Goethe-HLR Cluster Usage

The cluster was updated to a new operating system. Please help us to help you. In the beginning it is usally very stressful because we get bomarded with tickets, like my previous software ran but nothings works anymore. It can be nessecary to set up software e.g. Spack from the scratch. Also some old ssh-keys with the rsa cipher might not work anymore. Please provide us some time to rearrange our dokumentation. Please discuss problems first in your group, maybe team members already found a solution to your problem, before attacking us with tickets. As less tickets we get as faster we can proceed to provide a working cluster for everybody. Also see common errors.

The Goethe-HLR is a general-purpose computer cluster running AlmaLinux 9 and SLURM. Please read the following instructions and ensure that this guide is fully understood before using the system.

Login

An SSH client is required to connect to the cluster. On a Linux system the command is usually:

ssh <user_account>@goethe.hhlr-gu.de

Am I connected to the right server? Please find additional information here:

goethe.hhlr-gu.de fingerprints

Warnings - Security Breach - Keys etc.

You may receive a warning from your system that something with the security is wrong, “maybe somebody is evedropping”. Due to the upgrade of the operating system from Scientifc Linux 7.9 to AlmaLinux 9.2 the SSH server keys have changed. Please erase your old Goethe key with
ssh-keygen -R goethe.hhlr-gu.de

and accept the new goethe.hhlr-gu.de key. Above you will find our unique ECDSA and ED25519 fingerprints. Some programs tend to display the fingerprint in SHA256 or MD5 format. Just click on goethe.hhlr-gu.de fingerprints above.

On Windows systems please use/install a Windows SSH client (e.g. PuTTY, MobaXterm, the Cygwin ssh package or the built-in ssh command).

During your first login you will get the message, that your password has expired and you have to change it. Re-enter the initial password provided by the CSC, then choose a new one and confirm it. You will be logged out automatically. Now you can login with your new password and work on the cluster.

Never run heavy calculations on the login nodes, i.e. CPU-time- or RAM-consuming processes. You can check the CPU-time limit (in seconds) by running

ulimit -t

on the command line. On a login node, any process that exceeds the CPU-time limit (e.g. a long running test program or a long running rsync) will be killed automatically.

Environment Modules

There are several versions of software packages installed on our systems. The same name for an executable (e.g. mpirun) and/or library file may be used by more than one package. The environment module system, with its module command, helps to keep them apart and prevents name clashes. You can list the module-managed software by running module avail on the command line. Other important commands are module load <name> (loads a module) and module list (lists the already loaded modules).

It's important to know, which modules you really need. Loading more than one MPI module at the same time will likely lead to overlapping.

If you want to know more about module commands, the module help command will give you an overview.

Working with Intel oneAPI

With the command 'module avail' you are able to see all available modules on the cluster. The intel/oneapi/xxx works a kind of different, because it functions like a container. Start loading it with module load intel/oneapi/xxx and see with module avail what is inside. Now start loading your preferred module.

To avoid errors please use version numbers instead of latest.

Example

module load intel/oneapi/2023.2.0
module avail
...
# new modules available within the Intel oneAPI modulefile
----------------------------------- /cluster/intel/oneapi/2023.2.0/modulefiles -----------------------------------
advisor/2023.2.0        dev-utilities/2021.10.0  icc32/2023.2.1                mkl32/2023.2.0
advisor/latest          dev-utilities/latest     icc32/latest                  mkl32/latest
ccl/2021.10.0           dnnl-cpu-gomp/2023.2.0   inspector/2023.2.0            mpi/2021.10.0
ccl/latest              dnnl-cpu-gomp/latest     inspector/latest              mpi/latest
compiler-rt/2023.2.1    dnnl-cpu-iomp/2023.2.0   intel_ipp_ia32/2021.9.0       mpi_BROKEN/2021.10.0
compiler-rt/latest      dnnl-cpu-iomp/latest     intel_ipp_ia32/latest         mpi_BROKEN/latest
compiler-rt32/2023.2.1  dnnl-cpu-tbb/2023.2.0    intel_ipp_intel64/2021.9.0    oclfpga/2023.2.0
compiler-rt32/latest    dnnl-cpu-tbb/latest      intel_ipp_intel64/latest      oclfpga/2023.2.1
compiler/2023.2.1       dnnl/2023.2.0            intel_ippcp_ia32/2021.8.0     oclfpga/latest
compiler/latest         dnnl/latest              intel_ippcp_ia32/latest       tbb/2021.10.0
compiler32/2023.2.1     dpct/2023.2.0            intel_ippcp_intel64/2021.8.0  tbb/latest
compiler32/latest       dpct/latest              intel_ippcp_intel64/latest    tbb32/2021.10.0
dal/2023.2.0            dpl/2022.2.0             itac/2021.10.0                tbb32/latest
dal/latest              dpl/latest               itac/latest                   vtune/2023.2.0
debugger/2023.2.0       icc/2023.2.1             mkl/2023.2.0                  vtune/latest
debugger/latest         icc/latest               mkl/latest

