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slurm-user-guide

By | Beta

 Slurm User Guide for Beta

Slurm is a combined batch scheduler and resource manager that allows users to run their jobs on the University of Michigan’s high performance computing (HPC) clusters. This document describes the process for submitting and running jobs under the Slurm Workload Manager on Beta.

The Batch Scheduler and Resource Manager

The batch scheduler and resource manager work together to run jobs on an HPC cluster. The batch scheduler, sometimes called a workload manager, is responsible for finding and allocating the resources that fulfill the job’s request at the soonest available time.  When a job is scheduled to run, the scheduler instructs the resource manager to launch the application(s) across the job’s allocated resources.  This is also known as “running the job”.

The user can specify conditions for scheduling the job. One condition is the completion (successful or unsuccessful) of an earlier submitted job.  Other conditions include the availability of a specific license or access to a specific hardware accelerator.

Computing Resources

An HPC cluster is made up of a number of compute nodes, each with a complement of processors, memory and GPUs.  The user submits jobs that specify the application(s) they want to run along with a description of the computing resources needed to run the application(s).

Login Resources

Users interact with an HPC cluster through login nodes. Login nodes are a place where users can login, edit files, view job results and submit new jobs. Login nodes are a shared resource and should not be used to run application workloads.

Jobs and Job Steps

A job is an allocation of resources assigned to an individual user for a specified amount of time. Job steps are sets of (possibly parallel) tasks within a job. When a job runs, the scheduler selects and allocates resources to the job. The invocation of the application happens within the batch script, or at the command line for interactive and jobs.

When an application is launched using srun, it runs within a “job step”. The srun command causes the simultaneous launching of multiple tasks of a single application. Arguments to srun specify the number of tasks to launch as well as the number of nodes (and CPUs and memory) on which to launch the tasks.

srun can be invoked in parallel or sequentially (by backgrounding them). Furthermore, the number of nodes specified by srun (the -N option) can be less than but no more than the number of nodes (and CPUs and memory) that were allocated to the job.

srun can also be invoked directly at the command line (outside of a job allocation). Doing so will submit a job to the batch scheduler and srun will block until that job is scheduled to run. When the srun job runs, a single job step will be created. The job will complete when that job step terminates.

Batch Jobs

The sbatch command is used to submit a batch script to Slurm. It is designed to reject the job at submission time if there are requests or constraints that Slurm cannot fulfill as specified. This gives the user the opportunity to examine the job request and resubmit it with the necessary corrections. To submit a batch script simply run sbatch <scriptName>

$ sbatch myJob.sh

 

Anatomy of a Batch Job

The batch job script is composed of four main components:

  • The interpreter used to execute the script
  • #SBATCH directives that convey submission options.
  • The setting of environment and/or script variables (if necessary)
  • The application(s) to execute along with its input arguments and options

An Example Slurm job

#!/bin/bash
# The interpreter used to execute the script

#“#SBATCH” directives that convey submission options:

#SBATCH --job-name=example_job
#SBATCH --mail-user=uniqname@umich.edu
#SBATCH --mail-type=BEGIN,END
#SBATCH --cpus-per-task=1
#SBATCH --nodes=1
#SBATCH --ntasks-per-node=1
#SBATCH --mem-per-cpu=1000m 
#SBATCH --time=10:00
#SBATCH -A test
#SBATCH -p standard
#SBATCH --output=/home/%u/%x-%j.log

# The setting of environment and/or script variables (if necessary):
--export=EDITOR=/bin/vim

# The application(s) to execute along with its input arguments and options:

/bin/hostname
sleep 60

Common Job Submission Options

Option Slurm Command (sbatch) Beta Usage
Script directive #SBATCH #SBATCH
Job name --job-name=<name>

-J <name>

-J betajob1
Account --account=<account>

-A <account>

-A test
Queue --partition=standard

-p standard

-p standard

-p gpu (GPU jobs only)

Wall time limit --time=<hh:mm:ss> --time=02:00:00
Node count --nodes=<count>

-N <count>

-N 2
Process count per node --ntasks-per-node=<count> --ntasks-per-node=1
Core count (per process) --cpus-per-task=<cores> --cpus-per-task=1
Memory limit --mem=<limit> (Memory per node in MB) --mem=12000m
Minimum memory per processor --mem-per-cpu=<memory> --mem-per-cpu=1000m
Request GPUs --gres=gpu:<count> --gres=gpu:2
Job array --array=<array indices>
-a <array indices>
--array=0-15
Standard output file --output=<file path> (path must exist) --output=/scratch/pname_flux/uame/jobOut
Standard error file --error=<file path> (path must exist) --error=/scratch/pname_flux/uname/jobErr
Combine stdout/stderr to stdout --output=<combined out and err file path> --output=/scratch/pname_flux/uname/jobOut
Copy environment --export=ALL (default)

--export=NONE (to not export environment)

--export=ALL
Copy environment variable --export=<variable=value,var2=val2> --export=EDITOR=/bin/vim
Job dependency --dependency=after:jobID[:jobID...]

--dependency=afterok:jobID[:jobID...]

--dependency=afternotok:jobID[:jobID...]

--dependency=afterany:jobID[:jobID...]

--dependency=after:1234[:1233]
Request event notification

--mail-type=<events>

Note: multiple mail-type requests may be specified in a comma separated list:

--mail-type=BEGIN,END,NONE,FAIL,REQUEUE

--mail-type=BEGIN,END,FAIL

Email address --mail-user=<email address> --mail-user=uniqname@umich.edu
Defer job until the specified time --begin=<date/time> --begin=2020-12-25T12:30:00

Interactive Jobs

An interactive job is a job that returns a command line prompt (instead of running a script) when the job runs. Interactive jobs are useful when debugging or interacting with an application. The srun command is used to submit an interactive job to Slurm. When the job starts, a command line prompt will appear on one of the compute nodes assigned to the job. From here commands can be executed using the resources allocated on the local node.

