Streamlining HPC Workloads with Containers @DustinKirkland
what does high-performance computing look like?
Wikipedia says...
Or perhaps in China...
Google image search shows...
The university student learns...
HackerNews suggests...
x1.32xlarge Your DevOps engineer launches...
But then there is your real, actual data center...
what do all of these have in common?
a lot, actually
they’re all running Linux
directly on the bare metal itself
performance is maximized
overhead is minimized
big problems are distributed across a cluster
everyone prefers a clean environment
virtual machines always involve overhead
Guest VM Exit VMXON
VM Entry
VM Monitor
VMXOFF
oh, and let’s reboot a datacenter
BIOS is checking memory for problems… Scanning 1,199,511,627,776 bytes… This may take several minutes… Running test 1 of 8: 1.0% complete Overall test status: 0.1% complete Time elapsed: 17m23s Status: No problems have been found yet.
so let’s have a look at containers
first, process containers
awesome for HPC functions
LXD
second, machine containers
➢ Ultra fast “vm-lite” guests (bare metal speed) lxc distribution of Linux lxc - e.g. Ubuntu, CentOS lxc ➢ Any machine ➢ machine Starts in less than machine 1 second ➢ 15x density of KVM or ESX for idle workloads
lxc machine
kernel
lxd
host A
kernel
lxd
kernel
host B
lxd
host C
kernel
lxc machine
lxd
kernel
host D
LXD REST API nova-lxd
lxc cli
other restful apps
host ...
lxd
lxc machine
kernel
lxd
host A
➢ Ultra fast “vm-lite” guests (bare metal speed) ➢ Any lxcdistribution of Linux lxc- e.g. Ubuntu, CentOS lxc ➢ machine Starts in less than 1 machine second machine ➢ 15x density of KVM or ESX for idle workloads kernel
lxd
kernel
host B
lxd
host C
kernel
lxd
lxc machine
kernel
host D
LXD REST API nova-lxd
lxc cli
other restful apps
host ...
lxd
One LXD container, with 100% of the system: CPU Cores CPU Cycles Memory Disk Space Disk IO Network IO
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Google image search shows... Page 5 of 47. Streamlining HPC Workloads with Containers.pdf. Streamlining HPC Workloads with Containers.pdf. Open. Extract.
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This extends the analysis. of Dynarski and Scott-Clayton (2006) which was. limited to dependent students. Page 3 of 13. The Feasibility of Streamlining Aid.pdf.
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pull down, select: USER ACCOUNT REQUEST. 3) In the âDescrib your Issueâ. box,enter: Request Access. Research HPC Systems. If not the PI of a grant, please.
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Page 1 of 1. Deep dive into technical specifications of our machine. Within the last few years, we have seen the transformative impact of deep learning in many.
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savings compared with rigid, predefined machine types. Our global network delivers ultra-low-latency data transfer. After you migrate to GCP, you can selectively replatform onto a growing ecosystem of modern services: big data analytics, AI/ machine
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