Copy and Paste  -   An Application to Copy/Paste Text
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S.No 5728 Name ..... Date/Time 20-Jun-2023 08:38:08 AM Copy text from below import numpy as np N = 100 # Size of the matrices # Generate random matrices A and B A = np.random.rand(N, N) B = np.random.rand(N, N) # Sequential matrix multiplication def matrix_multiplication(A, B): rows_A, cols_A = A.shape rows_B, cols_B = B.shape # Check if matrices can be multiplied if cols_A != rows_B: raise ValueError("Matrices dimensions are not compatible for multiplication.") # Initialize resulting matrix C C = np.zeros((rows_A, cols_B)) # Perform matrix multiplication for i in range(rows_A): for j in range(cols_B): for k in range(cols_A): C[i][j] += A[i][k] * B[k][j] return C # Perform matrix multiplication using sequential algorithm C_seq = matrix_multiplication(A, B) # Print the resulting matrix C print("Sequential matrix multiplication:") print(C_seq) from mpi4py import MPI # Initialize MPI comm = MPI.COMM_WORLD num_processes = comm.Get_size() rank = comm.Get_rank() # Scatter matrix A to all other processes if rank == 0: scattered_A = np.array_split(A, num_processes) else: scattered_A = None # Receive the scattered portion of matrix A local_A = comm.scatter(scattered_A, root=0) # Print the received portion of matrix A on each process print("Rank", rank, "received portion of matrix A:") print(local_A) # Scatter matrix B to all processes scattered_B = comm.bcast(B, root=0) # Print the received portion of matrix B on each process print("Rank", rank, "received portion of matrix B:") print(scattered_B) # Perform local matrix multiplication local_C = matrix_multiplication(local_A, scattered_B) # Print the resulting local matrix on each process print("Rank", rank, "local matrix multiplication result:") print(local_C)# Reduce the local matrix portions using MPI_Reduce with the MPI_SUM operation reduced_C = comm.reduce(local_C, op=MPI.SUM, root=0) # Print the resulting matrix C on process rank 0 if rank == 0: print("Parallel matrix multiplication result:") print(reduced_C) import time # Measure the execution time of sequential matrix multiplication start_time = time.time() C_seq = matrix_multiplication(A, B) end_time = time.time() seq_execution_time = end_time - start_time # Measure the execution time of parallel matrix multiplication start_time = time.time() local_C = matrix_multiplication(local_A, scattered_B) reduced_C = comm.reduce(local_C, op=MPI.SUM, root=0) end_time = time.time() parallel_execution_time = end_time - start_time # Print the execution times if rank == 0: print("Sequential execution time:", seq_execution_time) print("Parallel execution time:", parallel_execution_time) comments powered by Disqus NEW ENTRIES S.No Name Entry Time/Date 5844 BMP 31-Oct-2023 06:41:06 PM 5843 ab 29-Oct-2023 08:51:02 PM 5842 a 29-Oct-2023 08:44:33 PM 5841 c++ program 29-Oct-2023 08:42:28 PM 5840 Child Vids 26-Oct-2023 07:07:04 AM 5839 23-Oct-2023 08:11:25 PM 5838 22-Oct-2023 06:29:37 PM 5837 Organic Loom 17-Oct-2023 07:02:03 PM 5836 Test 1 17-Oct-2023 11:16:30 AM 5835 PAF-IAST MET 17-Oct-2023 11:15:49 AM 5834 for jazz 16-Oct-2023 09:26:26 PM 5833 New Kid 16-Oct-2023 07:08:13 AM 5832 test 12-Oct-2023 10:38:09 AM 5831 a 12-Oct-2023 10:28:26 AM 5830 75 Videos 12-Oct-2023 09:32:23 AM 5829 11-Oct-2023 05:45:55 PM 5828 q44 10-Oct-2023 05:47:12 PM 5827 q4 10-Oct-2023 05:40:56 PM 5826 jflex q3 10-Oct-2023 05:37:43 PM 5825 q3 10-Oct-2023 05:33:13 PM 5824 q2 10-Oct-2023 05:32:15 PM 5823 jflex 10-Oct-2023 05:30:32 PM 5822 dddd 10-Oct-2023 04:41:21 PM 5821 nn 09-Oct-2023 11:55:27 AM 5820 Jarvo 09-Oct-2023 11:44:19 AM 5819 NEW CHILD 07-Oct-2023 11:16:57 AM 5818 05-Oct-2023 06:32:33 PM 5817 LandscapeLay 05-Oct-2023 11:42:00 AM 5813 responsive 02-Oct-2023 11:44:41 AM 5806 lab task 26-Sep-2023 05:28:21 PM [First] [Prev] 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | 9 | 10 [Next] [Last]
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