[user@beta-login ~]$ srun --pty /bin/bash
srun: job 309 queued and waiting for resources
srun: job 309 has been allocated resources
[user@bn01 ~]$ hostname
bn01.stage.arc-ts.umich.edu
[user@bn01 ~]$

Jobs submitted with srun –pty /bin/bash will be assigned the cluster default values of 1 CPU and 1024MB of memory. If additional resources are required, they can be requested as options to the srun command. The following example job is assigned 2 nodes with 4 CPUS and 4GB of memory each:

[user@beta-login ~]$ srun --nodes=2 --ntasks-per-node=4 --mem-per-cpu=1GB --cpus-per-task=1 --pty /bin/bash
srun: job 894 queued and waiting for resources
srun: job 894 has been allocated resources
[user@bn01 ~]$ srun hostname
bn01.stage.arc-ts.umich.edu
bn02.stage.arc-ts.umich.edu
bn01.stage.arc-ts.umich.edu
bn01.stage.arc-ts.umich.edu
bn01.stage.arc-ts.umich.edu
bn02.stage.arc-ts.umich.edu
bn02.stage.arc-ts.umich.edu
bn02.stage.arc-ts.umich.edu

In the above example srun is used within the job from the first compute node to run a command once for every task in the job on the assigned resources. srun can be used to run on a subset of the resources assigned to the job. See the srun man page for more details.

GPU Jobs

Jobs can request GPUs with the job submission options --partition=gpu and --gres=gpu:<count>. GPUs can be requested in both Batch and Interactive jobs.

Job Dependencies

You may want to run a set of jobs sequentially, so that the second job runs only after the first one has completed. This can be accomplished using Slurm’s job dependencies options. For example, if you have two jobs, Job1.sh and Job2.sh, you can utilize job dependencies as in the example below.

[user@beta-login]$ sbatch Job1.sh
123213

[user@beta-login]$ sbatch --dependency=afterany:123213 Job2.sh
123214

The flag --dependency=afterany:123213 tells the batch system to start the second job only after completion of the first job. afterany indicates that Job2 will run regardless of the exit status of Job1, i.e. regardless of whether the batch system thinks Job1 completed successfully or unsuccessfully.

Once job 123213 completes, job 123214 will be released by the batch system and then will run as the appropriate nodes become available.

Exit status: The exit status of a job is the exit status of the last command that was run in the batch script. An exit status of ‘0’ means that the batch system thinks the job completed successfully. It does not necessarily mean that all commands in the batch script completed successfully.

There are several options for the –dependency flag that depend on the status of Job1:

–dependency=afterany:Job1 Job2 will start after Job1 completes with any exit status
–dependency=after:Job1 Job2 will start any time after Job1 starts
–dependency=afterok:Job1 Job2 will run only if Job1 completed with an exit status of 0
–dependency=afternotok:Job1 Job2 will run only if Job1 completed with a non-zero exit status

Making several jobs depend on the completion of a single job is done in the example below:

[user@beta-login]$ sbatch Job1.sh 
13205 
[user@beta-login]$ sbatch --dependency=afterany:13205 Job2.sh 
13206 
[user@beta-login]$ sbatch --dependency=afterany:13205 Job3.sh 
13207 
[user@beta-login]$ squeue -u $USER -S S,i,M -o "%12i %15j %4t %30E" 
JOBID        NAME            ST   DEPENDENCY                    
13205        Job1.bat        R                                  
13206        Job2.bat        PD   afterany:13205                
13207        Job3.bat        PD   afterany:13205             

Making a job depend on the completion of several other jobs: example below.

[user@beta-login]$ sbatch Job1.sh
13201
[user@beta-login]$ sbatch Job2.sh
13202
[user@beta-login]$ sbatch --dependency=afterany:13201,13202 Job3.sh
13203
[user@beta-login]$ squeue -u $USER -S S,i,M -o "%12i %15j %4t %30E"
JOBID        NAME            ST   DEPENDENCY                    
13201        Job1.sh         R                                  
13202        Job2.sh         R                                  
13203        Job3.sh         PD   afterany:13201,afterany:13202 

Chaining jobs is most easily done by submitting the second dependent job from within the first job. Example batch script:

#!/bin/bash

cd /data/mydir
run_some_command
sbatch --dependency=afterany:$SLURM_JOB_ID  my_second_job

Job dependencies documentation adapted from https://hpc.nih.gov/docs/userguide.html#depend

Job Arrays

Job arrays are multiple jobs to be executed with identical parameters.  Job arrays are submitted with -a <indices> or --array=<indices>. The indices specification identifies what array index values should be used.  Multiple values may be specified using a comma separated list and/or a range of values with a “-” separator: --array=0-15 or --array=0,6,16-32.

A step function can also be specified with a suffix containing a colon and number. For example,--array=0-15:4 is equivalent to --array=0,4,8,12.
A  maximum  number  of  simultaneously running tasks from the job array may be specified using a “%” separator.  For example --array=0-15%4 will limit the number of simultaneously running tasks from this job array to 4. The minimum index value is 0.  The maximum value is 499999.

Execution Environment

For each job type above, the user has the ability to define the execution environment. This includes environment variable definitions as well as shell limits (bash ulimit or csh limit). sbatch and salloc provide the --export option to convey specific environment variables to the execution environment. sbatch and salloc provide the --propagate option to convey specific shell limits to the execution environment. By default Slurm does not source the files ~./bashrc or ~/.profile when requesting resources via sbatch (although it does when running srun / salloc ).  So, if you have a standard environment that you have set in either of these files or your current shell then you can do one of the following:

  1. Add the command #SBATCH --get-user-env to your job script (i.e. the module environment is propagated).
  2. Source the configuration file in your job script:
< #SBATCH statements >
source ~/.bashrc

Note: You may want to remove the influence of any other current environment variables by adding #SBATCH --export=NONE to the script. This removes all set/exported variables and then acts as if #SBATCH --get-user-env has been added (module environment is propagated).

Environment Variables

Slurm recognizes and provides a number of environment variables.

The first category of environment variables are those that Slurm inserts into the job’s execution environment. These convey to the job script and application information such as job ID (SLURM_JOB_ID) and task ID (SLURM_PROCID). For the complete list, see the “OUTPUT ENVIRONMENT VARIABLES” section under the sbatchsalloc, and srun man pages.

