Grant Proposal: Difference between revisions
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*** Cores: 64 | *** Cores: 64 | ||
*** Ram: 512GB | *** Ram: 512GB | ||
** Network: | ** 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: | ** Nodes: 15 | ||
*** Cores: At least 40 | *** Cores: At least 40 | ||
*** Ram: At least 192GB | *** Ram: At least 192GB | ||
** | ** GPUs: Multiple NVidia P40, T4, A2 GPUs | ||
** Network: | ** Network: 10Gbit interconnect | ||
** Storage: | ** Storage: 174 TB of dedicated CEPH storage | ||
== Systems Cluster == | == Systems Cluster == | ||
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*** Cores: 48 (Intel) | *** Cores: 48 (Intel) | ||
*** RAM: 256GB | *** RAM: 256GB | ||
** Network: | ** Network: 10Gbit interconnect | ||
** Storage: 73TB | ** Storage: 73TB | ||
== Kubernetes Clusters == | == Kubernetes Clusters == | ||
=== Discovery === | === Discovery === | ||
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*** Cores: 64 (Intel) | *** Cores: 64 (Intel) | ||
*** RAM: 7686GB | *** RAM: 7686GB | ||
** Network: | ** 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 If you see an outdated resource or a missing resource please contact techstaff@cs.vt.edu.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
- Nodes: 32 Compute 5 GPU
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.
- Nodes: 9
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
- Nodes: 15
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
- Nodes: 10 (Homogeneous)
Kubernetes Clusters
Discovery
- Hardware:
- Nodes: 24 (Compute)
- Cores: 1100
- RAM: 3.2TB
- Storage: 34TB
- Nodes: 24 (Compute)
Endeavour
- Hardware:
- Nodes: 12 (Compute)
- Cores: 728
- GPU's: 12
- RAM: 5.2TB
- Storage: 42TB
- Nodes: 12 (Compute)
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
- Nodes: 1
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.