Solve the matrix multiplication problem using the parallel models.

To solve the matrix multiplication problem using parallel computing models, we'll explore several approaches. Matrix multiplication is a fundamental operation in various scientific and engineering applications, and optimizing its computation is essential for improving performance. Parallel computing offers opportunities to expedite this process by distributing the workload across multiple processing units. In this answer, we'll delve into different parallel models and how they can be applied to matrix multiplication.

1. Shared Memory Parallelism (SMP/Multi-threading):
   In shared memory parallelism, multiple threads operate on shared memory. Each thread is assigned a portion of the matrix to compute the resulting matrix's elements. This approach is suitable for systems with multiple CPU cores.

   ```python
   import numpy as np
   import threading

   def matrix_multiply_shared(A, B, result, start_row, end_row):
       for i in range(start_row, end_row):
           for j in range(B.shape[1]):
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