Key:
loaded  modulepath

Compiling Software

You can compile your software on the login nodes (or on any other node, inside a job allocation). Several compiler suites are available. While GCC version 11.3.1 is the built-in OS default, you can list additional compilers and libraries by running module avail:

  • Intel compilers
  • MPI libraries

For the right compilation commands please consider:

C/C++, Fortran77, Fortran 95

Compilation commands for different compilers

To build and manage software which is not available via “module avail” and is not available as a built-in OS package, we recommend using Spack. Please read this small introduction on how to use Spack on the cluster. More information is available on the Spack webpage.

Storage

There are various storage systems available on the cluster. In this section we describe the most relevant:

  • your home directory /home/<group>/<user> (NFS, slow),
  • your scratch directory /scratch/<group>/<user> (parallel file system BeeGFS, fast),
  • the non-shared local storage (i.e. only accessible from the compute node it's connected to, max. 1.4 TB, slow) under /local/$SLURM_JOB_ID on each compute node
  • and the two (slow) archive file systems /arc01 and /arc02 (explained at the end of this section).

Please use your home directory for small permanent files, e.g. source files, libraries and executables.

Use the scratch space for large temporary job data and delete the data as soon as you no longer need it, e.g. when it's older than 30 days.

By default, the space in your home directory is limited to 10 GB and in your scratch directory to 5 TB and/or 800000 inodes (which corresponds to approximately 200000+ files). You can check your homedir and scratch usage by running the quota command on a login node.

While the data in your home directory is backed up nightly (please ask, if you want us to restore anything, see also HRZ-Backup), there is no backup of your scratch directory.

If you need local storage on the compute nodes, you have to add the --tmp parameter to your job script (see SLURM section below). Set the amount of storage in megabytes, e.g. set --tmp=5000 to allocate 5 GB of local disk space. The local directory (/local/$SLURM_JOB_ID) is deleted after the corresponding job has finished. If, for some reason, you don't want the data to be deleted (e.g. for debugging), you can use salloc instead of sbatch and work interactively (see man salloc). Or, one can put an rsync at the end of the job script, in order to save the local data to /scratch just before the job exits:

...
 
mkdir /scratch/<groupid>/<userid>/$SLURM_JOBID
scontrol show hostnames $SLURM_JOB_NODELIST | xargs -i ssh {} \
    rsync -a /local/$SLURM_JOBID/ \
    /scratch/<groupid>/<userid>/$SLURM_JOBID/{}

In addition to the “volatile” /scratch and the permanent /home, which come along with every user account, more permanent disk space (2 × N, where N ≤ 10 TB) can be requested by group leaders for archiving. Upon request, two file systems will be created for every group member, to be accessed through rsync1), e.g. list the contents of your folder and archive a /scratch directory:

rsync arc01:/archive/<group>/<user>/
...
cd /scratch/<group>/<user>/
rsync [--progress] -a <somefolder> arc01:/archive/<group>/<user>/

or, for arc02:

rsync arc02:/archive/<group>/<user>/
...
cd /scratch/<group>/<user>/
rsync [--progress] -a <somefolder> arc02:/archive/<group>/<user>/

The space is limited by N on each of the both systems. Limits are set for an entire group (there's no user quota). The disk usage can be checked by running

df -h /arc0{1,2}/archive/<group>

on the command line. The corresponding hardware resides in separate server rooms. There is no automatic backup. However, for a user, a possible backup scenario is to backup his or her data manually to both storage systems, arc01 and arc02 (e.g. at the end of a compute job). Note: The archive file systems are mounted on the login nodes, but not on the compute nodes. So it's not possible to use the archive for direct job I/O. Please use rsync as described above.

