Oscar
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      • From Non-compliant Networks (2-FA)
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  • Managing files
    • Oscar's Filesystem
    • Transferring Files to and from Oscar
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    • Resolving quota issues
      • Understanding Disk Quotas
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  • Submitting jobs
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  • GPU Computing
    • GPUs on Oscar
      • Grace Hopper GH200 GPUs
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    • Submitting GPU Jobs
    • Intro to CUDA
    • Compiling CUDA
    • Installing Frameworks (PyTorch, TensorFlow, Jax)
      • Installing JAX
      • Installing TensorFlow
    • Mixing MPI and CUDA
  • Large Memory Computing
    • Large Memory Nodes on Oscar
  • Software
    • Software on Oscar
    • Using Modules
    • Migration of MPI Apps to Slurm 22.05.7
    • Python on Oscar
    • Python in batch jobs
    • Installing Python Packages
    • Installing R Packages
    • Using CCMake
    • Intro to Parallel Programming
    • Anaconda
    • Conda and Mamba
    • DMTCP
    • Screen
    • VASP
    • Gaussian
    • IDL
    • MPI4PY
  • Jupyter Notebooks/Labs
    • Jupyter Notebooks on Oscar
    • Jupyter Labs on Oscar
    • Tunneling into Jupyter with Windows
  • Debugging
    • Arm Forge
      • Configuring Remote Launch
      • Setting Job Submission Settings
  • MATLAB
    • Matlab GUI
    • Matlab Batch Jobs
    • Improving Performance and Memory Management
    • Parallel Matlab
  • Visualization 🕶
    • ParaView Remote Rendering
  • Singularity Containers
    • Intro to Apptainer
    • Building Images
    • Running Images
    • Accessing Oscar Filesystem
      • Example Container (TensorFlow)
    • Singularity Tips and Tricks
  • Installing Software Packages Locally
    • Installing your own version of Quantum Espresso
    • Installing your own version of Qmcpack
  • dbGaP
    • dbGaP Architecture
    • dbGaP Data Transfers
    • dbGaP Job Submission
  • RHEL9 Migration
    • RHEL-9 Migration
    • LMOD - New Module System
    • Module Changes
    • Testing Jupyter Notebooks on RHEL9 mini-cluster
  • Large Language Models
    • Ollama
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  1. RHEL9 Migration

RHEL-9 Migration

Higher Throughput, More Secure, and Greener Computing!"

PreviousdbGaP Job SubmissionNextLMOD - New Module System

Last updated 1 year ago

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  1. What is the reason for this maintenance?

Oscar's current operating system, RHEL-7, and its maintenance support phase will come to an end in June 2024. So Oscar is being upgraded to latest RedHat Enterprise Linux RHEL-9.2

Due to the new kernel and glibc majority of applications will break.

  1. We are also introducing a bunch of new features:

  • Upgraded OS - The OS has been upgraded with a newer kernel and improved security patches

  • Power Saving Mode: In the batch partition, idle nodes now enter a power-saving mode, consuming only about 40W. They seamlessly transition to high-performance mode just before job execution begins

  • GPU Direct Storage: GDS enables a direct data path for direct memory access (DMA) transfers between GPU memory and storage, which avoids a bounce buffer through the CPU. This direct path increases system bandwidth and decreases the latency and utilization load on the CPU

  • SLURM Upgrade: We have tuned the scheduler to provide much higher throughput. Now supports json and yaml formatting for all slurm commands

  • SPACK & LMOD - Newer industry standard for installing and managing applications on Oscar. We now support multple shells bash,zsh & fish etc

  • Increased-core core count for GPU accounts:

Account
Partition
Current core-limit
New core-limit

Exploratory

gpu

4-cores

12-cores

Standard GPU Priority

gpu

16-cores

24-cores

Standard GPU Priority+

gpu

32-cores

48-cores

High-End GPU Priority

gpu-he

16-cores

24-cores

  1. What are exact version changes?

Component
Current Version
New Version

Operating System

RHEL-7.9

RHEL-9.2

Kernel

3.10.0-1160.76.1

5.14.0-284.11.1

GLIBC

2.17-326

2.34-60

SLURM

22.05.7

23.02.6

Nvidia Driver

535.54.03

535.113.01

Package Manager

PyModules

SPACK

  1. How to access the new cluster?

We will provide detailed instructions in coming weeks. Thank you for your patience.

Idle nodes enter power saving mode automatically
Unified Storage across all OIT data platforms
GPUDirect Storage - Lower laency & Higher Bandwidth for IO