This is very much worth watching. It is a tour de force.

Laurie does an amazing job of reimagining Google's strange job optimisation technique (for jobs running on hard disk storage) that uses 2 CPUs to do the same job. The technique simply takes the result of the machine that finishes it first, discarding the slower job's results... It seems expensive in resources, but it works and allows high priority tasks to run optimally.

Laurie re-imagines this process but for RAM!! In doing this she needs to deal with Cores, RAM channels and other relatively undocumented CPU memory management features.

She was even able to work out various undocumented CPU/RAM settings by using her tool to find where timing differences exposed various CPU settings.

She's turned "Tailslayer" into a lib now, available on Github, https://github.com/LaurieWired/tailslayer

You can see her having so much fun, doing cool victory dances as she works out ways of getting around each of the issues that she finds.

The experimentation, explanation and graphing of results is fantastic. Amazing stuff. Perhaps someone will use this somewhere?

As mentioned in the YT comments, the work done here is probably a Master's degrees worth of work, experimentation and documentation.

Go Laurie!

>> It replicates data across multiple, independent DRAM channels with uncorrelated refresh schedules

This is the sort of thing which was done before in a world where there was NUMA, but that is easy. Just task-set and mbind your way around it to keep your copies in both places.

The crazy part of what she's done is how to determine that the two copies don't get get hit by refresh cycles at the same time.

Particularly by experimenting on something proprietary like Graviton.

I like the video, but this is hardly groundbreaking. You send out two or more messengers hoping at least one of them will get there on time.