Grant Proposal: Difference between revisions
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= Introduction = | = 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 If you see an outdated resource or a missing resource please contact techstaff@cs.vt.edu.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 = | = Resources Available in the Department = | ||
== RLogin == | == RLogin == | ||
* Hardware: | |||
** Nodes: 32 Compute 5 GPU | |||
*** Cores: 32 Intel | |||
*** RAM: 384GB | |||
*** Storage: 1TB | |||
** Network: 10Gbit interconnect | |||
== CSRVM == | == 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. | 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. | |||
** Cores: At least 40 | == CSRGPU == | ||
** Ram: At least 192GB | 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: | ||
* Storage: | ** 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 | |||
== Secure Server Room == | == 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. | 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 = | ||
[https://arc.vt.edu/ Advanced Research Computing - ARC] |
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.