Great Lakes Timeline

By | Great Lakes, HPC

Dates in the future are subject to change. We use our best estimates given what we know today.

No upcoming events

Past Events

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

Order Service

Billing for the Great Lakes service began on January 6, 2020. Existing, active Flux accounts and logins have been added to the Great Lakes Cluster. Complete this form to get a new Great Lakes cluster login.

If you would like to create a Great Lakes Cluster account or have any questions, contact arcts-support@umich.edu with lists of users, admins, and a shortcode. UMRCP accounts are also available to eligible researchers. For more information, please visit our UMRCP page.

Beta Timeline

By | Beta, HPC

Dates in the future are subject to change. We use our best estimates given what we know today.

No upcoming events

If you have questions, please send an email to arcts-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

Fill out the HPC account request form.

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

Related Events

February 1 @ 1:00 pm - 4:00 pm

Introduction to Research Computing on the Great Lakes Cluster

OVERVIEW This workshop will introduce you to high performance computing on the Great Lakes cluster.  After a brief overview of the components of the cluster and the resources available there, the main…

February 2 @ 10:00 am - 1:00 pm

Advanced Research Computing on the Great Lakes Cluster

OVERVIEW This workshop will cover some more advanced topics in computing on the U-M Great Lakes Cluster. Topics to be covered include a review of common parallel programming models and…

February 7 @ 9:30 am - 3:30 pm

Introduction to Stata (Beginner)

Audience: Those who have never used Stata before but wish to learn. By the end of the workshop, participants will be able to: Work with Stata, including using Do-files and…

February 22 @ 2:00 pm - 4:30 pm

Intro to GPU & CUDA Programming

This workshop is an introduction to GPU programing for scientific and engineering applications. The basics of GPU architecture will be presented. Parallel programing strategies will be discussed followed by actual…

Great Lakes Configuration

By | Great Lakes, HPC

Hardware

Computing

Node Type Standard Large Memory GPU SPGPU
Number of Nodes 380 5 24 20
Processors 2x 3.0 GHz Intel Xeon Gold 6154 2x 3.0 GHz Intel Xeon Gold 6154 2x 2.4 GHz Intel Xeon Gold 6148 2x 2.9 GHz Intel Xeon Gold 6226R
Cores per Node 36 36 40 32
RAM 187 GB (180 GB requestable) 1.5 TB (1,503 GB requestable) 187 GB (180 GB requestable) 376 GB (372 GB requestable)
Storage 480 GB SSD + 4 TB HDD 4 TB HDD 4 TB HDD 480 GB SSD + 14 TB NVMe SSD
GPU N/A N/A 20 2x NVIDIA Tesla V100, 4 3x  V100 8x NVIDIA A40

 

Networking

The compute nodes are all interconnected with InfiniBand HDR100 networking, capable of 100 Gb/s throughput. In addition to the InfiniBand networking, there is 25 Gb/s ethernet for the login and transfer nodes and a gigabit Ethernet network that connects the remaining nodes. This is used for node management and NFS file system access.

Storage

The high-speed scratch file system provides 2 petabytes of storage at approximately 80 GB/s performance (compared to 7 GB/s on Flux).

Operation

Computing jobs on Great Lakes are managed completely through Slurm.

Software

There are three layers of software on Great Lakes.  For detailed information, see the software page.

Operating Software

The Great Lakes cluster runs CentOS 7. We update the operating system on Great Lakes 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

Great Lakes supports the Gnu Compiler Collection, the Intel Compilers, and the PGI Compilers for C and Fortran. The Great Lakes cluster’s parallel library is OpenMPI, with fully supported versions 1.10.7 and 3.1.4 with the latter being the default.  Great Lakes 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

Great Lakes supports a wide range of application software. We license common engineering simulation software (e.g. Ansys, Abaqus, VASP) and we compile others for use on Great Lakes (e.g. 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

Great Lakes has 52 NVIDIA Tesla V100 GPUs connected to 20 nodes. 160 NVIDIA A40 GPUs connected to 20 nodes are also available for single-precision work.

GPU Model NVIDIA Tesla V100 NVIDIA A40
Number and Type of GPU one Volta GPU one Ampere GPU
Peak double precision floating point perf. 7 TFLOPS N/A
Peak single precision floating point perf. 14 TFLOPS 37.4 TFLOPS (non-Tensor)

74.8 TFLOPS (Tensor)

Memory bandwidth (ECC off) 900 GB/sec 696 GB/sec
Memory size (GDDR5) 16 GB HBM2 48 GB GDDR5
CUDA cores 5120 10752
RT cores N/A 84
Tensor cores N/A 336

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

Order Service

Billing for the Great Lakes service began on January 6, 2020. Existing, active Flux accounts and logins have been added to the Great Lakes Cluster. Complete this form to get a new Great Lakes cluster login.

