Cancelling async CPU calculations as new ones are requested typically involves using multithreading or asynchronous programming techniques. Here's a high-level approach in Python using the `asyncio` library for illustration:
```python
import asyncio
async def calculate_task(task_id):
print(f"Starting calculation for Task {task_id}")
await asyncio.sleep(5) # Simulate a long-running computation
print(f"Completed calculation for Task {task_id}")
async def main():
tasks = []
# Start a series of calculations
for i in range(1, 6):
task = asyncio.create_task(calculate_task(i))
tasks.append(task)
# While calculations are running, request new calculations
for i in range(6, 11):
# If a new calculation is requested, you can cancel a previous one
if tasks:
old_task = tasks.pop(0)
old_task.cancel()
new_task = asyncio.create_task(calculate_task(i))
tasks.append(new_task)
# Wait for all tasks to complete
await asyncio.gather(*tasks)
if __name__ == '__main__':
asyncio.run(main())
```
In this example, we use the `asyncio` library to run asynchronous tasks. Here's how it works:
1. We define an `async` function `calculate_task` that simulates a long-running calculation.
2. In the `main` function, we start a series of calculations by creating and running tasks asynchronously using `asyncio.create_task`.
3. While these tasks are running, we request new calculations. If a new calculation is requested, we cancel the previous one by calling `old_task.cancel()`.
4. We use `asyncio.gather` to wait for all tasks to complete.
This example demonstrates how you can manage and cancel asynchronous CPU calculations as new ones are requested. However, keep in mind that the specific implementation details may vary depending on the programming language and libraries you are using. Additionally, you should handle exceptions and cleanup appropriately when cancelling tasks.