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=== Sapelo2 === | === Sapelo2 === | ||
Sapelo2 is a Linux cluster that runs a 64-bit | Sapelo2 is a Linux cluster that runs a 64-bit Rocky 8.8 operating system and it is managed using Warewulf. Several virtual login nodes are available, with Intel Xeon Gold 6230 processors, 32GB of RAM, and 16 cores per node. The queueing system on Sapelo2 is Slurm. | ||
Internodal communication among the compute nodes and between these nodes and the storage systems serving the home directories and the scratch directories is provided by an EDR Infiniband network (100Gbps). | |||
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'''Regular nodes''' | '''Regular nodes''' | ||
* | * 14 compute nodes with AMD EPYC (Genoa 4th gen) processors (128 cores and 745GB of RAM per node) | ||
* | * 120 compute nodes with AMD EPYC (Milan 3rd gen) processors (128 cores and 512GB of RAM per node) | ||
* | * 4 compute nodes with AMD EPYC (Milan 3rd gen) processors (64 cores and 256GB of RAM per node) | ||
* 2 compute nodes with AMD EPYC (Milan 3rd gen) processors (64 cores and 128GB of RAM per node) | |||
* 123 compute nodes with AMD EPYC (Rome 2nd gen) processors (64 cores and 128GB of RAM per node) | |||
* 50 compute nodes with AMD EPYC (Naples 1st gen) processors (32 cores and 128GB of RAM per node) | |||
* 42 compute nodes with Intel Xeon Skylake processors (32 cores and 192GB of RAM per node) | * 42 compute nodes with Intel Xeon Skylake processors (32 cores and 192GB of RAM per node) | ||
* | |||
* | |||
'''High memory nodes (3TB/node)''' | |||
* 3 compute nodes with AMD EPYC (Genoa 4th gen) processors (48 cores and 3TB of RAM per node) | |||
'''High memory nodes (2TB/node)''' | |||
* 2 compute nodes with AMD EPYC (Rome 2nd gen) processors (32 cores and 2TB of RAM per node) | |||
'''High memory nodes (1TB/node)''' | '''High memory nodes (1TB/node)''' | ||
* | * 2 compute nodes with AMD EPYC (Milan 3rd gen) processors (128 cores and 1TB of RAM per node) | ||
* | * 12 compute nodes with AMD EPYC (Milan 3rd gen) processors (32 cores and 1TB of RAM per node) | ||
* | * 2 compute nodes with AMD EPYC (Naples 1st gen) processors (64 cores and 1TB of RAM per node) | ||
* 1 compute nodes with Intel Xeon Broadwell processors (28 cores and 1TB of RAM per node) | |||
'''High memory nodes (512GB/node)''' | '''High memory nodes (512GB/node)''' | ||
* | * 18 compute nodes with AMD EPYC (Naples 1st gen) processors (32 cores and 512GB of RAM per node) | ||
<!-- * 1 compute node with Intel Xeon Nehalem processors (32 cores and 512GB of RAM per node) --> | <!-- * 1 compute node with Intel Xeon Nehalem processors (32 cores and 512GB of RAM per node) --> | ||
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'''GPU nodes''' | '''GPU nodes''' | ||
* | * 12 compute nodes with Intel Xeon SapphireRapids processors (64 cores and 1TB of RAM) and 4x NVIDIA H100 GPU cards. | ||
* | * 12 compute nodes with AMD EPYC (Genoa 4th gen) processors (128 cores and 745GB of RAM) and 4x NVIDIA L4 GPU cards. | ||
* | * 14 compute nodes with AMD EPYC (Milan 3rd gen) processors (64 cores and 1TB of RAM) and 4x NVIDIA A100 GPU cards. | ||
* 2 compute nodes with Intel Xeon Skylake processors (32 cores and 187GB of RAM) and 1x NVIDIA P100 GPU card per node | |||
<!-- * 2 compute nodes with Intel Xeon processors (16 cores and 128GB of RAM) and 8x NVIDIA K40m GPU cards per node --> | |||
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* Various configurations | * Various configurations | ||
<!-- | <!-- | ||
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====[[Disk Storage]]==== | ====[[Disk Storage]]==== | ||
====[[Software | ====[[Software on Sapelo2]]==== | ||
====[[Available Toolchains and Toolchain Compatibility]]==== | |||
====[[Code Compilation on Sapelo2]]==== | ====[[Code Compilation on Sapelo2]]==== | ||
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====[[Monitoring Jobs on Sapelo2]]==== | ====[[Monitoring Jobs on Sapelo2]]==== | ||
