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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 5745 VIDEO HOT 25-Jul-2023 12:26:38 PM 5744 Cute Sexy 22-Jul-2023 08:18:25 AM 5743 KIDs Sexy 17-Jul-2023 10:18:41 AM 5742 dfa 11-Jul-2023 09:08:36 AM 5741 HOT CHILD 11-Jul-2023 08:53:45 AM 5740 jj assigment 11-Jul-2023 08:43:20 AM 5739 07-Jul-2023 08:20:42 PM 5738 Kid Sexy 26-Jun-2023 09:33:43 AM 5737 mmmmm 24-Jun-2023 07:31:13 PM 5736 ....... 24-Jun-2023 07:17:01 PM 5735 Child Sexy 22-Jun-2023 10:15:56 AM 5734 Mm 20-Jun-2023 09:15:38 PM 5733 Kkk 20-Jun-2023 09:13:08 PM 5732 Ko 20-Jun-2023 08:44:44 PM 5731 bhatti 420 20-Jun-2023 10:53:59 AM 5730 Abc 20-Jun-2023 08:57:46 AM 5729 Lab 20-Jun-2023 08:53:25 AM 5728 ..... 20-Jun-2023 08:38:08 AM 5727 Best S.E.X.Y 17-Jun-2023 07:31:25 AM 5726 Aab 15-Jun-2023 09:29:28 PM 5725 a 13-Jun-2023 10:33:05 PM 5724 Child Vids 12-Jun-2023 11:21:50 AM 5723 Emi Pet 12-Jun-2023 06:50:51 AM 5722 cmd 06-Jun-2023 12:24:35 PM 5721 CHILD HOT 05-Jun-2023 09:08:42 AM 5720 Lakestone En 04-Jun-2023 05:08:53 PM 5719 . 03-Jun-2023 01:20:03 PM 5718 code 29-May-2023 05:50:57 PM 5717 link 29-May-2023 04:39:14 PM 5716 K1d S3xy 29-May-2023 12:49:04 PM [First] [Prev] 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | 9 | 10 [Next] [Last]
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