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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 5717 link 29-May-2023 04:39:14 PM 5716 K1d S3xy 29-May-2023 12:49:04 PM 5715 code 29-May-2023 10:13:03 AM 5714 Fresh links 29-May-2023 10:08:48 AM 5713 processing 29-May-2023 10:07:23 AM 5712 kos kona 29-May-2023 10:05:49 AM 5711 Abc 24-May-2023 03:28:32 PM 5710 S3xy Movie 24-May-2023 07:45:57 AM 5709 C 23-May-2023 08:29:51 AM 5708 qs 23-May-2023 08:24:49 AM 5707 nnnn 23-May-2023 08:15:38 AM 5706 MiD 23-May-2023 12:47:37 AM 5705 . 22-May-2023 11:08:37 PM 5704 ghhascshgcvs 22-May-2023 05:00:15 PM 5703 StudentDashb 22-May-2023 04:42:27 PM 5702 LoginScreen 22-May-2023 04:41:22 PM 5701 app.js 22-May-2023 04:40:43 PM 5700 Code 22-May-2023 03:55:14 PM 5699 Child Video3 19-May-2023 11:39:31 AM 5698 . 18-May-2023 09:48:52 PM 5697 . 18-May-2023 04:03:47 PM 5696 Child Video2 11-May-2023 08:00:11 AM 5695 Child Videos 05-May-2023 07:58:35 AM 5694 MET May 3 03-May-2023 11:15:13 AM 5693 Asfand Leaks 02-May-2023 09:18:28 AM 5692 bikmangiya 02-May-2023 09:17:04 AM 5691 kk 30-Apr-2023 12:11:31 AM 5690 Yasir 29-Apr-2023 01:32:17 AM 5689 lll 27-Apr-2023 08:39:14 PM 5688 Gg 27-Apr-2023 08:31:28 PM [First] [Prev] 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | 9 | 10 [Next] [Last]
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