Grant Proposal: Difference between revisions

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*** Cores: 64  
*** Cores: 64  
*** Ram: 512GB
*** Ram: 512GB
** Network: 10GB low latency SFP+ back-end network interconnecting each node
** Network: 10Gbit low latency SFP+ interconnect
** Storage: 91 terabytes of dedicated disk storage.
** Storage: 91 terabytes of dedicated disk storage.


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This GPU Virtual Machine cluster is dedicated to GPU computation research.  This is an excellent resource for prototyping small scale models in a virtual GPU environment before scaling up to larger hardware.
This GPU Virtual Machine cluster is dedicated to GPU computation research.  This is an excellent resource for prototyping small scale models in a virtual GPU environment before scaling up to larger hardware.
* Hardware:
* Hardware:
** Nodes: 10
** Nodes: 15
*** Cores: At least 40
*** Cores: At least 40
*** Ram: At least 192GB
*** Ram: At least 192GB
** GPU's: Multiple Nvidia P40 and T4's
** GPUs: Multiple NVidia P40, T4, A2 GPUs
** Network: 10GB low latency SFP+ back-end network interconnecting each node
** Network: 10Gbit interconnect
** Storage: Dedicated CEPH shared file system
** Storage: 174 TB of dedicated CEPH storage


== Systems Cluster ==
== Systems Cluster ==
Line 37: Line 37:
*** Cores: 48 (Intel)
*** Cores: 48 (Intel)
*** RAM: 256GB
*** RAM: 256GB
** Network: 10GB interconnected Nodes and Storage
** Network: 10Gbit interconnect
** Storage: 73TB
** Storage: 73TB
== Kubernetes Clusters ==
=== Discovery ===
* Hardware:
** Nodes: 24 (Compute)
*** Cores: 1100
*** RAM: 3.2TB
** Storage: 34TB
=== Endeavour ===
* Hardware:
** Nodes: 12 (Compute)
*** Cores: 728
*** GPU's: 12
*** RAM: 5.2TB
** Storage: 42TB


== CBB: Mnemosyne ==
== CBB: Mnemosyne ==
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*** Cores: 64 (Intel)
*** Cores: 64 (Intel)
*** RAM: 7686GB
*** RAM: 7686GB
** Network: 10GB interconnected to Storage
** Network: 10Gbit interconnected to Storage
** Storage: 30TB
** Storage: 30TB



Latest revision as of 14:30, 24 January 2024

Introduction

This page has been created as a quick reference which gathers all the available resources in a single location for the purposes of writing Grant Proposals. If you see an outdated resource or a missing resource please contact techstaff@cs.vt.edu.

Resources Available in the Department

RLogin

  • Hardware:
    • Nodes: 32 Compute 5 GPU
      • Cores: 32 Intel
      • RAM: 384GB
      • Storage: 1TB
    • Network: 10Gbit interconnect

CSRVM

To facilitate wide ranging research activities, some of the major services provided by the department include a Computer Science Research Virtual Machine cluster (CSRVM) which has the flexibility to create multi-core large scale VM's in a matter of minutes.

  • Hardware:
    • Nodes: 9
      • Cores: 64
      • Ram: 512GB
    • Network: 10Gbit low latency SFP+ interconnect
    • Storage: 91 terabytes of dedicated disk storage.

CSRGPU

This GPU Virtual Machine cluster is dedicated to GPU computation research. This is an excellent resource for prototyping small scale models in a virtual GPU environment before scaling up to larger hardware.

  • Hardware:
    • Nodes: 15
      • Cores: At least 40
      • Ram: At least 192GB
    • GPUs: Multiple NVidia P40, T4, A2 GPUs
    • Network: 10Gbit interconnect
    • Storage: 174 TB of dedicated CEPH storage

Systems Cluster

In addition, Computer Science has a ten (10) node homogeneous cluster, dedicated to systems research.

  • Hardware:
    • Nodes: 10 (Homogeneous)
      • Cores: 48 (Intel)
      • RAM: 256GB
    • Network: 10Gbit interconnect
    • Storage: 73TB

Kubernetes Clusters

Discovery

  • Hardware:
    • Nodes: 24 (Compute)
      • Cores: 1100
      • RAM: 3.2TB
    • Storage: 34TB

Endeavour

  • Hardware:
    • Nodes: 12 (Compute)
      • Cores: 728
      • GPU's: 12
      • RAM: 5.2TB
    • Storage: 42TB

CBB: Mnemosyne

High memory computation system, dedicated to computational bioinformatics research.

  • Hardware:
    • Nodes: 1
      • Cores: 64 (Intel)
      • RAM: 7686GB
    • Network: 10Gbit interconnected to Storage
    • Storage: 30TB

Secure Server Room & Graduate Area

The Computer Science department has a secure, modern, climate-controlled server room. The room is equipped with numerous sensors, controls, and backup systems to ensure all servers are continuously operating at peak performance. In addition, there is a separate secure, climate-controlled workspace for graduate students to work directly with servers and equipment, if appropriate.

Resources Available from the University

Advanced Research Computing - ARC