htcondor admin tutorial

122 Pages·2015·3.63 MB·English

HTCONDOR ADMIN TUTORIAL
Greg Thain
Center for High Throughput Computing
University of Wisconsin – Madison
[email protected]
© 2015 Internet2
Overview
•  What makes HTCondor different?
•  Nitty gritty of HTCondor configuration?
[ 2 ]
HTCondor differences and
implications
•  Goal: Run jobs on as many machines as possible*
•  -> implies heterogeneity:
•  Generational: different speeds, memory
•  OS Distribution and type (Linux/Windows)
•  HTCondor versions
•  Installed software base // installed datasets
•  Geography: building, campus,
•  Temporal
[ 3 ]
HTCondor differences and
implications
•  Goal: Run jobs on as many machines as possible*
•  -> implies scalability and reliability:
•  Worker machines can come and go
And there can be a lot of them
•  Horizontal scalability
Many independent submit nodes
•  No single point of failure
•  HTCondor job is “like money in the bank”
[ 4 ]
Requires Networking
•  Not just fibers and packets and routers
•  Community building:
– Breaking out of silos
•  Asymmetric relationships
[ 5 ]
HTCondor history
•  Cycle scavenging on desktops
– Implies temporal disruptions
– Delegating ownerships temporarily
•  Not just desktop scavenging anymore
– But same Model applies at different scales..
[ 6 ]
The Lehigh Story
•  Professor needed more compute power
•  Access to a small cluster not sufficient
•  The power of one lunch…
•  Computing demand outstrips capacity
[ 7 ]
Condominium cluster
•  Several groups pool money for big buy:
– Chemistry, physics, biology each buy 500 cores
– When sufficient demand, each get 500 cores
– When less demand, can get more than 500
•  Same model as delegated ownership
[ 8 ]
HPC Backfil l
•  HPC / MPI implies low utilization
– HTCondor can backfill HPC Cluster
– Same model as delegated ownership
•  HPC offload
– Many HPC workloads really HTC, clogging up
– Make HPC Admins happy
[ 9 ]
Cloud annexation
•  EC2 “spot instances”
– Delegated ownership yet again
•  Can build pool in cloud (Cycle computing)
•  Or can expand into the cloud
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