If you would like to create a Great Lakes Cluster account or have any questions, contact arc-support@umich.edu with lists of users, admins, and a shortcode. UMRCP accounts are also available to eligible researchers. For more information, please visit our UMRCP page.

Close up of Armis cluster

Armis2 (HIPAA-aligned HPC Cluster)

By | Systems and Services

Part of UMRCP provided by ITSThe Armis2 HPC cluster, in conjunction with Turbo Research Storage, provides a secure, scalable, and distributed computing environment that aligns with HIPAA privacy standards.

 

Armis2 features

  • Administrative nodes running the Slurm resource manager and task scheduler
  • Linux-based 2 and 4 socket server class hardware in a secure data center
  • High-speed Ethernet (1 Gbps) and InfiniBand (40Gbps) network
  • Secure parallel filesystem for temporary data, provided by HIPAA-aligned Turbo Research Storage

U-M Research Computing Package

The University of Michigan Research Computing Package (UMRCP) is an investment into the U-M research community via simple, dependable access to several ITS-provided high-performance computing clusters and data storage resources. CPU credits are allocated on the Armis2 protected data cluster and can be used for standard, larger memory, or GPU resources.

Order Service

Users need to request a user login to access the cluster. All users must have been granted access to an account before a user login can be created.

To set up a paid account on Armis2 we need the following information:

  1.  a shortcode that you are authorized to use
  2. a list of uniqnames of the users who should be able to use the account
  3. a list of uniqnames of the administrators who are authorized to make changes to the account
  4. any limits that you want to set on the account (such as a spending limit or resources usage limits)

If you had a pilot account, you can optionally use the same administrative group, the school or college you are a part of. 

UMRCP accounts are also available to eligible researchers. For more information, please visit our UMRCP page.

Please see the Terms of Use for more information.

Related Links

HPC Rates
Citations

Lighthouse (HPC Cluster for researcher-owned hardware)

By | Systems and Services

The Flux HPC ClusterThe Lighthouse cluster supports researchers with grants that require the purchase of computing hardware. Lighthouse allows researchers to place their own hardware within the ARC HPC Slurm environment. This is a dedicated HPC cluster which provides the same capability as the Flux Operating Environment (FOE) did as a part of Flux.

To use Lighthouse, we suggest researchers:

  1. Coordinate with ARC prior to including hardware in a proposal or purchasing hardware;
  2. Purchase hardware that is compatible with the rest of the Lighthouse installation;
  3. Obtain a subscription to Lighthouse for each compute node.

A subscription to Lighthouse provides access to the ARC HPC infrastructure—data center, staff, networking, storage and basic software—except the compute hardware.

Lighthouse is available for:

  1. Researchers who receive funds from external funding agencies for purchasing equipment.
  2. Researchers who have workflow and/or infrastructure needs that differ significantly from those that can be met by other ARC services (i.e., Great Lakes or Armis2).

The hardware added to Lighthouse is for the exclusive use of the research group that added it and will not be part of the general the Great Lakes cluster allocation pool. (Lighthouse gives research groups the option of operating with access to commercially available software to be determined at the time of installation.)

Budgeting for Lighthouse

The Lighthouse subscription rate and node purchase charges allow for funding from a range of sources: federally funded research projects; general funds; departmental instructional funds; faculty discretionary and research incentive accounts; cost-sharing funds; and faculty start-up or retention package funds. All Lighthouse purchase and subscription costs are charged to a U-M Shortcode; no other payment method (cash, credit cards, etc.) is accepted. The costs are determined by the rate that applies to the purchasing researcher and the number and price of the compute nodes being added. Lighthouse use is billed monthly. The details of the Lighthouse charges will appear on the normal monthly Statement of Activity.

Hardware purchases for Lighthouse are a one-time expense. If made from federal funds, the timing of the purchase relative to the end of the grant period must be consistent with federal regulations. Lighthouse subscription charges are a recurring expense; you can see the Lighthouse rates here. If made from federal funds, subscription charges can not be prepaid to extend beyond the end of the grant period.

Using Lighthouse

Slurm commands will be needed to submit jobs. The Lighthouse User Guide explains Slurm usage on the cluster. For a comparison of Slurm and Torque commands, see our Torque to Slurm migration page.

