Worth Reading: Redliner
The post Worth Reading: Redliner appeared first on 'net work.
The post Worth Reading: Redliner appeared first on 'net work.
In the first post of this series we talked about some of the CNI basics. We then followed that up with a second post showing a more real world example of how you could use CNI to network a container. We’ve covered IPAM lightly at this point since CNI relies on it for IP allocation but we haven’t talked about what it’s doing or how it works. In addition – DNS was discussed from a parameter perspective in the first post where we talked about the CNI spec but that’s about it. The reason for that is that CNI doesn’t actually configure container DNS. Confused? I was too. I mean why is it in the spec if I can’t configure it?
To answer these questions, and see how IPAM and DNS work with CNI, I think a deep dive into an actual CNI implementation would be helpful. That is – let’s look at a tool that actually implements CNI to see how it uses it. To do that we’re going to look at the container runtime from the folks at CoreOS – Rocket (rkt). Rkt can be installed fairly easily using this set of commands…
wget https://github.com/coreos/rkt/releases/download/v1.25.0/rkt_1. Continue reading
Just a few notes on the blog site in general. I’ve rebuilt the sixty books pages without tables. I don’t know if this is better, but it does load a bit faster. I’ve also added links to my GoodReads and Feedly profiles just in case you’re interested in what I’m currently reading/read on a regular basis. I didn’t include all the RSS feeds I read in the shared Feedly profile, just general, culture, and technology.
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Conventional wisdom says that choosing between a GPU versus CPU architecture for running scientific visualization workloads or irregular code is easy. GPUs have long been the go-to solution, although recent research shows how the status quo could be shifting.
At SC 16 in Salt Lake City in a talk called CPUs versus GPUs, Dr. Aaron Knoll of the University of Utah, and Professor Hiroshi Nakashima of Kyoto University, presented comparisons of various CPU and GPU-based architectures running visualizations and irregular code. Notably, both researchers have found that Intel Xeon Phi processor-based systems show stand-out performance compared to GPUs for …
CPU, GPU Potential for Visualization and Irregular Code was written by Nicole Hemsoth at The Next Platform.