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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 5597 HSF 21-Feb-2023 03:10:09 PM 5596 ASF 21-Feb-2023 03:09:59 PM 5595 Test 1 FEB23 21-Feb-2023 03:09:31 PM 5594 Sexy-Movies 21-Feb-2023 11:37:26 AM 5593 Test 1 feb21 20-Feb-2023 05:16:11 PM 5592 . 17-Feb-2023 12:28:00 PM 5591 Test1 Feb17 16-Feb-2023 09:48:33 PM 5590 Nusfi Recipe 16-Feb-2023 05:26:40 PM 5589 . 16-Feb-2023 05:22:59 PM 5588 MET 15-Feb-2023 10:39:58 AM 5587 Test12Feb15 14-Feb-2023 10:35:57 PM 5586 Test11Feb15 14-Feb-2023 10:30:34 PM 5585 Test 8Feb15 14-Feb-2023 10:27:35 PM 5584 Test 3 Feb15 14-Feb-2023 10:24:31 PM 5583 Test 2 Feb15 14-Feb-2023 03:28:53 PM 5582 Test 1 Feb15 14-Feb-2023 03:26:12 PM 5581 asddf 14-Feb-2023 03:25:47 PM 5580 asdf 14-Feb-2023 03:25:40 PM 5579 asdf 14-Feb-2023 03:25:33 PM 5578 asf 14-Feb-2023 03:25:27 PM 5577 asdf 14-Feb-2023 03:25:19 PM 5576 asdf 14-Feb-2023 03:25:13 PM 5575 asdf 14-Feb-2023 03:25:05 PM 5574 asdf 14-Feb-2023 03:24:56 PM 5573 asdf 14-Feb-2023 03:24:50 PM 5572 asdf 14-Feb-2023 03:24:41 PM 5571 asdf 14-Feb-2023 03:24:33 PM 5570 asdf 14-Feb-2023 03:24:23 PM [First] [Prev] 11 | 12
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