It, too, is a library for distributed parallel computing in Python, with a built-in task ... machine-learning tasks or a particular data-processing framework. Pandaral·lel, as the name implies ...

From these low-level interfaces emerged higher-level parallel processing libraries, such as concurrent.futures, joblib and loky (used by dask and scikit-learn) These libraries make it easy for Python ...

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 ...

The head node is connected to a workstation via USB 1.1 allowing the system to be controlled with a Python script. It turns out that the work of distributing the data dwarfs the compute by three ...

We want something as usable for general programming as Python, as easy for statistics as R, as natural for string processing as Perl, as powerful for linear algebra as Matlab, as good at gluing ...

There are many reasons why Python has emerged as the number one language for data ... “So the simulations are something that would be a good fit for Spark –very data-parallel stuff. But Spark wouldn’t ...

Until now, high-level languages such as Python are not optimized for this ... compute performance by maximizing FPGA unrivaled parallel processing capabilities,” said Sina Soltani, vice ...