In this video from EuroPython 2019, Pierre Glaser from INRIA presents: Parallel computing in Python: Current state and recent advances. Modern hardware is multi-core. It is crucial for Python to ...

Learn how to use asynchronous programming in Python 3.13 and higher. Get more done in less time, without waiting. Asynchronous programming, or async, is a feature of many modern languages that allows ...

According to a new edition of Parallel Universe Magazine, from Intel, Python has several pathways to vectorization. These range from just-intime (JIT) compilation with Numba 1 to C-like code with ...

The ability to execute code in parallel is crucial in a wide variety of scenarios. Concurrent programming is a key asset for web servers, producer/consumer models, batch number-crunching and pretty ...

Just because supercomputers are engineered to be far more powerful over time does not necessarily mean programmer productivity will follow the same curve. Added performance means more complexity, ...

I'm wondering if anyone has any recommendations for good resources to learn parallel/concurrent/multicore programming. I know this is a pretty damn vague question, so bear with me. I've been a ...