OCuLUS
Login-Frontends:fe.pc2.uni-paderborn.de.de, fe-2.cv2012.pc2.uni-paderborn.de
Inhaltsverzeichnis
System Specific Environment Variables
They are set automatically and can be used in scripts. Refer also to OCuLUS file systems.
| Environment Variable |
Contains |
|---|---|
| HOME | Absolute path to your PC² wide home directory |
| PC2DATA | Absolute path to the PC² wide group data directories |
| PC2PFS | Absolute path to the group directories on the OCULUS parallel file system BeeGFS |
| PC2SCRATCH | Absolute path to PC² wide group scratch directories |
| PC2SW | Absolute path to the OCULUS software directory |
| PC2SYSNAME | The name of the system: "OCULUS" |
FAQ
No host found which provides the requested resources:Perhaps a collision with a node freepool
A node freepool limits the access to a node. The constraints are part of the node's properties.
One can inspect these propertiers by calling ccsinfo -n --state=%H%p%m.
More details are in the OpenCCS User-Manual Appendix K
OpenCCS did not write a job trace file
It may happen that OpenCCS is temporarily not able to write the trace file to the specified directory. In such cases OpenCCS writes the file to $CCS/tmp/OCULUS/TRACES.
Which node types are available?
| Type | Nodes | CPU-Type | Cores | Memory | Accelerator |
|---|---|---|---|---|---|
| normal | 552 | two Intel Xeon E5-2670 | 16 | 64GB | - |
| washington | 20 | two Intel Xeon E5-2670 | 16 | 256GB | - |
| tesla | 32 | two Intel Xeon E5-2670 | 16 | 64GB | 1 nVIDIA K20 (Kepler) |
| gtx1080 | 8 | two Intel Xeon E5-2670 | 16 | 64GB | 2 nVIDIA GeForce GTX-1080 Ti) |
| smp | 4 | four Intel Xeon E5-4670 | 32 | 1TB | - |
'ccsinfo -n' shows 'only local jobs = true'. What does it mean?
This node does only accept jobs which run completey on that node. Jobs using more than one node are not mapped to this node.
Cannot get 16 cores on a gpu node
Nodes hosting GPU cards keep one core per GPU free for jobs requesting GPUs. Hence, jobs not requesting a GPU card, will only get 14 or 15 cores at maximum on that nodes.
HowTos
How to (un)select specific node types?
Normally, you won't care about this question, because you just request cores, memory, accelerators, or licences and CCS cares about the mapping. However, for benchmarking purposes it may be useful to (un)select specific node types. For this purpose, we provide resources of type Boolean to (un)select node types. ccsinfo -a shows the available resources:
Name Type, Amount Default Purpose
Flags Used/Online/Max
=============================================================
ncpus U,C 4920/8704/8704 1 number of cores
nodes U,C 0/525/617 1 number of exclusively used nodes
mem S,C 18.92t/40.18t/40.18t 3.94g physical memory
vmem S,C 23.50t/50.51t/50.51t 4.89g virtual memory
cput T, - N/A CPU time
walltime T,J - N/A walltime
hostname A, - N/A hostname
arch A, - N/A host architecture
mpiprocs U, - N/A number of mpi processes per chunk
ompthreads U, - N/A number of threads per chunk
mdce U,CJ 0/256/256 N/A Matlab Distributed Computing Environment licenses
norm B, - N/A 64GB compute node
gtx1080 U,C 0/8/16 2 nVIDIA GeForce GTX1080 Ti (11GB RAM)
rack U, - N/A rack number
smp B, - N/A SMP node
tesla U,C 0/32/32 1 Tesla K20xm card
wash B, - N/A washington node
For example. If you want to run a job only on the washington nodes set wash=true
--res=rset=2:ncpus=5:wash=t
requests 2 chunks each with 5 cores and wash==true.
To exclude washington and smp nodes use:
--res=rset=2:ncpus=5:wash=f:smp=f
How to allocate a Tesla accelerator
For Tesla K20, we provide the consumable resource tesla. This avoids that more than one job will be scheduled to a card at the same time.
Hence, to request 2 chunks each with 8 cpus and one Tesla card use:
--res=rset=2:ncpus=8:tesla=1
For offload jobs CCS sets the environment variable:
CUDA_VISIBLE_DEVICES=0
For jobs mapped on a GPU node but not requesting the Tesla accelerator CCS sets the environment variable:
CUDA_VISIBLE_DEVICES=1024
which is an invalid value.
Typically sufficient vmem has to be allocated.
E.g. --res=rset=1:ncpus=1:tesla=1:vmem=85g:mem=4g
How to allocate a GTX1080 accelerator
For GTX1080, we provide the consumable resource gtx1080. This avoids that more than one job will be scheduled to a card at the same time.
Hence, to request 2 chunks each with 8 cpus and one GTX1080 card use:
--res=rset=2:ncpus=8:gtx1080=1
For offload jobs CCS sets the environment variable:
CUDA_VISIBLE_DEVICES=0
For jobs mapped on a GPU node but not requesting the Tesla accelerator CCS sets the environment variable:
CUDA_VISIBLE_DEVICES=1024
which is an invalid value.
Typically sufficient vmem has to be allocated.
E.g. --res=rset=1:ncpus=1:gtx1080=1:vmem=85g:mem=4g
