange(1, 17)
 
 for cpu_num in cpu_num_array:
     os.system(f'qsub task_{cpu_num}.sh')