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USE SIMPLE PYTHON CODE

1) **Basic Version with Two Levels of Processes (Master and Slaves):**

- One master process aggregates and sums the results of *n* slave processes.
- Each slave process sums a different range of values in an array of 1000 random integers. (Program to generate 1000 integers to populate the array).
- The number of slave processes is a user-defined parameter.
- Example: If the user chooses 4 slave processes, each slave process will sum 1000/4 = 250 numbers.
- Example: If the user chooses 3 slave processes, the first two may each sum 333 numbers, and the third slave process sums the remaining 334 numbers.

2) **Advanced Version with More Than Two Levels of Processes:**

- The master process creates two slave processes, where each slave process is responsible for summing half of the array.
- Each slave process forks/spawns two new slave processes. Each new slave process sums half of the array segment received by its parent.
- Each slave process returns its subtotal to its parent process, and the parent process aggregates and returns the total to its parent process.
- Start with a 7-node process tree. When comfortable, you can extend it to a full process tree.

Answer :

Final answer:

To create a process tree in Python with different levels of processes, you can use the `multiprocessing` module. The basic version involves a master process and multiple slave processes, where each slave process sums a different range of values in an array. The advanced version extends this structure with more levels of processes, creating a hierarchical tree. The `multiprocessing` module allows for parallel processing and efficient computation. Here is an example code:

  1. Import the `multiprocessing` module
  2. Create functions for the slave and master processes
  3. Create an array of random integers
  4. Get the number of slave processes from the user
  5. Call the master process function
  6. Print the result

This code creates a process tree and calculates the sum of the array using parallel processing.

Explanation:

To create a process tree in Python with different levels of processes, we can use the `multiprocessing` module. Let's start with the basic version:

  1. Import the `multiprocessing` module: import multiprocessing
  2. Create a function that sums a range of values in an array:
    def sum_range(arr, start, end):
    return sum(arr[start:end])
  3. Create a function for the slave process:
    def slave_process(arr, start, end):
    result = sum_range(arr, start, end)
    return result
  4. Create a function for the master process:
    def master_process(arr, num_slaves):
    chunk_size = len(arr) // num_slaves
    processes = []
    for i in range(num_slaves):
    start = i * chunk_size
    end = (i + 1) * chunk_size if i != num_slaves - 1 else len(arr)
    p = multiprocessing.Process(target=slave_process, args=(arr, start, end))
    processes.append(p)
    p.start()
    results = []
    for p in processes:
    p.join()
    results.append(p.exitcode)
    total = sum(results)
    return total
  5. Create an array of 1000 random integers:
    import random
    arr = [random.randint(1, 100) for _ in range(1000)]
  6. Get the number of slave processes from the user:
    num_slaves = int(input('Enter the number of slave processes: '))
  7. Call the master process function:
    result = master_process(arr, num_slaves)
  8. Print the result:
    print('The sum of the array is:', result)

This code creates a process tree with a master process and multiple slave processes. Each slave process sums a different range of values in the array, and the master process aggregates the results of the slave processes to calculate the total sum of the array.

For the advanced version with more than two levels of processes, we can modify the code to create a hierarchical structure:

  1. Extend the `slave_process` function to spawn new processes:
    def slave_process(arr, start, end):
    if end - start <= 1:
    return sum_range(arr, start, end)
    mid = (start + end) // 2
    p1 = multiprocessing.Process(target=slave_process, args=(arr, start, mid))
    p2 = multiprocessing.Process(target=slave_process, args=(arr, mid, end))
    p1.start()
    p2.start()
    p1.join()
    p2.join()
    subtotal1 = p1.exitcode
    subtotal2 = p2.exitcode
    return subtotal1 + subtotal2
  2. Modify the `master_process` function to handle the new structure:
    def master_process(arr, num_slaves):
    chunk_size = len(arr) // num_slaves
    processes = []
    for i in range(num_slaves):
    start = i * chunk_size
    end = (i + 1) * chunk_size if i != num_slaves - 1 else len(arr)
    p = multiprocessing.Process(target=slave_process, args=(arr, start, end))
    processes.append(p)
    p.start()
    results = []
    for p in processes:
    p.join()
    results.append(p.exitcode)
    total = sum(results)
    return total

This modified code creates a process tree with multiple levels of processes. Each slave process spawns two new slave processes, which in turn spawn more processes until the segments of the array are small enough to be summed directly. The subtotals are returned to their parent processes, and the master process aggregates the subtotals to calculate the total sum of the array.

Learn more about creating a process tree in python here:

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