For example, you can prefix any Python statement with %px to automatically parallelize it. Joblib has two major goals: run jobs in parallel, and don’t recompute results if nothing has changed.

Most of the time this isn’t a significant bottleneck, but it becomes one when you want to run many jobs in parallel. Python provides two ways to work around this issue: threading and ...

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 ...

My preferred Python package for retrieving data from the web is ... Again, if the threads don't really need to execute in a truly parallel fashion, you're fine. But, what if your system receives a ...

So, it's something of a dilemma: do you launch easy-to-use threads, even though they don't really run in parallel? Or, do you launch new processes, over which you have little control? The answer is ...

I'm running some simulations using the joblib library. For that, I have some number of parameter combinations, each of which I run 100,000 times. I'd now like to write the result of each ...

Israeli researchers have developed a new software "platform" to turn easily readable Python instructions into low-level machine code and execute it in RAM without going through the CPU.