The next category of environment variables are those use user can set in their environment to convey default options for every job they submit. These include options such as the wall clock limit. For the complete list, see the “INPUT ENVIRONMENT VARIABLES” section under the sbatchsalloc, and srun man pages.

Finally, Slurm allows the user to customize the behavior and output of some commands using environment variables. For example, one can specify certain fields for the squeue command to display by setting the SQUEUE_FORMAT variable in the environment from which you invoke squeue.

Commonly Used Environment Variables

Info Slurm Notes
Job name $SLURM_JOB_NAME
Job ID $SLURM_JOB_ID
Submit directory $SLURM_SUBMIT_DIR Slurm jobs starts from the submit directory by default.
Submit host $SLURM_SUBMIT_HOST
Node list $SLURM_JOB_NODELIST The Slurm variable has a different format to the PBS one.

To get a list of nodes use:

scontrol show hostnames $SLURM_JOB_NODELIST

Job array index $SLURM_ARRAY_TASK_ID
Queue name $SLURM_JOB_PARTITION
Number of nodes allocated $SLURM_JOB_NUM_NODES

$SLURM_NNODES

Number of processes $SLURM_NTASKS
Number of processes per node $SLURM_TASKS_PER_NODE
Requested tasks per node $SLURM_NTASKS_PER_NODE
Requested CPUs per task $SLURM_CPUS_PER_TASK
Scheduling priority $SLURM_PRIO_PROCESS
Job user $SLURM_JOB_USER
Hostname $HOSTNAME == $SLURM_SUBMIT_HOST Unless a shell is invoked on an allocated resource, the HOSTNAME variable is propagated (copied) from the submit machine environments will be the same on all allocated nodes.

Job Output

Slurm merges the job’s standard error and output by default and saves it to an output file with a name that includes the job ID (slurm-<job_ID>.out for normal jobs and "slurm-<job_ID_index.out for arrays"). You can specify your own output and error files to the sbatch command using the -o /file/to/output and -e /file/to/error options respectively. If both standard out and error should go to the same file, only specify -o /file/to/output Slurm will append the job’s output to the specified file(s). If you want the output to overwrite any existing files, add the --open-mode=truncate option. The files are written as soon as output is created. It does not spool on the compute node and then get copied to the final location after the job ends. If not specified in the job submission, standard output and error are combined and written into a file in the working directory from which the job was submitted.

For example if I submit job 93 from my home directory, the job output and error will be written to my home directory in a file called slurm-93.out. The file appears while the job is still running.

[user@beta-login ~]$ sbatch test.sh
Submitted batch job 93 
[user@beta-login ~]$ ll slurm-93.out
-rw-r–r– 1 user hpcstaff 122 Jun 7 15:28 slurm-93.out 
[user@beta-login ~]$ squeue 
JOBID PARTITION NAME    USER ST TIME NODES NODELIST(REASON) 
93    standard  example user R  0:04 1     bn02

If you submit from a working directory which is not a shared filesystem, your output will only be available locally on the compute node and will need to be copied to another location after the job completes. /home, /scratch, and /nfs are all networked filesystems which are available on the login nodes and all compute nodes.

For example if I submit a job from /tmp on the login node, the output will be in /tmp on the compute node.

[user@beta-login tmp]$ pwd
/tmp
[user@beta-login tmp]$ sbatch /home/user/test.sh
Submitted batch job 98
[user@beta-login tmp]$ squeue
JOBID PARTITION     NAME     USER ST  TIME  NODES NODELIST(REASON)
98    standard      example  user R   0:03  1     bn02
[user@beta-login tmp]$ ssh bn01
[user@bn01 ~]$ ll /tmp/slurm-98.out
-rw-r–r– 1 user hpcstaff 78 Jun 7 15:46 /tmp/slurm-98.out

Serial vs. Parallel jobs

Parallel jobs launch applications that are comprised of many processes (aka tasks) that communicate with each other, typically over a high speed switch. Serial jobs launch one or more tasks that work independently on separate problems.

Parallel applications must be launched by the srun command. Serial applications can use srun to launch them, but it is not required in one node allocations.

Job Partitions

A cluster is often highly utilized and may not be able to run a job when it is submitted. When this occurs, the job is placed in a partition. Specific compute node resources are defined for every job partition. The Slurm partition is synonymous with the term queue.

Each partition can be configured with a set of limits which specify the requirements for every job that can run in that partition. These limits include job size, wall clock limits, and the users who are allowed to run in that partition.

The Beta cluster currently has the “standard” partition, used for most production jobs.  The “gpu” partition is currently running a single node and should only be used for GPU-intensive tasks.

Commands related to partitions include:

sinfo Lists all partitions currently configured
scontrol show partition <name> Provides details about each partition
squeue Lists all jobs currently on the system, one line per job

Job Status

Most of a job’s specifications can be seen by invoking scontrol show job <jobID>.  More details about the job can be written to a file by using  scontrol write batch_script <jobID> output.txt. If no output file is specified, the script will be written to slurm<jobID>.sh.

Slurm captures and reports the exit code of the job script (sbatch jobs) as well as the signal that caused the job’s termination when a signal caused a job’s termination.

A job’s record remains in Slurm’s memory for 30 minutes after it completes.  scontrol show job will return “Invalid job id specified” for a job that completed more than 30 minutes ago.  At that point, one must invoke the sacct command to retrieve the job’s record from the Slurm database.

Modifying a Batch Job

Many of the batch job specifications can be modified after a batch job is submitted and before it runs.  Typical fields that can be modified include the job size (number of nodes), partition (queue), and wall clock limit.  Job specifications cannot be modified by the user once the job enters the Running state.

Beside displaying a job’s specifications, the scontrol command is used to modify them.  Examples:

scontrol -dd show job <jobID> Displays all of a job’s characteristics
scontrol write batch_script <jobID> Retrieve the batch script for a given job
scontrol update JobId=<jobID> Account=science Change the job’s account to the “science” account
scontrol update JobId=<jobID> Partition=priority Changes the job’s partition to the priority partition

Holding and Releasing a Batch Job

If a user’s job is in the pending state waiting to be scheduled, the user can prevent the job from being scheduled by invoking the scontrol hold <jobID> command to place the job into a Held state. Jobs in the held state do not accrue any job priority based on queue wait time.  Once the user is ready for the job to become a candidate for scheduling once again, they can release the job using the scontrol release <jobID> command.

