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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 5686 Elif Yener 26-Apr-2023 03:51:34 PM 5685 cycling 26-Apr-2023 01:26:18 PM 5684 Muslim matri 23-Apr-2023 11:13:13 AM 5683 xxxxx 18-Apr-2023 08:57:31 AM 5682 Aa 16-Apr-2023 10:23:04 PM 5681 Apr 12 MET 12-Apr-2023 09:56:35 AM 5680 MET 12 Apr 12-Apr-2023 09:43:37 AM 5679 online 11-Apr-2023 01:16:47 PM 5678 MS Test 11-Apr-2023 12:59:16 PM 5677 asdf 11-Apr-2023 12:58:53 PM 5676 asdf 11-Apr-2023 12:58:38 PM 5675 asdf 11-Apr-2023 12:58:33 PM 5674 asdf 11-Apr-2023 12:58:27 PM 5673 asdf 11-Apr-2023 12:58:20 PM 5672 asdf 11-Apr-2023 12:58:14 PM 5671 asdf 11-Apr-2023 12:58:07 PM 5670 asdf 11-Apr-2023 12:57:59 PM 5669 asdf 11-Apr-2023 12:57:53 PM 5668 asdf 11-Apr-2023 12:57:45 PM 5667 asdf 11-Apr-2023 12:57:38 PM 5666 asdf 11-Apr-2023 12:57:30 PM 5665 asdf 11-Apr-2023 12:57:22 PM 5664 asdf 11-Apr-2023 12:57:14 PM 5663 asdf 11-Apr-2023 12:57:07 PM 5662 xxxx 11-Apr-2023 09:30:22 AM 5661 xxxx 11-Apr-2023 09:16:52 AM 5660 xxxx 11-Apr-2023 09:10:30 AM 5659 Geopharmarx 07-Apr-2023 10:10:22 PM 5658 ttt2 04-Apr-2023 08:53:46 AM 5657 ttt 04-Apr-2023 08:41:57 AM [First] [Prev] 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | 9 | 10 [Next] [Last]
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