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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 5628 Test 2 Mar 3 02-Mar-2023 09:34:35 PM 5627 Test 1 Mar 3 02-Mar-2023 09:31:07 PM 5626 Test 8 Mar 2 01-Mar-2023 07:58:21 PM 5625 Test 3 Mar 2 01-Mar-2023 07:57:36 PM 5624 Test 2 Mar 2 01-Mar-2023 07:56:55 PM 5623 Test 1 Mar 2 01-Mar-2023 07:56:17 PM 5622 MET March 1 28-Feb-2023 09:28:19 PM 5621 Test 5 Mar1 28-Feb-2023 09:24:26 PM 5620 Test3 Mar 1 28-Feb-2023 09:21:50 PM 5619 Test2 March1 28-Feb-2023 09:16:47 PM 5618 Test 1 Mar1 28-Feb-2023 08:35:23 PM 5617 Test 2 F28 28-Feb-2023 10:33:02 AM 5616 Test 2 Feb28 28-Feb-2023 10:22:59 AM 5615 SEXY HOT 28-Feb-2023 08:45:07 AM 5614 Test 1 Feb28 27-Feb-2023 09:41:28 PM 5613 PHD 24-Feb-2023 10:41:13 AM 5612 Test1 Feb24 23-Feb-2023 10:07:55 PM 5611 PHD 23-Feb-2023 06:39:41 PM 5610 online test 23-Feb-2023 10:37:21 AM 5609 MET 23 FEB 23-Feb-2023 10:04:42 AM 5608 Test 1 Feb23 22-Feb-2023 11:21:15 PM 5607 Test1Feb22 21-Feb-2023 03:12:58 PM 5606 PPSL 21-Feb-2023 03:11:57 PM 5605 PNG 21-Feb-2023 03:11:39 PM 5604 PCB 21-Feb-2023 03:11:28 PM 5603 PAK 21-Feb-2023 03:11:19 PM 5602 PPG 21-Feb-2023 03:11:03 PM 5601 PQR 21-Feb-2023 03:10:54 PM 5600 PPLGC 21-Feb-2023 03:10:44 PM 5599 PPKL 21-Feb-2023 03:10:36 PM [First] [Prev] 11 | 12 [Next] [Last]
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