  • All shared file systems are shared between users and jobs. There is no guarantee, that you always get the desired bandwidth and/or response time.
  • Although our storage systems are protected by RAID mechanisms, we can't guarantee the safety of your data. It is within the responsibility of the user to backup important files.

Running Jobs With SLURM

On our systems, compute jobs and resources are managed by SLURM (Simple Linux Utility for Resource Management). The compute nodes are organized in partitions (or queues) named general1 and gpu. There is also a small test partition called test. Additionally we offer the use of some old compute nodes from the former LOEWE-CSC cluster. Those nodes are organized in the partition general2. You can see more details (the current number of nodes in each partition and their state) by running the sinfo command on a login node.

PartitionCPUGPU
general1 Intel Skylake CPUn/a
general2 Intel Ivy Bridge CPU
Intel Broadwell CPU
n/a
gpu AMD EPYC 7452 AMD Instinct MI210
test Intel Skylake CPUn/a

Nodes are used exclusively, i.e. only whole nodes are allocated for a job and no other job can use the same nodes concurrently.

In this document we discuss several job types and use cases. In most cases, a compute job falls under one (or more than one) of the following categories:

For every compute job you have to submit a job script (unless working interactively using salloc or srun, see man page for more information). If jobscript.sh is such a script, then a job can be enqueued by running

sbatch jobscript.sh

on a login node. A SLURM job script is a shell script containing SLURM directives (options), i.e. pseudo-comment lines starting with

#SBATCH ...

The SLURM options define the resources to be allocated for the job (and some other properties). Otherwise the script contains the “job logic”, i.e. commands to be executed.

Read More

The following instructions shall provide you with the basic information you need to get started with SLURM on our systems. However, the official SLURM documentation covers some more use cases (also in more detail). Please read the SLURM man pages (e.g. man sbatch or man salloc) and/or visit http://www.schedmd.com/slurmdocs. It's highly recommended.

Helpful SLURM links: SLURM FAQ
SLURM documentation: SLURM

The test Partition: Your First Job Script

You can use the (very small) test partition for pre-production or tests. In test you can run jobs with a walltime of no longer than two hours. In the following example we allocate 160 CPU tasks (i.e. 160 CPU threads on two nodes = 80 tasks per node) and 512 MB per task for 5 minutes (SLURM may kill the job after that time, if it's still running):

#!/bin/bash
#SBATCH --job-name=foo
#SBATCH --partition=test
#SBATCH --nodes=2
#SBATCH --ntasks=160
#SBATCH --cpus-per-task=1
#SBATCH --mem-per-cpu=512
#SBATCH --time=00:05:00
#SBATCH --no-requeue
#SBATCH --mail-type=FAIL
 
srun hostname
sleep 3

--cpus-per-task=1 For SLURM, a CPU core (a CPU thread, to be more precise) is a CPU.
--no-requeue Prevent the job from being requeued after a failure.
--mail-type=FAIL Send an e-mail if sth. goes wrong.

The srun command is responsible for the distribution of the program (hostname in our case) across the allocated resources, so that 80 instances of hostname will run on each of the allocated nodes concurrently. Please note, that this is not the only way to run or to distribute your processes. Other cases and methods are covered later in this document. In contrast, the sleep command is executed only on the head2) node.

Although nodes are allocated exclusively, you should always specify a memory value that reflects the RAM requirements of your job. The scheduler treats RAM as a consumable resource. As a consequence, if you omit the --nodes parameter (so that only the number of CPU cores is defined) and allocate more memory per core than there actually is on a node, you'll automatically get more nodes if the job doesn't fit in otherwise.3) Moreover, jobs are killed through SLURM's memory enforcement when using more memory than requested.

As already mentioned, after saving the above job script as e.g. jobscript.sh, you can submit your job by running

sbatch jobscript.sh

on the command line. The job's output streams (stdout and stderr) will be joined and saved to slurm-ID.out, where ID is a SLURM job ID, which is assigned automatically. You can change this behavior by adding an --output and/or --error argument to the SLURM options.

Job Monitoring

For job monitoring (to check the current state of your jobs) you can use the squeue command. Depending on the current cluster utilization (and other factors), your job(s) may take a while to start. You can list the current queuing times by running sqtimes on the command line.

If you need to cancel a job, you can use the scancel command (please see the manpage, man scancel, for further details).

