Copy and Paste  -   An Application to Copy/Paste Text
HOME SEARCH ENTRY Go to www.pakproject.com
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 5805 q 4 26-Sep-2023 05:19:19 PM 5804 111 26-Sep-2023 04:45:42 PM 5803 comp 26-Sep-2023 04:14:07 PM 5802 COlor 25-Sep-2023 03:03:06 PM 5801 islamabad 25-Sep-2023 02:56:47 PM 5800 Ikack 25-Sep-2023 02:27:21 PM 5799 Blue Green n 25-Sep-2023 02:01:07 PM 5798 Jask 25-Sep-2023 01:59:04 PM 5797 Blue Green 25-Sep-2023 01:59:02 PM 5796 fire-wood-wa 25-Sep-2023 12:49:39 PM 5795 6001 25-Sep-2023 11:54:34 AM 5794 23-Sep-2023 08:28:04 PM 5793 Child Hot 22-Sep-2023 11:53:03 AM 5792 dfd 22-Sep-2023 10:42:45 AM 5791 Codeeee 22-Sep-2023 09:44:28 AM 5790 HEC 22-Sep-2023 09:41:20 AM 5789 llllllllll 22-Sep-2023 09:20:11 AM 5788 Man 22-Sep-2023 08:58:04 AM 5787 ssss 22-Sep-2023 08:55:24 AM 5786 2 21-Sep-2023 10:40:17 AM 5785 fire wood 21-Sep-2023 10:34:52 AM 5784 calculator 21-Sep-2023 10:09:45 AM 5783 t1 21-Sep-2023 10:02:23 AM 5782 test 21-Sep-2023 09:39:22 AM 5781 design_new 21-Sep-2023 09:09:57 AM 5780 calc_design 21-Sep-2023 08:59:28 AM 5779 stepsis 19-Sep-2023 05:53:02 PM 5778 good 19-Sep-2023 05:51:44 PM 5777 Button 18-Sep-2023 02:31:47 PM 5776 Step1 18-Sep-2023 01:53:15 PM [First] [Prev] 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | 9 | 10 [Next] [Last]
web counter To report any error messages or bugs, or other issues, please send email at: info@pakproject.com