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Note

Slurm is currently being tested and is scheduled for deployment on the SLAC Scientific Shared Data Facility. We welcome any suggestions and issues to be reported to unix-admin@slac.stanford.edu. Note that whilst we strive to keep the information on these pages up-to-date, there may be inconsistencies and/or incorrect information contained within.

Note

By default, all users when they first use slurm will have access to the shared Account on the shared Partition with scavenger QoS.

If you belong to a group that has contributed hardware into the SDF, you will be eligible to use different Accounts and Partitions:

You will need to request access to slurm whilst we work out more automated ways of adding users to the system.
  • We are testing the ability for your group/team Slurm Administrator to have the ability to add users to their Accounts (delegated administration). If you wish to represent your group/team to do this, please contact us!
  • We will need to know which slurm Account to 'bill' you against (don't worry, there will be no $ charge for usage, it's purely for accounting and reporting). This Account will most likely be your immediate group/team that you work with. Please send your unix username and your group/team name to unix-admin@slac.stanford.edu
.We are also testing the ability for your group/team Slurm Administrator to have the ability to add users to their Accounts (delegated administration) If you wish to represent your group/team to do this, please contact us!

 

Why should I use Batch?

Whilst your desktop computer and or laptop computer has a fast processor and quick local access to data stored on its hard disk/ssd; you may want to run either very big and/or very large compute tasks that may require a lot of CPUs, GPUs, memory, or a lot of data. Our compute servers that are part of the Batch system allows your to do this. Our servers typically also have very fast access to centralised storage, have (some) common software already preinstalled, and will enable you to run these long tasks without impacting your local desktop/laptop resources.

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