Systems
Sapelo2
Sapelo2 is a Linux cluster that runs a 64-bit CentOS 7.9 operating system and it is managed using xCAT and Puppet. 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.
For a subset of compute nodes, internodal communication among them and between these nodes and the storage systems serving the home directories and the scratch directories is provided by a QDR Infiniband network(40Gbps). For another (larger) subset of compute nodes, these communications are provided by an EDR Infiniband network (100Gbps).
The cluster is currently comprised of the following resources:
Regular nodes
- 24 compute nodes with AMD EPYC (Milan) processors (128 cores and 512GB of RAM per node)
- 4 compute nodes with AMD EPYC (Milan) processors (64 cores and 256GB of RAM per node)
- 2 compute nodes with AMD EPYC (Milan) processors (64 cores and 128GB of RAM per node)
- 117 compute nodes with AMD EPYC (Rome) processors (64 cores and 128GB of RAM per node)
- 64 compute nodes with AMD EPYC (Naples)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)
- 34 compute nodes with Intel Xeon Broadwell processors (28 cores and 64GB of RAM per node)
- 3 compute nodes with AMD Opteron processors (48 cores and 256GB of RAM per node)
- 87 compute nodes with AMD Opteron processors (48 cores and 128GB of RAM per node)
High memory nodes (2TB/node)
- 2 compute nodes with AMD EPYC processors (32 cores and 2TB of RAM per node)
High memory nodes (1TB/node)
- 4 compute nodes with AMD EPYC processors (64 cores and 1TB of RAM per node)
- 4 compute nodes with Intel Xeon Broadwell processors (28 cores and 1TB of RAM per node)
- 1 compute node with AMD Opteron processors (48 cores and 1TB of RAM per node)
High memory nodes (512GB/node)
- 18 compute nodes with AMD EPYC (Naples)processors (32 cores and 512GB of RAM per node)
- 2 compute nodes with AMD Opteron processors (48 cores and 512GB of RAM per node)
GPU nodes
- 1 compute node with AMD EPYC (Milan) processors (64 coress and 1TB of RAM) and 4 NVIDIA A100 GPU cards.
- 4 compute nodes with Intel Xeon Skylake processors (32 cores and 187GB of RAM) and 1 NVIDIA P100 GPU card per node
- 2 compute nodes with Intel Xeon processors (16 cores and 128GB of RAM) and 8 NVIDIA K40m GPU cards per node
- 2 compute node with Intel Xeon processors (12 cores and 96GB of RAM) and 7 NVIDIA K20Xm GPU cards 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 Centos 7.8. The login node is a VM that has 4 cores (Intel Xeon Gold 6230 processor) and 16GB of RAM. An Ethernet network (1Gbps) 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:
- 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.
Connecting to the teaching cluster
Transferring Files
Disk Storage
Software Installed on the teaching cluster
The list of installed application is available at Software page.