Signalling and Cancelling a Batch Job

Pending jobs can be cancelled (withdrawn from the queue) using the scancel command (scancel <jobID>).  The scancel command can also be used to terminate a running job.  The default behavior is to issue the job a SIGTERM, wait 30 seconds, and if processes from the job continue to run, issue a SIGKILL command.

The -s option of the scancel command (scancel -s <signal> <jobID>) allows the user to issue any signal to a running job.

Common Job Commands

Command Slurm
Submit a job sbatch <job script>
Delete a job scancel <job ID>
Job status (all) squeue
Job status (by job) squeue -j <job ID>
Job status (by user) squeue -u <user>
Job status (detailed) scontrol show job -dd <job ID>
Show expected start time squeue -j <job ID> --start
Queue list / info scontrol show partition <name>
Node list scontrol show nodes
Node details scontrol show node <node>
Hold a job scontrol hold <job ID>
Release a job scontrol release <job ID>
Cluster status sinfo
Start an interactive job salloc <args>srun --pty <args>
X forwarding srun --pty <args> --x11(Update with --x11 once 17.11 is released)
Read stdout messages at runtime No equivalent command / not needed. Use the --output option instead.
Monitor or review a job’s resource usage sacct -j <job_num> --format JobID,jobname,NTasks,nodelist,CPUTime,ReqMem,Elapsed

(see sacct for all format options)

View job batch script scontrol write batch_script <jobID> [filename]
View accounts you can submit to sacctmgr show assoc user=$USER
View users with access to an account sacctmgr show assoc account=<account>
View default submission account and wckey sacctmgr show User <account>

Job States

The basic job states are these:

  • Pending – the job is in the queue, waiting to be scheduled
  • Held – the job was submitted, but was put in the held state (ineligible to run)
  • Running – the job has been granted an allocation.  If it’s a batch job, the batch script has been run
  • Complete – the job has completed successfully
  • Timeout – the job was terminated for running longer than its wall clock limit
  • Preempted – the running job was terminated to reassign its resources to a higher QoS job
  • Failed – the job terminated with a non-zero status
  • Node Fail – the job terminated after a compute node reported a problem

For the complete list, see the “JOB STATE CODES” section under the squeue man page.

Pending Reasons

A pending job can remain pending for a number of reasons:

  • Dependency – the pending job is waiting for another job to complete
  • Priority – the job is not high enough in the queue
  • Resources – the job is high in the queue, but there are not enough resources to satisfy the job’s request
  • Partition Down – the queue is currently closed to running any new jobs

For the complete list, see the “JOB REASON CODES” section under the squeue man page.

Displaying Computing Resources

As stated above, computing resources are nodes, CPUs, memory, and generic resources like GPUs.  The resources of each compute node can be seen by running the scontrol show node command.  The characteristics of each partition can be seen by running the scontrol show partition command.  Finally, a load summary report for each partition can be seen by running sinfo.

To show a summary of cluster resources on a per partition basis:

[user@beta-login ~]$ sinfo
PARTITION     AVAIL    TIMELIMIT    NODES STATE   NODELIST
standard*     up       14-00:00:00  5     comp    bn[16-20]
standard*     up       14-00:00:00  15    idle    bn[01-15]
gpu           up       14-00:00:00  1     idle    bn15
[user@beta-login ~]$ sstate
———————————————————————————————————————
Node    AllocCPU TotalCPU PercentUsedCPU  CPULoad AllocMem TotalMem PercentUsedMem NodeState
———————————————————————————————————————
bn01    0        16       0.00            0.03    0        64170    0.00           IDLE
bn02    0        16       0.00            0.04    0        64170    0.00           IDLE
bn03    0        16       0.00            0.05    0        64170    0.00           IDLE
bn04    0        16       0.00            0.01    0        64170    0.00           IDLE
bn05    0        16       0.00            0.04    0        64170    0.00           IDLE
bn06    0        16       0.00            0.05    0        64170    0.00           IDLE
bn07    0        16       0.00            0.03    0        64170    0.00           IDLE
bn08    0        16       0.00            0.04    0        64170    0.00           IDLE
bn09    0        16       0.00            0.08    0        64221    0.00           IDLE
bn10    0        16       0.00            0.05    0        64170    0.00           IDLE
bn11    0        16       0.00            0.02    0        64170    0.00           IDLE
bn12    0        16       0.00            0.07    0        64170    0.00           IDLE
bn13    0        16       0.00            0.01    0        64170    0.00           IDLE
bn14    0        16       0.00            0.03    0        64170    0.00           IDLE
bn15    0        16       0.00            0.02    0        64224    0.00           IDLE
bn16    0        16       0.00            0.06    0        64170    0.00           IDLE
bn17    0        16       0.00            0.03    0        64170    0.00           IDLE
bn18    0        16       0.00            0.03    0        64221    0.00           IDLE
bn19    0        16       0.00            0.02    0        64170    0.00           IDLE
bn20    0        16       0.00            0.07    0        64170    0.00           IDLE
———————————————————————————————————————
Totals:
Node    AllocCPU TotalCPU PercentUsedCPU  CPULoad AllocMem TotalMem PercentUsedMem NodeState
———————————————————————————————————————
20      0        320      0.00                    0        1283556  0.00

In this example the user “user” has access to submit workloads to the accounts support and hpcstaff on the Beta cluster. To show associations for the current user:

[user@beta-login ~]$ sacctmgr show assoc user=$USER

Cluster  Account  User  Partition  ...
———————————————————————————————————————
beta     support  user  1    
beta     hpcstaff user  1

Job Statistics and Accounting

The sreport command provides aggregated usage reports by user and account over a specified period. Examples:

By user: sreport -T billing cluster AccountUtilizationByUser Start=2017-01-01 End=2017-12-31

By account: sreport -T billing cluster UserUtilizationByAccount Start=2017-01-01 End=2017-12-31

For all of the sreport options see the sreport man page.

