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Re:RTX3090 TensorFlow, NAMD and HPCG Performance on Linux by SloopJon on Friday October 02, 2020 @08:20AM (#24661842)
Enroot sounds interesting:

https://github.com/NVIDIA/enroot

It basically returns containers to their chroot origins, promising "no performance overhead." I'm looking forward to more posts on that.

Re:RTX3090 TensorFlow, NAMD and HPCG Performance on Linux by bcatanzaro on Friday October 02, 2020 @01:09PM (#24664711)
My team at NVIDIA uses enroot extensively. It's been really nice. We build containers using Docker but then run them with enroot.
Re:RTX3090 TensorFlow, NAMD and HPCG Performance on Linux by fulafel on Friday October 02, 2020 @01:41PM (#24665005)
What performance overhead does this avoid compared to other container runtimes?

Another aspect, the "unprivileged "part sounds like an advantage over Docker, on par with podman and lxd etc.

Re:RTX3090 TensorFlow, NAMD and HPCG Performance on Linux by gnufx on Friday October 02, 2020 @05:22PM (#24667159)
What's the advantage of enroot over charliecloud, which is unprivileged in the sense of being installable in your home directory (given user namespaces)? https://hpc.github.io/charliecloud/
Re:RTX3090 TensorFlow, NAMD and HPCG Performance on Linux by exxo_ on Friday October 02, 2020 @10:13PM (#24668933)
It is the same idea, we actually considered it at first. There are some differences in the implementation though and we built enroot with the idea of being more extensible. We also have a plugin for SLURM (https://github.com/NVIDIA/pyxis)
Re:RTX3090 TensorFlow, NAMD and HPCG Performance on Linux by exxo_ on Friday October 02, 2020 @10:27PM (#24669003)
There are several things that can impact performance on "traditional" container runtimes. For example, cgroups, LSMs, seccomp (especially with spectre mitigations), network NS/bridges, etc. There are also more subtle things like being able to do CMA, or deal with shared memory. Most runtimes let you opt out but this becomes difficult to manage and secure with multiple users.