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Ascend, Cardinal, Pitzer
Introduction
osc-seff is a command developed at OSC for use on OSC's systems and provides a the CPU resource data of the seff command with the GPU resource data of gpu-seff.
Ascend, Cardinal, Pitzer
Introduction
gpu-seff is a command developed at OSC for use on OSC's systems and is similar providing GPU resource data, similar to the CPU resource data reported by the seff command.
Ascend
The following are technical specifications for Quad GPU nodes.
- Number of Nodes
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24 nodes
- Number of CPU Sockets
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48 (2 sockets/node)
- Number of CPU Cores
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2,304 (96 cores/node)
- Cores Per Node
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96 cores/node (88 usable cores/node)
- Internal Storage
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12.8 TB NVMe internal storage
Ascend
We use Slurm syntax for all the discussions on this page. Please check how to prepare slurm job script if your script is prepared in PBS syntax.
Memory limit
Ascend
Compilers
C, C++ and Fortran are supported on the Ascend cluster. Intel, oneAPI, GNU Compiler Collectio (GCC) and AOCC are available. The Intel development tool chain is loaded by default. Compiler commands and recommended options for serial programs are listed in the table below. See also our compilation guide.
Ascend
The Next Gen Ascend (hereafter referred to as “Ascend”) cluster is now running on Red Hat Enterprise Linux (RHEL) 9, introducing several software-related changes compared to the RHEL 7/8 environment used on the Pitzer and original Ascend cluster. These updates provide access to modern tools and libraries but may also require adjustments to your workflows.
Ascend
- These are the public key fingerprints for Ascend:
ascend: ssh_host_rsa_key.pub = 2f:ad:ee:99:5a:f4:7f:0d:58:8f:d1:70:9d:e4:f4:16
ascend: ssh_host_ed25519_key.pub = 6b:0e:f1:fb:10:da:8c:0b:36:12:04:57:2b:2c:2b:4d
ascend: ssh_host_ecdsa_key.pub = f4:6f:b5:d2:fa:96:02:73:9a:40:5e:cf:ad:6d:19:e5
Ascend, Cardinal, Pitzer
AlphaFold 3 developed by DeepMind and Isomorphic Labs, is an advanced artificial intelligence system that predicts the 3D structures of proteins and their interactions with other molecules, including DNA, RNA, ligands, and ions.
Owens
After eight years of service, the Owens high performance computing (HPC) cluster will be decommissioned over the next two months. Clients currently using Owens for research and classroom instruction must migrate jobs to other OSC clusters during this time.
Overview
Estimating GPU memory (VRAM) usage for training or running inference with large deep learning models is critical to both 1. requesting the appropriate resources for running your computation and 2. optimizing your job once it is setup. Out-of-memory (OOM) errors can be avoided by requesting appropriate resources and by better understanding memory usage during the job using memory profiling tools described here.
