Stories from the Web Crawling trenches in parallelism

Does asyncio use multiple cores?

Author: Mohan Ganesan

Date: Mar 24, 2024

Asyncio enables concurrency, but not parallelism by default. You can achieve parallelism by integrating thread pools and process pools.

Does asyncio run in parallel python ?

Author: Mohan Ganesan

Date: Mar 17, 2024

Python's asyncio module enables concurrency, not parallelism, by using coroutines and an event loop.

Does asyncio use multiple cores python ?

Author: Mohan Ganesan

Date: Mar 17, 2024

Python's asyncio module enables concurrency within a single thread, but not parallelism across multiple threads or processes. However, by utilizing multiprocessing or multithreading, we can achieve true parallelism.

Faster Parallel Processing Alternatives to Multithreading in Python

Author: Mohan Ganesan

Date: Mar 17, 2024

Multithreading in Python allows concurrent execution of multiple threads within a process. However, it has limitations due to the GIL. Alternatives like multiprocessing, Numba, and Cython provide better parallelism and performance.

Making the Most of asyncio: Adding Tasks to Event Loops

Author: Mohan Ganesan

Date: Mar 25, 2024

The asyncio module in Python provides infrastructure for writing asynchronous code using the async/await syntax. The event loop is at the heart of asyncio and manages task execution. Enqueue tasks with loop.create_task() or ensure_future().

Concurrency in Python: Understanding Asyncio and Futures

Author: Mohan Ganesan

Date: Mar 24, 2024

Python provides powerful tools for handling concurrency and parallelism with asyncio and futures. Asyncio enables asynchronous I/O handling in a single thread, while futures handle parallelism across threads/processes.

What is the difference between asyncio and time sleep in Python?

Author: Mohan Ganesan

Date: Mar 17, 2024

Python provides asyncio module for concurrency and time.sleep for pausing execution. Use asyncio for parallelism and time.sleep carefully.

Is asyncio concurrent or parallel python?

Author: Mohan Ganesan

Date: Mar 17, 2024

Asyncio provides concurrency, not parallelism. It shines for I/O bound work and can achieve high performance. Use multiprocessing for CPU intensive tasks.

Achieving Speed with Asyncio in Python

Author: Mohan Ganesan

Date: Mar 24, 2024

Python's asyncio library enables concurrency for improved performance, but not parallelism. It allows efficient use of I/O resources within a single thread.

Async IO in Python: When and Why to Use It Over Threads

Author: Mohan Ganesan

Date: Mar 17, 2024

Leverage async I/O for non-CPU bound tasks that deal with network, disk, or user interactions for great performance gains. Stick to threads for intensive computational workloads.

Understanding Multithreading Models: Green, Native, and Pool

Author: Mohan Ganesan

Date: Mar 24, 2024

Multithreading enables parallel execution, with green threads managed by runtime, native threads by OS, and thread pools for task execution.

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