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Batch System PBSPro (Hawk)
The batch system on Hawk is PBSPro 19.2.X. For general usage see the PBS User Guide (19.2.3)
Introduction
The only way to start a job (parallel or single node) on the compute nodes of this system is to use the batch system.
Writing a submission script is typically the most convenient way to submit your job to the batch system. You generally interact with the batch system in two ways: through options specified in job submission scripts (these are detailed below in the examples) and by using PBSPro commands on the login nodes. There are three key commands used to interact with PBSPro:
- qsub
- qstat
- qdel
Check the man page of PBSPro for more advanced commands and options
man pbs_professional
Requesting Resources using batch system
Resources are allocated to jobs both by explicitly requesting them and by applying specified defaults.
Jobs explicitly request resources either at the host level in chunks defined in a selection statement, or in job-wide resource requests.
-
Format:
- job wide request:
qsub ... -l <resource name>=<value>
The only resources that can be in a job-wide request are server-level or queue-level resources, such as walltime.
- selection statement:
qsub ... -l select=<chunks>
The only resources that can be requested in chunks are host-level resources, such as mem and ncpus. A chunk is the smallest set of resources that will be allocated to a job. It is one or more resource_name=value statements separated by a colon, e.g.:
ncpus=2:mem=32GB
A selection statement is of the form:
-l select=[N:]chunk[+[N:]chunk ...]
Note: If N is not specified, it is taken to be 1. No spaces are allowed between chunks.
Node types
You have to specify the resources you need for your batch job. These resources are specified by including them in the -l argument (selection statement and job-wide resources) on the qsub command or in the PBS job script. The 2 important resources you have to specify are number of nodes of a specific node type in the selection statement and the walltime in the job-wide resource request you need for this job:
select=<number of nodes>:<node_resource_variable=type>
- 16 x AMD EPYC Naples (2 x 32 cores each): select with #PBS -l node_type=naples
- 2 x AMD EPYC Rome (2 x 64 cores each): select with #PBS -l node_type=rome
- This node type is available on workdays between 7 to 18 only by using the queue "workday" (max. walltime 30min). On other times you need to use the default queue "route" (max. walltime on weekends are 24h).
- Rank 0, thread 0 on core 0 of socket 0 on compute node 0
- Rank 0, thread 1 on core 1 of socket 0 on compute node 0
- ...
- Rank 31, thread 1 on core 63 of socket 0 on compute node 0
- Rank 32, thread 0 on core 0 of socket 1 on compute node 0
- ...
- Rank 63, thread 1 on core 63 of socket 1 on compute node 0
- Rank 64, thread 1 on core 0 of socket 0 on compute node 1
- ...
- Rank 127, thread 1 on core 63 of socket 1 on compute node 1
- Rank 0, thread 0 on logical core 0 of core 0 of socket 0 on compute node 0
- Rank 0, thread 1 on logical core 0 of core 1 of socket 0 on compute node 0
- ...
- Rank 31, thread 1 on logical core 0 of core 63 of socket 0 on compute node 0
- Rank 32, thread 0 on logical core 0 of core 0 of socket 1 on compute node 0
- ...
- Rank 63, thread 1 on logical core 0 of core 63 of socket 1 on compute node 0
- Rank 64, thread 0 on logical core 1 of core 0 of socket 0 on compute node 0
- ...
- Rank 127, thread 1 on logical core 1 of core 63 of socket 1 on compute node 0
- Rank 128, thread 0 on logical core 0 of core 0 of socket 0 on compute node 1
- ...
- Rank 255, thread 1 on logical core 1 of core 63 of socket 1 on compute node 1
- To distinguish between different nodes 4 node resource variables are assigned to each node. The node_type, node_type_cpu, node_type_mem and node_type_core of each node. You have to specify at least one of the resource variable or you can specify a valid available combination of the resources for a specific type of nodes.
| node_type | node_type_cpu | node_type_mem | node_type_core | description | notes | # of nodes |
| rome | AMD7742 | 256gb | 128c | HPE Apollo9000 | 200 | |
| rome | AMD7742 | 128gb | 128c | HPE Apollo9000 | 5432 | |
| rome | AMD7742 | 2048gb | 128c | HPE | only available on queue smp | 4 |
| rome | AMD7742 | 4096gb | 128c | HPE | only available on queue smp | 1 |
There are two types of nodes installed in the test system:
Core order
On Rome-based nodes, the core id corresponds to hyperthreads and sockets as follows:
core 0 - core 63: hyperthread 0 @ socket 0
core 64 - core 127: hyperthread 0 @ socket 1
core 128 - core 191: hyperthread 1 @ socket 0
core 192 - core 256: hyperthread 1 @ socket 1
Hence, cores 128 to 256 are using the same physical resources as cores 0 to 127! Only use them if you understand the concept of hyperthreads and actually like to use them! If you do not like to use them, start a maximum of 128 threads per node only!
Pinning
We recommend to always (in hybrid as well as pure MPI jobs) use omplace to pin processes and threads to CPU cores (cf. below) in order to prevent expensive migration.
Shall I use all the available cores?
