Create Scenario1.py

Add the code and its explanation for Scenario2

Scenario1.py

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+# ************************ Written by Alireza ************************************
+import numpy as np
+import time
+
+# Define the matrix sizes
+matrix_size = 1000
+matrix_a = np.random.rand(matrix_size, matrix_size)
+matrix_b = np.random.rand(matrix_size, matrix_size)
+
+# CPU Matrix Multiplication
+def cpu_matrix_multiplication(matrix_a, matrix_b):
+    start_time = time.time()
+
+    # Perform matrix multiplication using CPU
+    cpu_result = np.dot(matrix_a, matrix_b)
+
+    end_time = time.time()
+    execution_time = end_time - start_time
+    return cpu_result, execution_time
+
+# GPU Matrix Multiplication
+def gpu_matrix_multiplication(matrix_a, matrix_b):
+    start_time = time.time()
+
+    # Perform matrix multiplication using GPU
+    gpu_matrix_a = cp.asarray(matrix_a)
+    gpu_matrix_b = cp.asarray(matrix_b)
+    gpu_result = cp.dot(gpu_matrix_a, gpu_matrix_b)
+    cpu_result = cp.asnumpy(gpu_result)
+
+    end_time = time.time()
+    execution_time = end_time - start_time
+    return cpu_result, execution_time
+
+# Run CPU Matrix Multiplication
+cpu_result, cpu_execution_time = cpu_matrix_multiplication(matrix_a, matrix_b)
+
+# Run GPU Matrix Multiplication
+gpu_result, gpu_execution_time = gpu_matrix_multiplication(matrix_a, matrix_b)
+
+# Compare the results (optional)
+print("CPU Result:")
+print(cpu_result)
+print("GPU Result:")
+print(gpu_result)
+
+# Print the execution times
+print("CPU Execution Time:", cpu_execution_time, "seconds")
+print("GPU Execution Time:", gpu_execution_time, "seconds")
+
+""" The Explanation of the Code:
+In this code, we start by defining the size of the square matrices to be multiplied (matrix_size). 
+We then generate two random matrices (matrix_a and matrix_b) using the np.random.rand function.
+
+We define two functions: cpu_matrix_multiplication and gpu_matrix_multiplication, 
+which perform matrix multiplication using CPU and GPU, respectively. 
+
+In the CPU function, we use the NumPy np.dot function for matrix multiplication. 
+In the GPU function, we use the cp.asarray function from the cupy library 
+to transfer the matrices to the GPU, perform matrix multiplication using cp.dot, 
+and then transfer the result back to the CPU using cp.asnumpy.
+
+After running the CPU and GPU matrix multiplication functions, 
+we can optionally compare the results and print the execution times.
+
+Please note that for the GPU matrix multiplication to work, 
+you need to have the cupy library installed, which provides 
+a NumPy-compatible interface for GPU computations. 
+Also, make sure you have a compatible GPU and CUDA drivers installed.
+
+Feel free to adjust the matrix size and modify the code according to your specific requirements.
+"""