====[[Migrating from Torque to Slurm]]==== | |||
'''Training material''' | |||
To help users familiarize with Slurm and the test cluster environment, we have prepared some training videos that are available from the GACRC's Kaltura channel at https://kaltura.uga.edu/channel/GACRC/176125031 (login with MyID and password is required). Training sessions and slides are available at https://wiki.gacrc.uga.edu/wiki/Training | |||
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[[#top|Back to Top]] | [[#top|Back to Top]] | ||
<!-- | |||
=== Slurm | === Slurm Test Cluster (Sap2test) === | ||
GACRC is planning to switch the queueing system on Sapelo2 from Torque/Moab to Slurm later this year. At the same time, we will update the cluster OS, from CentOS 7.5 to CentOS 7.8, the compiler toolchains, and the application software packages. Older versions of the applications, currently on Sapelo2, will only be installed in the updated cluster if necessary, upon user request. | GACRC is planning to switch the queueing system on Sapelo2 from Torque/Moab to Slurm later this year. At the same time, we will update the cluster OS, from CentOS 7.5 to CentOS 7.8, the compiler toolchains, and the application software packages. Older versions of the applications, currently on Sapelo2, will only be installed in the updated cluster if necessary, upon user request. | ||
In preparation for implementing this major change in the Fall, we are deploying a Slurm development (dev) cluster | In preparation for implementing this major change in the Fall, we are deploying a Slurm development (dev) cluster, that will be available ahead of time. The goal is to give users an environment to modify their workflow scripts to use Slurm and possibly to use newer versions of the applications, prior to the major change. All job submission scripts will need to be changed, because Slurm uses different syntax from Torque/Moab, as summarized in [[Migrating from Torque to Slurm]]. We strongly encourage everyone to fully test their ported workflow scripts on the Slurm dev cluster, to ensure a smooth transition to the new system later in the year. | ||
This dev cluster is intended to allow users to port their workflow scripts to Slurm, and it is not a platform for users to run jobs extensively. This dev cluster currently has the following resources: | This dev cluster is intended to allow users to port their workflow scripts to Slurm, and it is not a platform for users to run jobs extensively. This dev cluster currently has the following resources: | ||
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'''Storage''' | '''Storage''' | ||
The user's home directory (/home), scratch directory (/scratch), and each group's work directory (/work) on | The user's home directory (/home), scratch directory (/scratch), and each group's work directory (/work) on the Slurm test cluster are the same file systems as on Sapelo2. So there is no need to transfer data between Sapelo2 and Slurm test cluster. If you have Sapelo2 specific settings in your dotfiles (for example in .bashrc or in software specific configuration files), those might need to get changed when you work on Sap2test. The environment variable GACRC_CLUSTER stores the test cluster name, and can be used to set up a cluster specific dotfile to use on the test cluster. | ||
However, Sapelo2's /usr/local file system and therefore the applications installed on Sapelo2 are not available on the Slurm test cluster. | |||
'''Training material''' | |||
To help users familiarize with Slurm and the test cluster environment, we have prepared some training videos that are available from the GACRC's Kaltura channel at https://kaltura.uga.edu/channel/GACRC/176125031 (login with MyID and password is required). Training sessions and slides are available at https://wiki.gacrc.uga.edu/wiki/Training | |||
'''Getting Help''' | |||