No PHI or sensitive data may be stored or processed on Lighthouse. If you need access to a cluster appropriate for this data, see Armis2. For more policies and user responsibilities, see the Lighthouse User Guide.

Getting Access

Principal Investigators: Email arc-support@umich.edu before including hardware in a proposal or purchasing hardware. Once a plan is in place, you will work with support to get a subscription to Lighthouse for each node.

All Lighthouse users must be authorized first by their PI or a user authorized to make changes to the account. Requests can be sent to arc-support@umich.edu.

Fill out the Lighthouse user form to get a login. To login, SSH to lighthouse.arc-ts.umich.edu.

For technical support, email arc-support@umich.edu.

Additional Information on Using Lighthouse

Order Service

Principal Investigators: Email arc-support@umich.edu before including hardware in a proposal or purchasing hardware.  Once a plan is in place, you will work with support to get a subscription to Lighthouse for each node.

You also need a Lighthouse user login to access your nodes.

Slurm User Guide for Beta

By | Beta

Go to Beta Overview     To search this user guide, use the Command + F (Mac) or Ctrl + F (Win) keyboard shortcuts.

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 the Beta test cluster.

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 the following from a shared file system; those include your home directory, /scratch, and any directory under /nfs that you can normally use in a job on Flux. Output will be sent to this working directory (jobName-jobID.log). Do not submit jobs from /tmp or any of its subdirectories.

$ sbatch myJob.sh

The batch job script is composed of three main components:

  • The interpreter used to execute the script
  • #SBATCH directives that convey submission options
  • The application(s) to execute along with its input arguments and options

Example:

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

#“#SBATCH” directives that convey submission options:

#SBATCH --job-name=example_job
#SBATCH --mail-type=BEGIN,END
#SBATCH --nodes=1
#SBATCH --ntasks-per-node=1
#SBATCH --mem-per-cpu=1000m 
#SBATCH --time=10:00
#SBATCH --account=test
#SBATCH --partition=standard

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

/bin/hostname
sleep 60

How many nodes and processors you request will depend on the capability of your software and what it can do. There are four common scenarios:

Example: One Node, One Processor

This is the simplest case and is shown in the example above. The majority of software cannot use more than this. Some examples of software for which this would be the right configuration are SAS, Stata, R, many Python programs, most Perl programs.

#!/bin/bash
#SBATCH --job-name JOBNAME
#SBATCH --nodes=1
#SBATCH --cpus-per-task=1
#SBATCH --mem-per-cpu=1g
#SBATCH --time=00:15:00
#SBATCH --account=test
#SBATCH --partition=standard
#SBATCH --mail-type=NONE

srun hostname -s

Example: One Node, Multiple Processors

This is similar to what a modern desktop or laptop is likely to have. Software that can use more than one processor may be described as multicore, multiprocessor, or mulithreaded. Some examples of software that can benefit from this are MATLAB and Stata/MP. You should read the documentation for your software to see if this is one of its capabilities.

#!/bin/bash
#SBATCH --job-name JOBNAME
#SBATCH --nodes=1
#SBATCH --ntasks-per-node=4
#SBATCH --mem-per-cpu=1g
#SBATCH --time=00:15:00
#SBATCH --account=test
#SBATCH --partition=standard
#SBATCH --mail-type=NONE

srun hostname -s

Example: Multiple Nodes, One Process per CPU

This is the classic MPI approach, where multiple machines are requested, one process per processor on each node is started using MPI. This is the way most MPI-enabled software is written to work.

#!/bin/bash
#SBATCH --job-name JOBNAME
#SBATCH --nodes=2
#SBATCH --ntasks-per-node=4
#SBATCH --mem-per-cpu=1g
#SBATCH --time=00:15:00
#SBATCH --account=test
#SBATCH --partition=standard
#SBATCH --mail-type=NONE

srun hostname -s

Example: Multiple Nodes, Multiple CPUs per Process

This is often referred to as the “hybrid mode” MPI approach, where multiple machines are requested and multiple processes are requested. MPI will start a parent process or processes on each node, and those in turn will be able to use more than one processor for threaded calculations.