Time Remaining in an Allocation

If a running application overruns its wall clock limit, all its work could be lost. To prevent such an outcome, applications have two means for discovering the time remaining in the application.

The first means is to use the sbatch --signal=<sig_num>[@<sig_time>] option to request a signal (like USR1 or USR2) at sig_time number of seconds before the allocation expires. The application must register a signal handler for the requested signal in order to to receive it. The handler takes the necessary steps to write a checkpoint file and terminate gracefully.

The second means is for the application to issue a library call to retrieve its remaining time periodically. When the library call returns a remaining time below a certain threshold, the application can take the necessary steps to write a checkpoint file and terminate gracefully.

Slurm offers the slurm_get_rem_time() library call that returns the time remaining. On some systems, the yogrt library (man yogrt) is also available to provide the time remaining.

Beta Configuration

By | Beta, HPC

Beta Configuration

Hardware

Computing

The Beta hardware is a subset of the hardware currently used in Flux.

Networking

The compute nodes are all interconnected with InfinBand networking. In addition to the InfiniBand networking, there is a gigabit Ethernet network that also connects all of the nodes. This is used for node management and NFS file system access.

Storage

The high-speed scratch file system is based on Lustre v2.5 and is a DDN SFA10000 backed by the hardware described in this table, the same that is used in current Flux. All other group volumes use the same storage as current Flux.

Operation

Computing jobs on Beta are managed completely through Slurm.  See the Beta User Guide for directions on how to submit and manage jobs.

Software

There are three layers of software on Beta.

Operating Software

The Beta cluster runs CentOS 7. We update the operating system on Beta as CentOS releases new versions and our library of third-party applications offers support. Due to the need to support several types of drivers (AFS and Lustre file system drivers, InfiniBand network drivers and NVIDIA GPU drivers) and dozens of third party applications, we are cautious in upgrading and can lag CentOS’s releases by months.

Compilers and Parallel and Scientific Libraries

Beta supports the Gnu Compiler Collection, the Intel Compilers, and the PGI Compilers for C and Fortran. The Beta cluster’s parallel library is OpenMPI, and the default versions are 1.10.7 (i686) and 3.1.2 (x86_64), and there are limited earlier versions available.  Beta provides the Intel Math Kernel Library (MKL) set of high-performance mathematical libraries. Other common scientific libraries are compiled from source and include HDF5, NetCDF, FFTW3, Boost, and others.

Please contact us if you have questions about the availability of, or support for, any other compilers or libraries.

Application Software

Beta supports a wide range of application software. We license common engineering simulation software, for example, Ansys, Abaqus, VASP, and we compile other for use on Beta, for example, OpenFOAM and Abinit. We also have software for statistics, mathematics, debugging and profiling, etc. Please contact us if you wish to inquire about the current availability of a particular application.

GPUs

Beta has eight K20x GPUs on one node for testing GPU workloads under Slurm.

 
GPU Model NVidia K20X
Number and Type of GPU one Kepler GK110
Peak double precision floating point perf. 1.31 Tflops
Peak single precision floating point perf. 3.95 Tflops
Memory bandwidth (ECC off) 250 GB/sec
Memory size (GDDR5) 6 GB
CUDA cores 2688

If you have questions, please send email to hpc-support@umich.edu.

Getting Access

Beta is intended for small scale testing to convert Torque/PBS scripts to Slurm. No sensitive data of any type should be used on Beta.

To request:

1. Fill out the ARC-TS HPC account request form.

Because this is a test platform, there is no cost for using Beta.

Related Event

October 22 @ 12:00 pm - 1:00 pm

MICDE/Quantitative Biology Seminar: Padmini Rangamani, Mechanical and Aerospace Engineering, UC San Diego

Bio: Padmini Rangamani is an associate professor in Mechanical Engineering at the University of California, San Diego. She joined the department in July 2014. Earlier, she was a UC Berkeley…

October 23 @ 2:00 pm - 4:00 pm

Introduction to Deep Neural Networks with Keras/TensorFlow

Deep Neural Networks (DNNs) are used as a machine learning method for both regression and classification problems. Keras is a high-level, Python interface running on top of multiple neural network libraries, including…

October 24 @ 9:00 am - 11:00 am

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October 24 @ 3:00 pm - 4:00 pm

MICDE Seminar: Juan Pablo Vielma, Sloan School of Management, MIT

Bio: Juan Pablo Vielma is the Richard S. Leghorn (1939) Career Development Associate Professor at MIT Sloan School of Management and is affiliated to MIT’s Operations Research Center. Dr. Vielma…

Beta

By | Beta

Beta is the Linux-based high-performance computing (HPC) test cluster available to all researchers at the University of Michigan.  It is available to all current Flux users.

Beta is intended only for non-commercial, academic research and instruction. It is specifically suited to testing scripts and is also not intended for production-level research.  No PHI or sensitive data may be stored or processed.

Beta consists of several compute nodes, each with 16 AVX cores (Intel(R) Xeon(R) CPU E5-2670 @ 2.60GHz) and 64 GB of RAM, interconnected with 40Gb/s InfiniBand networking.  It is using the Slurm workload manager.

Getting Access:

All current Flux users have access. To login, please SSH to beta.arc-ts.umich.edu (Duo authentication required).

For technical support, please email hpc-support@umich.edu.

For additional information on using Beta:

Getting Access

Beta is intended for small scale testing to convert Torque/PBS scripts to Slurm.

To request:

Fill out the ARC-TS HPC account request form.

Because this is a test platform, there is no cost for using Beta.

Related Events

There are no upcoming events at this time.

Related Links

ARC-TS HPC Rates

Great Lakes

By | Systems and Services

Advanced Research Computing – Technology Services (ARC-TS) is creating a new, campus-wide computing cluster, “Great Lakes,” that will serve the broad needs of researchers across the University. Over time, Great Lakes will replace Flux, the shared research computing cluster that currently serves over 300 research projects and 2,500 active users.

The Great Lakes cluster will be available to all researchers on campus for simulation, modeling, machine learning, data science, genomics, and more. The platform will provide a balanced combination of computing power, I/O performance, storage capability, and accelerators.