Node Types

On Goethe-HLR four different types of compute nodes are available. There are

NumberTypeVendorCPUGPUCores per CPUCores per NodeHyper-Threads
per Node
RAM [GB]
412dual-socket IntelXeon Skylake Gold 6148 n/a204080192
72 dual-socket IntelXeon Skylake Gold 6148 n/a204080772
139dual-socket IntelXeon Broadwell E5-2640 v4n/a102040128
112dual-socket AMD EPYC 7452 8x MI2103264128512

In order to separate the node types, we employ the concept of partitions. We provide three partitions for the nodes, one for the Skylake CPU nodes, one for the Broadwell and one for the GPU nodes. Furthermore there is a small test partition (7 nodes). You have to select the node type you prefer by setting the partition:

PartitionPartition/featureCPU
general1#SBATCH --partition=general1Intel Skylake CPU
general2#SBATCH --partition=general2
#SBATCH --constraint=broadwell
Intel Broadwell CPU
gpu#SBATCH --partition=gpu AMD EPYC 7452
test#SBATCH --partition=testIntel Skylake CPU

Per-User Resource Limits

On Goethe-HLR, you have the following limits for the partitions general1 and general2:

LimitValueDescription
MaxTime 21 days the maximum run time for jobs
MaxJobsPU 40 max. number of jobs a user is able to run simultaneously
MaxSubmitPU 50 max. number of jobs in running or pending state
MaxNodesPU 150 max. number of nodes a user is able to use at the same time
MaxArraySize 1001 the maximum job array size

For the partition gpu we have following limits:

LimitValueDescription
MaxTime 21 days the maximum run time for jobs
MaxJobsPU 20 max. number of jobs a user is able to run simultaneously
MaxSubmitPU 30 max. number of jobs in running or pending state
MaxNodesPU 40 max. number of nodes a user is able to use at the same time

For the partition test we have following limits:

LimitValueDescription
MaxTime 2 hours the maximum run time for jobs
MaxJobsPU 3 max. number of jobs a user is able to run simultaneously
MaxSubmitPU 4 max. number of jobs in running or pending state
MaxNodesPU 3 max. number of nodes a user is able to use at the same time

GPU Jobs

Since December 2020 GPU nodes are part of the cluster. Select the gpu partition by setting

#SBATCH --partition=gpu

Hyper-Threading

On compute nodes you can use Hyper-Threading. That means, in addition to each physical CPU core a virtual core is available. SLURM identifies all physical and virtual cores of a node, so that you have 80 logical CPU cores on an Intel Skylake node, 40 logical CPU cores on an Intel Broadwell or Ivy Bridge node, and 128 logical CPU cores on a GPU node. If you don't want to use HT, you can disable it by adding

Node typehyperthreading=OFF#cores
per node
hyperthreading=ON#cores
per node
Skylake #SBATCH --extra-node-info=2:20:140#SBATCH --extra-node-info=2:20:280
Broadwell / Ivy Bridge#SBATCH --extra-node-info=2:10:120#SBATCH --extra-node-info=2:10:240
AMD EPYC 7452 #SBATCH --extra-node-info=2:32:164#SBATCH --extra-node-info=2:32:2128

to your job script. Then you'll get half the threads per node (which will correspond to the number of cores). This can be beneficial in some cases (some jobs may run faster and/or more stable).

Bundling Single-Threaded Tasks

Note: Please also see the Job Arrays section below. Because only full nodes are given to you, you have to ensure, that the available resources are used efficiently. Please combine as many single-threaded jobs as possible into one. The limits for the number of combined jobs are given by the number of cores and the available memory. A simple job script to start 40 independent processes may look like this one:

#!/bin/bash
#SBATCH --partition=general1
#SBATCH --nodes=1
#SBATCH --ntasks=40
#SBATCH --cpus-per-task=1
#SBATCH --mem-per-cpu=2000
#SBATCH --time=01:00:00
#SBATCH --mail-type=FAIL
 
export OMP_NUM_THREADS=1
 
#
# Replace by a for loop.
 
./program input01 &> 01.out &
./program input02 &> 02.out &
 
...
 
./program input40 &> 40.out &
# Wait for all child processes to terminate.
wait

In this (SIMD) example we assume, that there is a program (called program) which is run 40 times on 40 different inputs (usually input files). Both output streams (stdout and stderr) of each process are redirected to a file N.out. A job script is always executed on the first allocated node, so we don't need to use srun, since exactly one node is allocated. Further we assume that the executable is located in the same directory where the job was submitted (that is the initial working directory).