#!/bin/bash
#SBATCH --job-name JOBNAME
#SBATCH --nodes=2
#SBATCH --ntasks-per-node=4
#SBATCH --cpus-per-task=4
#SBATCH --mem-per-cpu=1g
#SBATCH --time=00:15:00
#SBATCH --account=test
#SBATCH --partition=standard
#SBATCH --mail-type=NONE

srun hostname -s

Common Job Submission Options

Option Slurm Command (#SBATCH) Beta Usage
Job name --job-name=<name> --job-name=betajob1
Account --account=<account> --account=test
Queue --partition=<name> --partition=partitionname

Available partitions: standard, gpu (GPU jobs only), largemem (RAM-intensive jobs only), debug

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

(default is 15 minutes if not specified)

Node count --nodes=<count> --nodes=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> --array=0-15
Standard output file --output=<file path> (path must exist) --output=/home/%u/%x-%j.log
%u = username
%x = job name
%j = job ID
Standard error file --error=<file path> (path must exist) --error=/home/%u/error-%x-%j.log
Combine stdout/stderr to stdout --output=<combined out and err file path> --output=/home/%u/%x-%j.log
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 software license(s) --licenses=<application>@slurmdb:<N> --licenses=stata@slurmdb:1
requests one license for Stata
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,ARRAY_TASKS
--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

Please note that if your job is set to utilize more than one node, make sure your code is MPI enabled in order to run across these nodes and you must use srun rather then mpirun or mpiexec.

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 --account=test --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, 1024MB of memory, and 15 minutes of walltime. 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 --account=test --time=00:15:00 --nodes=2 --ntasks-per-node=4 --mem-per-cpu=1GB --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 and Large Memory 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.
Similarly, jobs can request nodes with large amounts of RAM with --partition=largemem.  The largemem (and debug) partition’s nodes on Beta have the same configuration as standard nodes, so this partition is just for testing.  Great Lakes will have high-RAM nodes on this partition.

Requesting software licenses

Many of the software packages that are licensed for use on ARC clusters are licensed for a limited number of concurrent uses. If you will use one of those packages, then you must request a license or licenses in your submission script. As an example, to request one Stata license, you would use

#SBATCH --licenses=stata@slurmdb:1

The list of software can be found from Beta by using the command

$ scontrol show licenses

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.

To receive mail alerts for each individual array task, --mail-type=ARRAY_TASKS should be added to the Slurm job script. Unless this option is specified, mail notifications on job BEGIN, END and FAIL apply to a job array as a whole rather than generating individual email messages for each task in the job array.

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.

To view TRES (Trackable RESource) utilization by user or account, use the following commands (substitute your values for bolded parts):
Shows TRES usage by all users on account during date range:

sreport cluster UserUtilizationByAccount start=mm/dd/yy end=mm/dd/yy account=test --tres type

Shows TRES usage by specified user(s) on account during date range:

sreport cluster UserUtilizationByAccount start=mm/dd/yy end=mm/dd/yy users=un1,un2 account=test --tres type

Lists users alphabetically along with TRES usage and total during date range:

sreport cluster AccountUtilizationByUser start=mm/dd/yy end=mm/dd/yy tree account=test --tres type

Possible TRES types:
cpu
mem
node
gres/gpu

For more reporting options, see the Slurm sreport documentation.

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

Hardware

Computing

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

Networking

The compute nodes are all interconnected with InfiniBand 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 Flux:

Server Type Network Connection Disk Capacity (raw/usable)
Dell R610 40Gbps InfiniBand 520 TB / 379 TB
Dell R610 40Gbps InfiniBand 530 TB / 386 TB
Dell R610 40Gbps InfiniBand 530 TB / 386 TB
Dell R610 40Gbps InfiniBand 520 TB / 379 TB
Totals 160 Gbps 2100 TB / 1530 TB

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 arcts-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

Fill out the HPC account request form.

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

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February 1 @ 1:00 pm - 4:00 pm

Introduction to Research Computing on the Great Lakes Cluster

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February 2 @ 10:00 am - 1:00 pm

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February 7 @ 9:30 am - 3:30 pm

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Great Lakes

By | Systems and Services

Part of UMRCP provided by ITS

The Great Lakes Slurm cluster is a campus-wide computing cluster that serves the broad needs of researchers across the university. The Great Lakes HPC Cluster replaced Flux, the shared research computing cluster that served over 300 research projects and 2,500 active users.

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

Based on extensive input from faculty and other stakeholders across campus, the Great Lakes HPC Cluster is designed to deliver similar services and capabilities as Flux, including access to GPUs and large-memory nodes and improved support for emerging uses such as machine learning and genomics. The Great Lakes HPC Cluster consists of approximately 13,000 cores.