Due to limited memory bandwidth, it might be beneficial to not use all the available cores in a node. Unfortunately, you have to figure out your sweet spot by means of trial & error. While doing this, please have in mind the internal structure of the processor (cf. Processor) and try to uniformly distribute processes over architectural building blocks (i.e. CCXs, CCDs, NUMA nodes and sockets). In order to make things more easy, please use the block and stride features of omplace (cf. manpage) or use the scripts provided below to generate lists of core IDs to be passed to omplace via the -c flag if your intended placement is not possible by means of blocks & strides.
#!/usr/bin/python
#########################################################################
# Usage: ./distribute_by_fraction.py <numerator> <denominator> #
# Example: ./distribute_by_fraction.py 32 128 #
# #
# The script will then generate a list of <numerator>/<denominator>*128 #
# cores to be used, equally distributed among the available 128 cores. #
#########################################################################
import sys
numerator = int(sys.argv[1])
denominator = int(sys.argv[2])
core_list = ""
for offset in range(0, 127, denominator):
for j in range(numerator):
index = int(j*round(float(denominator)/float(numerator)))
core_list = core_list + str(offset + index) + ","
sys.stdout.write(core_list[:-1] + "\n")
#!/usr/bin/python
#########################################################################
# Example usage: ./distribute_by_pattern.py 1 0 0 0 #
# #
# This will generate a list with core 0 being used, cores 1-3 not being #
# used and so on (i.e. pattern will be repeated until status of all 128 #
# cores is defined). #
#########################################################################
import sys
core_list = ""
for i in range(128):
if sys.argv[i%(len(sys.argv) - 1) + 1] == "1":
core_list = core_list + str(i) + ","
sys.stdout.write(core_list[:-1] + "\n")
Examples
Seeman pbs_resourcesregarding available resources (e.g. ncpus, mpiprocs, etc.) and how to specify resources in the job script.
pure MPI job using HPE MPI
Here is a simple pbs job script:
#!/bin/bash #PBS -N Hi_Thomas #PBS -l select=16:node_type=rome:mpiprocs=128 #PBS -l walltime=00:20:00 module load mpt/2.21 mpirun -np 2048 ./hi.hpe
To submit the job script execute
pure MPI job using OpenMPI
Here is a simple pbs job script:
#!/bin/bash #PBS -N Hi_Thomas #PBS -l select=16:node_type=naples:mpiprocs=64 #PBS -l walltime=00:20:00 module load openmpi/4.0.1 mpirun -np 1024 --map-by core --bind-to core ./hi.hpe
hybrid MPI/OpenMP job using HPE MPI
To run a MPI application with 128 Processes and two OpenMP threads per process on two compute nodes, include the following in the pbs job script:
#!/bin/bash #PBS -N Hi_MPI_OpenMP #PBS -l select=2:node_type=rome:mpiprocs=64:ompthreads=2 #PBS -l walltime=00:20:00 module load mpt/2.21 export OMP_NUM_THREADS=2 mpirun -np 128 omplace -nt 2 [-vv] ./hi.mpiomp
The omplace command helps with the placement of OpenMP threads within an MPI program. In the above example, the threads in a 128-process MPI program with two threads per process are placed as follows:
The optional -vv parameter prints out the placement of the processes and threads to standard output.
Warning: Due to the limited scaling of the standard output, you should not use the optional parameter -vv for medium and large jobs!
hybrid MPI/OpenMP job using HPE MPI and hyperthreads
The job described before can be run on the same physical resources with twice the number of processes respectively threads by means of hyperthreads as follows:
#!/bin/bash #PBS -N Hi_MPI_OpenMP_HT #PBS -l select=2:node_type=rome:mpiprocs=128:ompthreads=2 #PBS -l walltime=00:20:00 module load mpt/2.21 export OMP_NUM_THREADS=2 mpirun -np 128 omplace -nt 2 [-vv] ./hi.mpiomp
Ranks will be placed as follows:
pure MPI job with stride > 1
If you need to let cores unused, do as follows in order to anyway uniformly distribute processes over cores:
#!/bin/bash #PBS -N Hi_Thomas #PBS -l select=1:node_type=rome:mpiprocs=32 #PBS -l walltime=00:20:00 module load mpt/2.21 mpirun -np 32 omplace -c 0-127:st=4 ./hi.hpe
This will start processes on cores 0, 4, 8, etc., i.e. with a stride of 4 (which means having one process per CCX respectively L3 slice (cf. Processor)).
With respect to more advanced placement of processes and threads, cf.
man omplace
as well as here.
time-dependent limitations on the test system
In order to allow for fair share of the Rome node during business hours, HLRS decided to limit the walltime to a maximum of 30min in the timeframe 7:00am - 6:00pm CET from Monday to Friday. In order to get your jobs run, you have to use the queue workday via qsub -q workday or #PBS -q workday. Outside of this timeframe there are no explicit limits, but beware of the implicit 13h limit if you like your job to run over night during the working week. If you require more than 13h (usually untypical in case of a testsystem), you have to wait until the next weekend.
Further information
With respect to further details, please refer to these slides.