Job Arrays

If you have a lot of single-core computations to run, job arrays are worth a look. Telling SLURM to run a job script as a job array will result in running that script multiple times (after the corresponding resources have been allocated). Each instance will have a distinct SLURM_ARRAY_TASK_ID variable defined in its environment.

Due to our full-node policy, you still have to ensure, that your jobs don't waste any resources. Let's say, you have 320 single-core tasks. In the following example 320 tasks are run inside a job array while ensuring that only 40-core nodes are used and that each node runs exactly 40 tasks in parallel.

#!/bin/bash
#SBATCH --partition=general1
#SBATCH --nodes=1
#SBATCH --ntasks=40
#SBATCH --cpus-per-task=1
#SBATCH --mem-per-cpu=2000
#SBATCH --time=00:10:00
#SBATCH --array=0-319:40
#SBATCH --mail-type=FAIL
 
my_task() {
     # Print the given "global task number" with leading zeroes
     # followed by the hostname of the executing node.
     K=$(printf "%03d" $1)
     echo "$K: $HOSTNAME"
 
     # Do nothing, just sleep for 3 seconds.
     sleep 3
}
 
#
# Every 40-task block will run on a separate node.
 
for I in $(seq 40); do
     # This is the "global task number". Since we have an array of
     # 320 tasks, J will range from 1 to 320.
     J=$(($SLURM_ARRAY_TASK_ID+$I))
 
     # Put each task into background, so that tasks are executed
     # concurrently.
     my_task $J &
 
     # Wait a little before starting the next one.
     sleep 1
done
 
# Wait for all child processes to terminate.
wait

If the task running times vary a lot, consider using the thread pool pattern. Have a look at GNU parallel, for instance.

OpenMP Jobs

For OpenMP jobs, set the --cpus-per-task parameter. As usual, you should also specify a --mem-per-cpu value. But in this case you have to divide the total RAM required by your program by the number of threads. E.g. if your application needs 8000 MB and you want to run 40 threads, then you have to set --mem-per-cpu=200 (8000/40 = 200). Don't forget to set the OMP_NUM_THREADS environment variable. Example:

#!/bin/bash
#SBATCH --partition=general1
#SBATCH --ntasks=1
#SBATCH --cpus-per-task=40
#SBATCH --mem-per-cpu=200
#SBATCH --mail-type=ALL
#SBATCH --time=48:00:00
 
export OMP_NUM_THREADS=$SLURM_CPUS_PER_TASK
./your_omp_program

MPI Jobs

Remember: Nodes are used exclusively. Each node has 40 CPU cores. If you want to run a lot of small jobs (i.e. where more than one job could be run on a single node concurrently), consider running more than one computation within a job. Otherwise it will most likely result in a waste of resources and will lead to a longer queueing time (for you and others).

See also: http://www.schedmd.com/slurmdocs/faq.html#steps

As an example, we want to run a program that spawns 80 MPI ranks and where 1200 MB of RAM are allocated for each rank.

#!/bin/bash
#SBATCH --partition=general1
#SBATCH --ntasks=80
#SBATCH --cpus-per-task=1
#SBATCH --mem-per-cpu=1200
#SBATCH --mail-type=ALL
#SBATCH --extra-node-info=2:20:1
#SBATCH --time=48:00:00
 
module load mpi/XXXX/....
export OMP_NUM_THREADS=1
mpirun ./your_mpi_program

Please note that some MPI installations support the srun command (instead of or in addition to mpirun), e.g.:
srun [--mpi=pmix] ./your_mpi_program

MPI implementations are typically designed to work seamlessly with job schedulers like Slurm. When you launch MPI tasks with mpirun (or srun) inside your job script, the MPI library uses the information provided by Slurm (via environment variables or other means) to determine the communication topology and allocate processes accordingly.

Hybrid Jobs: MPI/OpenMP

MVAPICH2 example script (40 ranks, 6 threads each and 200 MB per thread, i.e. 1.2 GB per rank; so, for 40*6 threads, you'll get six 40-core nodes):

#!/bin/bash
#SBATCH --partition=general1
#SBATCH --ntasks=40
#SBATCH --cpus-per-task=6
#SBATCH --mem-per-cpu=200
#SBATCH --mail-type=ALL
#SBATCH --extra-node-info=2:20:1
#SBATCH --time=48:00:00
 
export OMP_NUM_THREADS=6
export MV2_ENABLE_AFFINITY=0
mpirun -np 40 ./example_program

Please note, that this is just an example. You may or may not run it as-it-is with your software, which is likely to have a different scalability.