U-M Research Computing Package

The University of Michigan Research Computing Package (UMRCP) is an investment into the U-M research community via simple, dependable access to several ITS-provided high-performance computing clusters and data storage resources. CPU credits are allocated on the Great Lakes cluster and can be used for standard, larger memory, or GPU resources.

Grant Support

See the Grant Resources page for information regarding grant proposals using the Great Lakes HPC Cluster.

LSA-specific Information

See the LSA funding page for information on funding courses at the College of Literature, Science, and the Arts. LSA researchers who do not have access to any other account may be eligible to use the accounts provided centrally by LSA. The usage policy and restrictions on these accounts is described in detail on the LSA’s public Great Lakes accounts page.

LSA increased their cost-sharing for the rest of the 2021-2022 fiscal year.  Read about the details here.

Questions about access or use of these accounts should be sent to arc-support@umich.edu.

Student Teams

See the Great Lakes Student Teams and Organizations page if your team requires HPC resources.

Use Within Courses

Learn how you can use Great Lakes in your course.

To establish a Slurm account for a class please contact us at
arc-support@umich.edu with the following information:

  • Students to be put on the account
  • List of individuals to administer the account
  • Any limits to be placed on the either the users or the account as a whole
  • The unit abbreviation and course and section numbers for the course you are leading (i.e., eecs498 section 400)

Please note: all students will need to have a user login to use the account and can request one online.

For technical support, email arc-support@umich.edu.

Order Service

Complete this form to get a new Great Lakes cluster login.

If you would like to create a Great Lakes Cluster account or have any questions, contact arc-support@umich.edu with lists of users, admins, and a Shortcode. UMRCP accounts are also available to eligible researchers. For more information, please visit our UMRCP page.

Related Links

HPC Rates
Citations

Beta

By | Beta

Beta is the Linux-based high-performance computing (HPC) test cluster available to all researchers at the University of Michigan. Beta will provide a testing environment for the transition to a new job scheduler, Slurm. Slurm will be used on the Great Lakes cluster; the Armis HIPAA-aligned cluster; and a new resource called “Lighthouse” which will succeed the Flux Operating Environment in early 2019.

Currently, Flux and Armis use the Torque (PBS) resource manager and the Moab scheduling system; when completed, the Great Lakes cluster and Lighthouse will use the Slurm scheduler and resource manager, which will enhance the performance and reliability of the new resources. Armis will transition from Torque to Slurm in early 2019.

The Beta test cluster is available to all Flux users, who can login via ssh at ‘beta.arc-ts.umich.edu’. Beta has its own /home directory, so users will need to create or transfer any files they need, via scp/sftp or Globus.

Slurm commands will be needed to submit jobs. For a comparison of Slurm and Torque commands, see our Torque to Slurm migration page.

Beta consists of 20 compute nodes and eight K20x GPUs on one node for testing GPU workloads. Each of the standard nodes have 16 AVX cores (Intel(R) Xeon(R) CPU E5-2670 0 @ 2.60GHz) and 62GB of RAM, interconnected with 40Gb/s InfiniBand networking.

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.

Getting Access

All HPC users with Flux accounts should have access. If you complete an HPC User form to get an account. To log in, ssh to beta.arc-ts.umich.edu.

For technical support, email arcts-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. No sensitive data of any type should be used on Beta.

To Request

Fill out the HPC account request form.

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

Related Events

February 1 @ 1:00 pm - 4:00 pm

Introduction to Research Computing on the Great Lakes Cluster

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February 2 @ 10:00 am - 1:00 pm

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February 7 @ 9:30 am - 3:30 pm

Introduction to Stata (Beginner)

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February 22 @ 2:00 pm - 4:30 pm

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Introduction to the Linux Command Line

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Stata’s `margins` command

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Yottabyte Research Cloud powered by Verge.io

By | Systems and Services

Yottabyte Research Cloud (YBRC) is no longer available. The YBRC service has been migrated to the Secure Enclave Service

YBRC was a partnership between Information and Technology Services (ITS) and Yottabyte/Verge.io from 2016 to 2022 that provided 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. 

Why migrate? 

  • Researcher workloads have evolved and need consistently tuned performance across the subsystems of the enclave, which became more difficult as the YBRC hardware aged
  • The initial funding for YBRC was provided, in part, by a grant that was coming to an end from the company behind the Yottabyte software
  • ITS currently offers a virtual server environment called MiServer for non-sensitive data

How can we help you? 

Contact ARC at arc-support@umich.edu for assistance.

Yottabyte Research Cloud (YBRC) is no longer available. The YBRC service has been migrated to the Secure Enclave Service