If you run into any issues on the test cluster or have any questions or suggestions, please let me know via the online form below, as it will reach all the GACRC staff members: | |||
[https://uga.teamdynamix.com/TDClient/2060/Portal/Requests/ServiceDet?ID=41600 Support for Slurm test cluster] | |||
====[[Connecting to the Slurm test cluster]]==== | |||
====[[ | ====[[Sapelo2 and Sap2test comparison]]==== | ||
====[[Software on | ====[[Software on sap2test | Software Installed on the Slurm test cluster]]==== | ||
====[[Code Compilation on | ====[[Code Compilation on Sap2test]]==== | ||
====[[ | ====[[Available Toolchains and Toolchain Compatibility]]==== | ||
====[[ | ====[[Running Jobs on Sap2test | Running Jobs on the Slurm test cluster]]==== | ||
====[[Sample batch job submission scripts on | ====[[Monitoring Jobs on Sap2test | Monitoring Jobs on Slurm test cluster]]==== | ||
====[[Sample batch job submission scripts on the Slurm test cluster]]==== | |||
====[[Migrating from Torque to Slurm]]==== | ====[[Migrating from Torque to Slurm]]==== | ||
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---- | ---- | ||
[[#top|Back to Top]] | [[#top|Back to Top]] | ||
--> | |||
=== Teaching cluster === | === Teaching cluster === | ||
The teaching cluster is a Linux cluster that runs a 64-bit Linux, with | The teaching cluster is a Linux cluster that runs a 64-bit Linux, with Rocky 8.8. The login node is a VM that has 4 cores (Intel Xeon Gold 6230 processor) and 16GB of RAM. An EDR Infiniband network (100Gbps) provides internodal communication among compute nodes, and between the compute nodes and the storage systems serving the home directories and the work directories. | ||
The cluster is currently comprised of the following resources: | The cluster is currently comprised of the following resources: | ||
* | '''Regular nodes:''' | ||
* 2 compute nodes with | |||
* | * 10 compute nodes with AMD EPYC (Naples 1st gen) processors (32 cores and 128GB or RAM per node) | ||
* | |||
* | '''High-memory nodes:''' | ||
* | |||
* 2 compute nodes with AMD EPYC (Naples 1st gen) processors (64 cores and 1TB of RAM per node) | |||
'''GPU nodes:''' | |||
* 1 compute node with Intel Skylake processors (32 cores, 192GB RAM per node) and a P100 GPU card | |||
<!-- | |||
*30 compute nodes with Intel Xeon X5650 2.67GHz processors (12 cores and 48GB of RAM per node) | |||
* 2 compute nodes with Intel Xeon L7555 1.87GHz processors (32 cores and 512GB of RAM per node) | |||
* 4 NVIDIA Tesla (Kepler) K20Xm GPU cards. These cards are installed on one host that has dual 6-core Intel Xeon CPUs and 48GB of RAM | |||
--> | |||
The queueing system on the teaching cluster is Slurm. | The queueing system on the teaching cluster is Slurm. | ||
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====[[Connecting#Connecting_to_the_teaching_cluster |Connecting to the teaching cluster]]==== | ====[[Connecting#Connecting_to_the_teaching_cluster |Connecting to the teaching cluster]]==== | ||
====[[Transferring Files]]==== | |||
<!-- | |||
====[[Disk Storage]]==== | ====[[Disk Storage]]==== | ||
--> | |||
====Software Installed on the teaching cluster==== | ====Software Installed on the teaching cluster==== | ||
The | The teaching cluster has access to the same software stack installed on Sapelo2. | ||
====[[Code Compilation on the teaching cluster]]==== | ====[[Code Compilation on the teaching cluster]]==== | ||
Latest revision as of 09:26, 12 September 2024
Sapelo2
Sapelo2 is a Linux cluster that runs a 64-bit Rocky 8.8 operating system and it is managed using Warewulf. Several virtual login nodes are available, with Intel Xeon Gold 6230 processors, 32GB of RAM, and 16 cores per node. The queueing system on Sapelo2 is Slurm.
Internodal communication among the compute nodes and between these nodes and the storage systems serving the home directories and the scratch directories is provided by an EDR Infiniband network (100Gbps).