You have to disable the core affinity when running hybrid jobs with MVAPICH2. Otherwise all threads of an MPI rank will be pinned to the same core. Our example now includes the command

export MV2_ENABLE_AFFINITY=0

which disables this feature. The OS scheduler is now responsible for the placement of the threads during the runtime of the program. But the OS scheduler can dynamically change the thread placement during the runtime of the program. This leads to cache invalidation, which degrades performance. This can be prevented by thread pinning.

Memory Allocation

Normally the memory available per CPU thread is calculated by the whole amount of RAM divided by the number of threads. On GOETHE you can choose between four different types of nodes. For instance we select one Sylake node with 192GB of memory and divide it by 80 threads which is 2.4GB per thread 4). Keep in mind that the GOETHE clusters provides two threads per core. On GOETHE you have the convenience to select nodes with more memory but there is also another way to accomplish your job on an ordinary node with 192GB. Now imagine you need more memory as provided due to the selection of an ordinary Skylake node with 2.4GB per thread, we assume 8192MB per task. Type scontrol show node=<node> and look for 'mem', which is 187,5G(B). Another way is to login into a Skylake Node and type free -m, which states 192089M(B).5) Now we calculate how many processes we can launch on one node like this: 192089MB/8192MB=23.44… . With this result we can determine how many nodes we need. In the following example we like to have 71 processes à 8192MB.

#!/bin/bash
#SBATCH --job-name=<your_job_name>
#SBATCH --partition=fuchs
#SBATCH --ntasks=71            # Whole amount of processes we have
#SBATCH --cpus-per-task=1      # Only one task per CPU

##SBATCH --mem-per-cpu=8192    # We can't use this argument here, because it ends up with an error message
                               # therefore it's commented out.

#SBATCH --mem=0                # Instead we use this argument, which provides us the full memory of the node  
#SBATCH --ntasks-per-node=23   # We have calculated and rounded that we need 23 process on one node

srun hostname

# If everythins works fine you were granted 4 nodes, for example 3 nodes à 22 tasks and 1 node à 5 tasks
# 66 tasks + 5 tasks = 71 tasks, as requested.
Please keep in mind, that you might waste resources because you could have used 80 threads but only use 23/80 due to your memory requirement.

Local Storage

On each node there is up to 1.4 TB of local disk space (see also Storage). If you need local storage, you have to add the --tmp parameter to your SLURM script. Set the amount of storage in megabytes, e.g. set --tmp=5000 to allocate 5 GB of local disk space. The data in the local directory (/local/$SLURM_JOB_ID) is automatically deleted after the corresponding batch job has finished.

The salloc Command

For interactive workflows you can use SLURM's salloc command. With salloc almost the same options can be used as with sbatch, e.g.:

[user@loginnode ~]$ salloc --nodes=4 --time=0:45:00 --mem=100g --partition=test
salloc: Granted job allocation 197553
salloc: Waiting for resource configuration
salloc: Nodes node45-[002-005] are ready for job
[user@loginnode ~]$ 

Now you can ssh to the nodes that were allocated for the job and run further commands, e.g.:

[user@loginnode ~]$ ssh node45-002
[user@node45-002 ~]$ hostname
node45-002.cm.cluster
[user@node45-002 ~]$ logout
Connection to node45-002 closed.

Or you can use srun for running a command on all allocated nodes in parallel:

[user@loginnode ~]$ srun hostname
node45-002.cm.cluster
node45-003.cm.cluster
node45-005.cm.cluster
node45-004.cm.cluster

Finally you can terminate your interactive job session by running exit, which will free the allocated nodes:

[user@loginnode ~]$ exit
salloc: Relinquishing job allocation 197553

Planning Work

By using the --begin option it's possible to tell SLURM that you need the resources at some point in the future. Also, you might find it useful to use this feature for creating “user-mode reservations”. E.g.

