Google's distributed computing for dummies trains ResNet-50 in under half an hour Google's new "TF-Replicator" technology is meant to be drop-dead simple distributed computing for AI researchers.

It is crucial for Python to provide high-performance parallelism. This talk will expose to both data-scientists and library developers the current state of affairs and the recent advances for parallel ...

Nvidia wants to extend the success of the GPU beyond graphics and deep learning to the full data science experience. Open source Python library Dask is the key to this.

Anyscale, a startup founded by a team out of UC Berkeley that created the Ray open-source Python framework for running distributed computing projects, has raised $40 million.

Anyscale, the startup behind the open source project Ray, today closed a $40 million round to support its first commercial offering, a managed Ray platform.

Is distributed computing dying, or just fading into the background? There seems to be much less excitement about distributed computing these days.

In this video, Jan Meinke and Olav Zimmermann from the Jülich Supercomputing Centre present: High-Performance Computing with Python: Reducing Bottlenecks. This course addresses scientists with a ...

Distributed computing allowed these researchers to efficiently share the computational load between giant mainframes and individual workstations, even if those machines came from different ...

So what’s the difference? At a fundamental level, distributed computing and concurrent programming are simply descriptive terms that refer to ways of getting work done at runtime (as is parallel ...