main.py

import time
import numpy as np
from typing import List, Optional

import threading
import pandas as pd
import requests
import plotly.express as px


def generate_data() -> List[int]:
    """Generate some random data."""
    return np.random.randint(100, 10000, 1000).tolist()


def process1(data: List[int]) -> List[int]:
    """ given a list of integers, returns a new list of integers where each integer is the closest prime number
    to the corresponding integer in the original list that's greater than the original integer
        Args:
            data: a list of integers
        Returns:
            List[int]: a list of prime numbers that are each the closest prime number that's
            greater than each of the integers in the data list
    """
    def foo(x):
        """Find the next largest prime number."""
        while True:
            x += 1
            if all(x % i for i in range(2, x)):
                return x
    return [foo(x) for x in data]


def process2(data: List[int]) -> List[int]:
    """ given a list of integers, returns a new list of integers where each integer is the closest perfect square
    to the corresponding integer in the original list that's greater than the original integer
        Args:
            data: a list of integers
        Returns:
            List[int]: a list of perfect squares that are each the closest perfect square that's
            greater than each of the integers in the data list
    """
    def foo(x):
        """Find the next perfect square."""
        while True:
            x += 1
            if int(np.sqrt(x)) ** 2 == x:
                return x
    return [foo(x) for x in data]


def final_process(data1: List[int], data2: List[int]) -> List[int]:
    """ given two lists of integers, returns the average difference between the nth
        element in the first list and the nth element in the second list
    """
    return np.mean([x - y for x, y in zip(data1, data2)])


# TODO: Change this to the IP address of your server
offload_url = 'http://172.20.10.3:5000'
# offload_url = 'http://127.0.0.1:5000'


def run(offload: Optional[str] = None) -> float:
    """Run the program, offloading the specified function(s) to the server.

    Args:
        offload: Which function(s) to offload to the server. Can be None, 'process1', 'process2', or 'both'.

    Returns:
        float: the final result of the program.
    """
    data = generate_data()
    if offload is None:  # in this case, we run the program locally
        data1 = process1(data)
        data2 = process2(data)
    elif offload == 'process1':
        data1 = None

        def offload_process1(data):
            nonlocal data1
            # TODO: Send a POST request to the server with the input data
            response = requests.post(f"{offload_url}/process1", json=data)
            data1 = response.json()
        thread = threading.Thread(target=offload_process1, args=(data,))
        thread.start()
        data2 = process2(data)
        thread.join()
        # Question 2: Why do we need to join the thread here?
        # Question 3: Are the processing functions executing in parallel or just concurrently? What is the difference?
        #   See this article: https://oxylabs.io/blog/concurrency-vs-parallelism
        #   ChatGPT is also good at explaining the difference between parallel and concurrent execution!
        #   Make sure to cite any sources you use to answer this question.
    elif offload == 'process2':
        # TODO: Implement this case
        data2 = None

        def offload_process2(data):
            nonlocal data2
            # TODO: Send a POST request to the server with the input data
            response = requests.post(f"{offload_url}/process2", json=data)
            data2 = response.json()
        thread = threading.Thread(target=offload_process2, args=(data,))
        thread.start()
        data1 = process1(data)
        thread.join()
    elif offload == 'both':
        data1 = None
        data2 = None

        def offload_process1(data):
            nonlocal data1
            # TODO: Send a POST request to the server with the input data
            response = requests.post(f"{offload_url}/process1", json=data)
            data1 = response.json()

        def offload_process2(data):
            nonlocal data2
            # TODO: Send a POST request to the server with the input data
            response = requests.post(f"{offload_url}/process2", json=data)
            data2 = response.json()

        thread1 = threading.Thread(target=offload_process1, args=(data,))
        thread2 = threading.Thread(target=offload_process2, args=(data,))
        thread1.start()
        thread2.start()
        thread1.join()
        thread2.join()

    ans = final_process(data1, data2)
    return ans


def main():
    # TODO: Run the program 5 times for each offloading mode, and record the total execution time
    #   Compute the mean and standard deviation of the execution times
    #   Hint: store the results in a pandas DataFrame, use previous labs as a reference
    
    data = []
    for mode in [None, 'process1', 'process2', 'both']:
        runtimes = []
        for i in range(5):
            start = time.time()
            runtime = run(mode)
            end = time.time()

            runtime = end - start

            runtimes.append(runtime)

        runtime_mean = np.mean(runtimes)
        runtime_std = np.std(runtimes)
        if mode is None:
            data.append(('None', runtime_mean, runtime_std))
            continue
        data.append((mode, runtime_mean, runtime_std))
        
    df = pd.DataFrame(
        data, columns=['mode', 'runtime_mean', 'runtime_std'])
    # TODO: Plot makespans (total execution time) as a bar chart with error bars
    # Make sure to include a title and x and y labels
    fig = px.bar(df, x='mode', y='runtime_mean', error_y='runtime_std')
    fig.update_layout(title='Execution time by Offloading Mode', xaxis_title='Offloading Mode', yaxis_title='Execution Time (s)')


    # TODO: save plot to "makespan.png"
    fig.write_image('makespan.png')

# Question 4: What is the best offloading mode? Why do you think that is?
# Question 5: What is the worst offloading mode? Why do you think that is?
# Question 6: The processing functions in the example aren't very likely to be used in a real-world application.
#   What kind of processing functions would be more likely to be used in a real-world application?
#   When would you want to offload these functions to a server?


if __name__ == '__main__':
    main()