ARC-TS is in the process of building the cluster. A “Beta” cluster will be available to help researchers learn the new system before Great Lakes is deployed in the first half of 2019.

Based on extensive input from faculty and other stakeholders across campus, the new Great Lakes cluster will be designed to deliver similar services and capabilities as Flux, including the ability to accommodate faculty purchases of hardware (as in the current Flux Operating Environment service), access to GPUs and large-memory nodes, and improved support for emerging uses such as machine learning and genomics. Great Lakes will consist of approximately 15,000 cores.

For technical support, please email hpc-support@umich.edu.

Current status

October 16, 2018: U-M announces selection of Dell, Mellanox and DDN to supply Great Lakes. Read more…

Order Service

Great Lakes will be available in the first half of 2019. This page will provide updates on the progress of the project.

Please contact hpc-support@umich.edu with any questions.

Yottabyte Research Cloud

By | Systems and Services

yb-logoThe Yottabyte Research Cloud is a partnership between ARC and Yottabyte that provides U-M researchers with high performance, secure and flexible computing environments enabling the analysis of sensitive data sets restricted by federal privacy laws, proprietary access agreements, or confidentiality requirements.

The system is built on Yottabyte’s composable, software-defined infrastructure platform, called Cloud Composer and represents U-M’s first use of software-defined infrastructure for research, allowing the on-the-fly personalized configuration of any-scale computing resources.

Cloud Composer software inventories the physical CPU, RAM and storage components of Cloud Blox appliances into definable and configurable virtual resource groups that may be used to build multi-tenant, multi-site infrastructure as a service.

See the Sept. 2016 press release for more information.

The YRBC platform can accommodate sensitive institutional data classified up to High — including CUI — as identified in the Sensitive Data Guide.

Capabilities

The Yottabyte Research Cloud supports several existing and planned platforms for researchers at the University of Michigan:

  • Data Pipeline Tools, which include databases, message buses, data processing and storage solutions. This platform is suitable for sensitive institutional data classified up to High — including CUI, and data that is not classified as sensitive.
  • Research Database Hosting, an environment that can house research-focused data stored in a number of different database engines.
  • Glovebox, a virtual desktop service for researchers who have sensitive institutional data classified up to High — including CUI — and require higher security. (planned)
  • Virtual desktops for research. This service is similar to Glovebox but is suitable for data that is not classified as sensitive. (planned)
  • Docker Container Service. This service can take any research application that can be containerized for deployment. This service will be suitable forfor sensitive institutional data classified up to High — including CUI, and data that is not classified as sensitive. (planned)

Researchers who need to use Hadoop or Spark for data-intensive work should explore ARC-TS’s separate Hadoop cluster.

Contact arcts-support@umich.edu for more information.

Hardware

The system deploys 40 high performance Hyperconverged YottaBlox nodes (H2400i-E5), each consisting of two, Intel Xeon E5-2680V4 CPU (1,120 cores total), 512GB DDR4 2400MHz RAM (20,480GB total), dual port 40GbE network adapters (80 total) and (2) 800GB NVMe SSD DC P3700 drives (64TB); and 20 storage YottaBlox nodes (S2400i-E5-HDD), each consisting of two, Intel Xeon E5-2620V4 CPU (320 cores total), 128 GB DDR4 2133MHz RAM (2,560 GB total), quad port 10GbE network adapters (80 total),  (2) 800 GB DC S3610 SSD (32 TB total) and 12 x 6 TB 7200 RPM (1,440TB total).

Access

These tools are offered to all researchers at the University of Michigan free of charge, provided that certain usage restrictions are not exceeded. Large-scale users who outgrow the no-cost allotment may purchase additional YBRC resources. All interested parties should contact arcts-support@umich.edu.

Sensitive Data

The U-M Research Ethics and Compliance webpage on Controlled Unclassified Information provides details on handling this type of data. The U-M Sensitive Data Guide to IT Services is a comprehensive guide to sensitive data.

Order Service

The Yottabyte Research Cloud is a pilot program available to all U-M researchers.

Access to Yottabyte Research Cloud resources involves a single email to us at arcts-support@umich.edu. Please include:

  • Your name or your advisor’s name
  • Your unit
  • What you would like to use YBRC for
  • Whether you plan to use restricted data.

Someone from your unit IT staff or an ARC-TS IT staff member will reach out to you and arrange details to determine the best path to make your request work within the Yottabyte Cloud environment.

General Questions

What is the Yottabyte Research Cloud?

The Yottabyte Research Cloud (YBRC) is the University’s private cloud environment for research.   It’s a collection of processors, memory, storage, and networking that can be subdivided into smaller units and allocated to research projects on an as-needed basis to be accessed by virtual machines and containers.

How do I get access to Yottabyte Research Cloud Resources?

Access to Yottabyte Research Cloud resources involves a single email to us at arcts-support@umich.edu. Please include:

  • Your name or your advisor’s name
  • Your unit
  • What you would like to use YBRC for
  • Whether you plan to use restricted data.

Someone from your unit IT staff or an ARC-TS IT staff member will reach out to you and arrange details to determine the best path to make your request work within the Yottabyte Cloud environment.  

What class of problems is Yottabyte Research Cloud designed to solve?

Yottabyte Research Cloud resources are aimed squarely at research and the teaching and training of students involved in research.  Primarily, Yottabyte resources are for sponsored research.  Yottabyte Research Cloud is not for administrative or clinical use (business of the university or the hospital).  Clinical research is acceptable as long as it is sponsored research.  

How large is the Yottabyte Research Cloud?

In total, Yottabyte Research Cloud (YBRC) has 960 processing cores for each Yottabyte cluster, 7.5 Terabytes, and roughly 330 TB of scratch storage available in Maize and Blue each.   

What does Maize Yottabyte Research Cloud and Blue Yottabyte Research Cloud stand for?

Yottabyte resources are divided up between two clusters of computing and storage.    Maize YBRC is for restricted data analyses and storage, and Blue YBRC is for unrestricted data analyses and storage.

What can I do with the Yottabyte Research Cloud?