  1. Submit a sleep job (allocate twenty intel20 nodes for 3 days), you can logout after running this command (but check the output of the squeue command first, if there is no corresponding pending job, then sth. went wrong):
    $ sbatch --begin=202X-07-23T08:00 --time=3-0 --nodes=20 \
      --partition=general2 --mem=120g \
      --wrap="sleep 3d"
  2. Wait until the time has come (07/23/202X 8:00am or later, there is no guarantee, that the allocation will be made on time, but the earlier you submit the job, the more likely you'll get the resources by that time).
  3. Find out whether the sleep job is running (i.e. is in R state) and run a new job step within that allocation (see also http://slurm.schedmd.com/faq.html#multi_batch):
    $ squeue
      JOBID    PARTITION     NAME   ST      TIME  NODES
    2717365     parallel   sbatch    R   3:28:29     20
    
    $ srun --jobid 2717365 hostname
    ...
    Note: Please note, that we are using the srun command. The sbatch command is not supported in this scenario.
  4. Finally, don't forget to release the allocation, if there's time left and the sleep job is still running:
    $ scancel 2717365

Special software

Install R in ${HOME}

Please note, R is readily available, you don't have to install it, unless you need a different version.

Howto install a local R from scratch in your /home directory without root access:

  1. Go to the main page https://cran.r-project.org. There you see the latest release. Copy the link.
  2. Go to to your preferred directory and do a
    wget https://cran.r-project.org/src/base/R-<X>/R-<X.Y.Z>.tar.gz
  3. tar xvf R-<X>/R-<X.Y.Z>.tar.gz
  4. Go to R-<X.Y.Z>
  5. Go to your terminal and type:
    ./configure --prefix=${HOME}/where/you/want/R/to/go
    make
    make install
  6. make install is not mandatory, you could go to R-<X.Y.Z>/bin and call R, but make install works with your prefix directory.
  7. Go to ${HOME}/prefix_R_directory/bin
  8. Don't forget to add the R directory to your path: export PATH=prefix_R_directory:${PATH}
  9. Type ./R
  10. Done.

R script for parallelization

Run with ''Rscript test.r''

# Rscript test.r
 
library(foreach)
library(doParallel)
library(MASS)
 
starts <- rep(100, 40)
fx <- function(nstart) kmeans(Boston, 4, nstart=nstart)
numCores <- detectCores()
numCores
 
system.time(
  results <- lapply(starts, fx)
)
 
system.time(
  results <- mclapply(starts, fx, mc.cores = numCores)
)
 
x <- iris[which(iris[,5] != "setosa"), c(1,5)]
trials <- seq(1, 10000)
boot_fx <- function(trial) {
  ind <- sample(100, 100, replace=TRUE)
  result1 <- glm(x[ind,2]~x[ind,1], family=binomial(logit))
  r <- coefficients(result1)
  res <- rbind(data.frame(), r)
}
 
system.time({
  results <- mclapply(trials, boot_fx, mc.cores = numCores)
})
 
registerDoParallel(numCores)  # use multicore, set to the number of our cores
foreach (i=1:3) %dopar% {
  sqrt(i)
}
 
# Return a vector
foreach (i=1:3, .combine=c) %dopar% {
  sqrt(i)
}
 
# Return a data frame
foreach (i=1:3, .combine=rbind) %dopar% {
  sqrt(i)
}
 
# Let's use the iris data set to do a parallel bootstrap
# From the doParallel vignette, but slightly modified
x <- iris[which(iris[,5] != "setosa"), c(1,5)]
trials <- 10000
 
system.time({
  r <- foreach(icount(trials), .combine=rbind) %dopar% {
    ind <- sample(100, 100, replace=TRUE)
    result1 <- glm(x[ind,2]~x[ind,1], family=binomial(logit))
    coefficients(result1)
  }
})
 
# And compare that to what it takes to do the same analysis in serial
system.time({
  r <- foreach(icount(trials), .combine=rbind) %do% {
    ind <- sample(100, 100, replace=TRUE)
    result1 <- glm(x[ind,2]~x[ind,1], family=binomial(logit))
    coefficients(result1)
  }
})
 
stopImplicitCluster()

1)
For further information on how to use rsync, please read its excellent man page.
2)
the first one of the two allocated nodes
3)
please see Memory Allocation for the new Slurm behavior
4)
In comparison to 768GB/80 threads = 9.6GB per thread when selecting the Skylake nodes with more memory
5)
except you have chosen Skylake nodes with 768GB
public/usage/goethe.txt · Last modified: 2024/02/09 13:19 by keiling
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