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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 5776 Step1 18-Sep-2023 01:53:15 PM 5775 Hello 18-Sep-2023 01:50:17 PM 5774 123 18-Sep-2023 01:45:52 PM 5773 bijlee 18-Sep-2023 01:41:40 PM 5772 StepMom 18-Sep-2023 01:37:22 PM 5771 Fuc 18-Sep-2023 12:38:14 PM 5770 lab 18-Sep-2023 12:21:41 PM 5769 dani 18-Sep-2023 12:21:30 PM 5768 abc 18-Sep-2023 12:19:36 PM 5767 Only Boys 18-Sep-2023 12:15:25 PM 5766 test 1 16-Sep-2023 11:39:43 AM 5765 New Kiddo 16-Sep-2023 09:05:19 AM 5764 my Video 13-Sep-2023 10:14:44 AM 5763 NEW CHILD 07-Sep-2023 06:40:17 AM 5762 03-Sep-2023 10:57:01 AM 5761 Canine Lingu 02-Sep-2023 01:41:53 PM 5760 Fix Child 29-Aug-2023 06:40:56 AM 5759 27-Aug-2023 01:33:04 PM 5758 Anime 26-Aug-2023 02:35:13 PM 5757 New Child 23-Aug-2023 11:35:24 AM 5756 ee 20-Aug-2023 03:52:39 PM 5755 20-Aug-2023 10:53:49 AM 5754 Child 12-Aug-2023 06:45:08 AM 5753 SexyGirl 11-Aug-2023 09:07:17 AM 5752 11-Aug-2023 07:27:51 AM 5751 08-Aug-2023 07:02:27 PM 5750 Test 1 03-Aug-2023 01:08:58 PM 5749 CHILD HOT 03-Aug-2023 08:06:54 AM 5748 31-Jul-2023 05:20:29 PM 5747 . 30-Jul-2023 12:29:44 PM [First] [Prev] 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | 9 | 10 [Next] [Last]
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