The initial offering of YBRC is focused on a few different types of use cases:  

  1. Database hosting and data ingestion of streaming data from an external source into a database. We can host many types of databases within Yottabyte, including most structured and unstructured databases.  Examples include MariaDB, PostgreSQL, and MongoDB.
  2. Hosting for applications that you can’t host locally in your lab or you would like to connect to our HPC and data science clusters, such as Material Studio, Galaxy, and SAS Studio.
  3. Hosting of Virtual Desktops and Servers for restricted data use cases, such as statistical analysis of health data, or an analytical project for Controlled Unsecured Information (CUI).  Most people in this case may need a powerful workstation for SAS, Stata or R analyses, for example, or some other application.  

Are these the only things I can do with resources in the Yottabyte Research Cloud?

No!  Contact us at arcts-support@umich.edu if you want to learn whether or not your idea can be done within YBRC!  

How do I get help if I have an issue with something in Yottabyte?

The best way to get help is to send an email to arcts-support@umich.edu with a brief description of the issues that you are seeing.  

What are the support hours for the Yottabyte Research Cloud?

Yottabyte is supported between the hours of 9am to 5pm Monday through Friday.  Response times for support outside of these hours will be longer.

Usage Questions

What’s the biggest machine I can build within Yottabyte Research Cloud?

Because of the way that YBRC divides up resources, the largest Virtual Machine within the cluster is 16 processing cores, and 128 GB of RAM.  

How many Yottabyte Research Cloud resources am I able to access at no cost?

ARC-TS policy is to limit no-cost individual allocations to 100 cores, so that access is always open to multiple research groups.

What if I need more than the no-cost maximum?

If you need to use more than 100 cores of YBRC, we recommend that you purchase YBRC physical infrastructure of your own and add it to the cluster.  Physical infrastructure can be purchased in 96 physical core chunks, which can be oversubscribed as memory allows.  For every block purchased, the researcher will also receive 4 years of hardware and OS support for that block in the case of failure.  For a cost estimate of buying your own blocks of infrastructure and adding to the cluster, please email arcts-support@umich.edu.  

What is ‘scratch’ storage?

Scratch storage for Yottabyte Research Cloud is the storage area network that OS storage and active data storage on the local virtual machines that are not actively being backed up or replicated to a separate infrastructure.  Like the scratch storage on Flux, we don’t recommend storing any data solely on the local disk of any machines.  Make sure that you have backups on other machines, like Turbo, Locker, or some other service.  

HIPAA Compliance Questions

What can I do inside of an HIPAA network enclave?

For researchers with restricted data with a HIPAA classification, we provide a small menu of Linux and Windows workstations to be installed within your enclave.  We do not delegate administrative rights for those workstations to researchers or research staff.  We may delegate administrative rights for workstations and services in your enclaves to IT staff in your unit who have successfully completed the HIPAA IT training coursework given by ITS or HITS, and are familiar with desktop and virtual machine environments.  

Machines in the HIPAA network enclaves are encircled by a deny first firewall that prevents most traffic from entering the enclaves.  Researchers can still visit external-to-campus websites from within a HIPAA network enclave.  Researchers within a HIPAA network enclave can use storage services such as Turbo and MiStorage Silver (via CIFS) to host data for longer-term storage.

What are a researcher and research group responsibilities when they have HIPAA data within YBRC?

All researchers, staff, and students that use YBRC when analyzing restricted data have a shared responsibility in keeping their restricted data secure.

  • Researchers need to be aware of the personnel in their labs who have access to the data in their enclaves.  
    • Each lab should have a process for adding and removing users from enclaves that includes removing departed lab members from access to restricted data as soon as possible after they have left the lab.
    • Each lab should review who has access to their data and enclaves on a twice yearly basis via checking the memberships of their M-Community and Active Directory groups to ensure that people have been removed as requested.  
  • Each lab user must store their restricted data in a specific directory, as discussed during their introductory meeting with YBRC staff.  They must keep the data only in this directory over the life of the data on the system.  

CUI Compliance Questions

What can I do inside of a Secure Enclave Service CUI enclave?

Staff will work with researchers using CUI-classified data to determine the types of analysis that can be conducted on YBRC resources that comply with relevant regulations.

What are a researcher and research group responsibilities when they have CUI data within YBRC?

All researchers, staff, and students that use YBRC when analyzing restricted data have a shared responsibility in keeping their restricted data secure.

  • Researchers need to be aware of the personnel in their labs who have access to the data in their enclaves.  
    • Each lab should have a process for adding and removing users from enclaves that includes removing departed lab members from access to restricted data as soon as possible after they have left the lab.
    • Each lab should review who has access to their data and enclaves on a twice yearly basis via checking the memberships of their M-Community and Active Directory groups to ensure that people have been removed as requested.  
  • Each lab user must store their restricted data in a specific directory, as discussed during their introductory meeting with YBRC staff.  They must keep the data only in this directory over the life of the data on the system.  

ConFlux

By | Systems and Services

conflux1-300x260ConFlux is a cluster that seamlessly combines the computing power of HPC with the analytical power of data science. The next generation of computational physics requires HPC applications (running on external clusters) to interconnect with large data sets at run time. ConFlux provides low latency communications for in- and out- of-core data, cross-platform storage, as well as high throughput interconnects and massive memory allocations. The file-system and scheduler natively handle extreme-scale machine learning and traditional HPC modules in a tightly integrated work flow — rather than in segregated operations — leading to significantly lower latencies, fewer algorithmic barriers and less data movement.

The ConFlux cluster is built with ~58 IBM Power8 CPU two-socket “Firestone” S822LC compute nodes providing 20 cores in each.  Seventeen Power8 CPU two-socket “Garrison” S822LC compute nodes provide an additional 20 cores and host four NVIDIA Pascal GPUs connected via NVIDIA’s NVLink technology to the Power8 system bus.  Each GPU based node has a local high-speed NVMe flash memory for random access.

All compute and storage is connected via a 100 Gb/s InfiniBand fabric. The IBM and NVLink connectivity, combined with IBM CAPI Technology provide an unprecedented data transfer throughput required for the data-driven computational physics researchers will be conducting.

