app.py

import time
import numpy as np
from concurrent.futures import ThreadPoolExecutor, ProcessPoolExecutor

import tensorflow as tf
# Removed: import cupy as cp

# Simulate a computationally intensive task (e.g., matrix multiplication)
def cpu_task(data):
    result = np.dot(data, data.T)  # CPU-based task
    return result

# Originally meant for GPU; now replaced with NumPy for CPU fallback
def pseudo_gpu_task(data):
    # Simulate GPU task using NumPy on CPU
    result = np.dot(data, data.T)
    return result

# Simulate a deep learning task using TensorFlow (uses CPU if GPU unavailable)
def deep_learning_task(data):
    model = tf.keras.Sequential([
        tf.keras.layers.Dense(64, input_dim=data.shape[1], activation='relu'),
        tf.keras.layers.Dense(32, activation='relu'),
        tf.keras.layers.Dense(1, activation='sigmoid')
    ])

    model.compile(optimizer='adam', loss='binary_crossentropy', metrics=['accuracy'])

    labels = np.random.randint(0, 2, size=(data.shape[0], 1))
    model.fit(data, labels, epochs=1, batch_size=32, verbose=0)

    return model.evaluate(data, labels, verbose=0)

# Task Offloading decision logic
def offload_task(data, task_type):
    if task_type == 'cpu':
        print("Using CPU for the task")
        return cpu_task(data)
    elif task_type == 'gpu':
        print("Simulated GPU task using CPU (no GPU available)")
        return pseudo_gpu_task(data)
    elif task_type == 'deep_learning':
        print("Using TensorFlow for deep learning task")
        return deep_learning_task(data)
    else:
        print("Invalid task type")
        return None

# Auto-Parallelization System (APTOS)
class APTOS:
    def __init__(self):
        self.executor_cpu = ThreadPoolExecutor(max_workers=4)
        self.executor_gpu = ProcessPoolExecutor(max_workers=2)
        self.executor_dl = ThreadPoolExecutor(max_workers=1)

    def run_parallel_tasks(self, data, task_type):
        if task_type == 'cpu':
            future = self.executor_cpu.submit(cpu_task, data)
        elif task_type == 'gpu':
            future = self.executor_gpu.submit(pseudo_gpu_task, data)
        elif task_type == 'deep_learning':
            future = self.executor_dl.submit(deep_learning_task, data)
        else:
            raise ValueError("Unknown task type.")

        return future.result()

# Example usage
if __name__ == "__main__":
    data = np.random.rand(1000, 100)

    aptos = APTOS()

    start_time = time.time()
    cpu_result = aptos.run_parallel_tasks(data, 'cpu')
    print(f"CPU Task Completed in {time.time() - start_time:.4f} seconds.")

    start_time = time.time()
    gpu_result = aptos.run_parallel_tasks(data, 'gpu')
    print(f"Simulated GPU Task Completed in {time.time() - start_time:.4f} seconds.")

    start_time = time.time()
    dl_result = aptos.run_parallel_tasks(data, 'deep_learning')
    print(f"Deep Learning Task Completed in {time.time() - start_time:.4f} seconds.")