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import tensorflow as tf\r\n\r\n# Create a large matrix\r\nmatrix_size = 10000\r\nmatrix = tf.random.normal([matrix_size, matrix_size])\r\n\r\n# Perform matrix multiplication\r\nresult = tf.matmul(matrix, matrix)\r\n\r\n# Run on GPU\r\nwith tf.device('\/GPU:0'):\r\n    gpu_result = tf.matmul(matrix, matrix)\r\n\r\nprint(f\"CPU time: {tf.test.compute_gradient(lambda: tf.matmul(matrix, matrix))[1]}\")\r\nprint(f\"GPU time: {tf.test.compute_gradient(lambda: gpu_result)[1]}\")\r\n<\/code><\/pre>\n
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#include <mpi.h>\r\n#include <iostream>\r\n\r\nint main(int argc, char** argv) {\r\n    MPI_Init(&argc, &argv);\r\n\r\n    int world_size;\r\n    MPI_Comm_size(MPI_COMM_WORLD, &world_size);\r\n\r\n    int world_rank;\r\n    MPI_Comm_rank(MPI_COMM_WORLD, &world_rank);\r\n\r\n    \/\/ Simulate CFD calculation\r\n    double local_result = complex_cfd_calculation(world_rank);\r\n\r\n    double global_result;\r\n    MPI_Reduce(&local_result, &global_result, 1, MPI_DOUBLE, MPI_SUM, 0, MPI_COMM_WORLD);\r\n\r\n    if (world_rank == 0) {\r\n        std::cout << \"Final result: \" << global_result << std::endl;\r\n    }\r\n\r\n    MPI_Finalize();\r\n    return 0;\r\n}\r\n<\/code><\/pre>\n
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