ConFlux is funded by a National Science Foundation grant; the Principal Investigator is Karthik Duraisamy, Assistant Professor of Aerospace Engineering and Director of the Center for Data-Driven Computational Physics (CDDCP). ConFlux and the CDDCP are under the auspices of the Michigan Institute for Computational Discovery and Engineering.

Order Service

A portion of the cycles on ConFlux will be available through a competitive application process. More information will be posted as it becomes available.

Close up of Armis cluster

Armis (HIPAA-aligned HPC Cluster)

By | Systems and Services

The Armis HPC cluster, in conjunction with Turbo Research Storage, provides a secure, scalable, and distributed computing environment that aligns with HIPAA privacy standards. The HPC environment is composed of a task-managing administrative nodes, standard Linux-based 2 and 4 socket server class hardware in a secure data center, connected by both a high-speed ethernet (1 Gbps) and InfiniBand network (40Gbps), and a secure parallel filesystem for temporary data, provided by HIPAA-aligned Turbo Research Storage. Armis is currently provided as a pilot service.

Order Service

Armis is currently offered as a pilot program. To request an Armis account, please fill out this form.

Please see the Terms of Usage for more information.

Related Events

There are no upcoming events at this time.

XSEDE

XSEDE

By | Systems and Services

xsedebanner

The Extreme Scale Engineering and Discovery Environment (XSEDE) is an NSF-funded service that provides computing resources to institutions across the country. XSEDE is an open scientific discovery infrastructure combining leadership class resources at eleven partner sites to create an integrated, persistent computational resource.

ARC-TS participates in the XSEDE Campus Champion program, facilitating access to the organization’s resources. Contact Brock Palen for more information.

For general information on XSEDE, visit the XSEDE home page.

Order Service

Visit the XSEDE User Portal for information on getting an XSEDE allocation.

Related Event

October 22 @ 12:00 pm - 1:00 pm

MICDE/Quantitative Biology Seminar: Padmini Rangamani, Mechanical and Aerospace Engineering, UC San Diego

Bio: Padmini Rangamani is an associate professor in Mechanical Engineering at the University of California, San Diego. She joined the department in July 2014. Earlier, she was a UC Berkeley…

October 23 @ 2:00 pm - 4:00 pm

Introduction to Deep Neural Networks with Keras/TensorFlow

Deep Neural Networks (DNNs) are used as a machine learning method for both regression and classification problems. Keras is a high-level, Python interface running on top of multiple neural network libraries, including…

October 24 @ 9:00 am - 11:00 am

Mixed Effects Modeling in Stata

We’ll discuss mixed model regression (also known as multi-level models or hierarchical linear models) in this session which is used for repeated measures data or data which has a clustering…

October 24 @ 3:00 pm - 4:00 pm

MICDE Seminar: Juan Pablo Vielma, Sloan School of Management, MIT

Bio: Juan Pablo Vielma is the Richard S. Leghorn (1939) Career Development Associate Professor at MIT Sloan School of Management and is affiliated to MIT’s Operations Research Center. Dr. Vielma…

Flux HPC Cluster

By | Systems and Services

The Flux HPC ClusterFlux is the shared, Linux-based high-performance computing (HPC) cluster available to all researchers at the University of Michigan.

Flux consists of approximately 27,000 cores – including 1,372 compute nodes composed of multiple CPU cores, with at least 4 GB of RAM per core, interconnected with InfiniBand networking.

Please see the following pages for more information on Flux:

For technical support, please email hpc-support@umich.edu.

Unit-specific Flux Allocations

Flux Operating Environment

The Flux Operating Environment (FOE) supports researchers with grants that require the purchase of computing hardware. FOE allows researchers to place their own hardware within the Flux cluster.

For more information, visit our FOE page.

Flux On Demand

Flux on Demand (FOD) allows users to run jobs as needed without committing to a month-long allocation. FOD may be the right choice for users with sporadic workloads that don’t result in consistent sets of jobs run over the course of a month. FOD jobs have access to 3,900 Standard Flux processors.

To create a Flux On Demand allocation, email hpc-support@umich.edu with the list of users who should have access to the account. See the ARC-TS Computing Resources Rates page for details on the costs of Flux On Demand.

Large Memory Flux

Flux has 360 cores with larger amounts of RAM — about 25GB per core, or 1TB in a 40-core node. Large Memory Flux is designed for researchers with codes requiring large amounts of RAM or cores in a single system.

For information on determining the size of a Flux allocation, please see our pages on How Flux WorksSizing a Flux Order, and Managing a Flux Project.

GPUs

Flux has 24 K20x GPUs connected to 3 compute nodes,  24 K40 GPUs connected to 6 nodes, and 12 TITANV GPUs connected to 3 nodes. These are available for researchers who have applications that can benefit from the acceleration provided by GPU co-processors. In addition, the software library on Flux has several programs that can benefit from these accelerators.

Each GPU allocation comes with 2 compute cores and 8GB of CPU RAM.

FluxG GPU Specifications

GPU Model NVidia K20X NVidia K40 NVidia TITANV
Number and Type of GPU one Kepler GK110 Kepler GK110B GV100
Peak double precision floating point perf. 1.31 Tflops 1.43 Tflops 7.5 Tflops
Peak single precision floating point perf. 3.95 Tflops 4.29 Tflops 15 Tflops
Tensor Performance (Deep Learning) 110 Tflops
Memory bandwidth (ECC off) 250 GB/sec 288 GB/sec 652.8 GB/sec
Memory size (GDDR5) 6 GB 12 GB 12 GB
CUDA cores 2688 2880 5120 (single precision)

If you have questions, please send email to hpc-support@umich.edu.

Order Service

For information on determining the size of a Flux allocation, please see our pages on How Flux Works, Sizing a Flux Order, and Managing a Flux Project.

To order:

1. Fill out the ARC-TS HPC account request form.

2. Email hpc-support@umich.edu with the following information:

  • the number of cores needed
  • the start date and number of months for the allocation
  • the shortcode for the funding source
  • the list of people who should have access to the allocation
  • the list of people who can change the user list and augment or end the allocations.

For information on costs, visit our Rates page.

Related Events

There are no upcoming events at this time.