The cluster is currently comprised of the following resources:
Regular nodes
- 14 compute nodes with AMD EPYC (Genoa 4th gen) processors (128 cores and 745GB of RAM per node)
- 120 compute nodes with AMD EPYC (Milan 3rd gen) processors (128 cores and 512GB of RAM per node)
- 4 compute nodes with AMD EPYC (Milan 3rd gen) processors (64 cores and 256GB of RAM per node)
- 2 compute nodes with AMD EPYC (Milan 3rd gen) processors (64 cores and 128GB of RAM per node)
- 123 compute nodes with AMD EPYC (Rome 2nd gen) processors (64 cores and 128GB of RAM per node)
- 50 compute nodes with AMD EPYC (Naples 1st gen) processors (32 cores and 128GB of RAM per node)
- 42 compute nodes with Intel Xeon Skylake processors (32 cores and 192GB of RAM per node)
High memory nodes (3TB/node)
- 3 compute nodes with AMD EPYC (Genoa 4th gen) processors (48 cores and 3TB of RAM per node)
High memory nodes (2TB/node)
- 2 compute nodes with AMD EPYC (Rome 2nd gen) processors (32 cores and 2TB of RAM per node)
High memory nodes (1TB/node)
- 2 compute nodes with AMD EPYC (Milan 3rd gen) processors (128 cores and 1TB of RAM per node)
- 12 compute nodes with AMD EPYC (Milan 3rd gen) processors (32 cores and 1TB of RAM per node)
- 2 compute nodes with AMD EPYC (Naples 1st gen) processors (64 cores and 1TB of RAM per node)
- 1 compute nodes with Intel Xeon Broadwell processors (28 cores and 1TB of RAM per node)
High memory nodes (512GB/node)
- 18 compute nodes with AMD EPYC (Naples 1st gen) processors (32 cores and 512GB of RAM per node)
GPU nodes
- 12 compute nodes with Intel Xeon SapphireRapids processors (64 cores and 1TB of RAM) and 4x NVIDIA H100 GPU cards.
- 12 compute nodes with AMD EPYC (Genoa 4th gen) processors (128 cores and 745GB of RAM) and 4x NVIDIA L4 GPU cards.
- 14 compute nodes with AMD EPYC (Milan 3rd gen) processors (64 cores and 1TB of RAM) and 4x NVIDIA A100 GPU cards.
- 2 compute nodes with Intel Xeon Skylake processors (32 cores and 187GB of RAM) and 1x NVIDIA P100 GPU card per node
Buy-in nodes
- Various configurations
Connecting to Sapelo2
Transferring Files
Disk Storage
Software on Sapelo2
Available Toolchains and Toolchain Compatibility
Code Compilation on Sapelo2
Running Jobs on Sapelo2
Monitoring Jobs on Sapelo2
Migrating from Torque to Slurm
Training material
To help users familiarize with Slurm and the test cluster environment, we have prepared some training videos that are available from the GACRC's Kaltura channel at https://kaltura.uga.edu/channel/GACRC/176125031 (login with MyID and password is required). Training sessions and slides are available at https://wiki.gacrc.uga.edu/wiki/Training
Teaching cluster
The teaching cluster is a Linux cluster that runs a 64-bit Linux, with Rocky 8.8. The login node is a VM that has 4 cores (Intel Xeon Gold 6230 processor) and 16GB of RAM. An EDR Infiniband network (100Gbps) provides internodal communication among compute nodes, and between the compute nodes and the storage systems serving the home directories and the work directories.
The cluster is currently comprised of the following resources:
Regular nodes:
- 10 compute nodes with AMD EPYC (Naples 1st gen) processors (32 cores and 128GB or RAM per node)
High-memory nodes:
- 2 compute nodes with AMD EPYC (Naples 1st gen) processors (64 cores and 1TB of RAM per node)
GPU nodes:
- 1 compute node with Intel Skylake processors (32 cores, 192GB RAM per node) and a P100 GPU card
The queueing system on the teaching cluster is Slurm.
Connecting to the teaching cluster
Transferring Files
Software Installed on the teaching cluster
The teaching cluster has access to the same software stack installed on Sapelo2.