compute_resources

Computing Resources

The CS Dept. deploys general purpose compute and GPU compute servers, as well as specialized GPU servers, all running Linux. We also have database, NX/Nomachine (remote Linux desktop), a Windows desktop server, and dedicated servers for developing and running Apptainer containers. This section describes the general use and research use servers (see other sections for database and other server information).

portal load balanced servers

The portal servers are general purpose servers, running Linux, into which anyone can login. They are available for general use. They are also the “jump off” point for off Grounds connections to the CS network. The servers are in a load balanced cluster, and are accessed through ssh to portal.cs.virginia.edu. See: The portal cluster

Use these servers to code, compile, test, etc.. However these are not meant for long running processes that will use excessive resources. Computationally expensive processes should be run on other servers listed below.

(all GPU, Memory, CPU, etc. counts are per node).

Hostname	Memory (GB)	CPU Type	CPUs	Cores/CPU	Threads/Core	Total Cores
portal[01-12]	132-256	Intel	1	8	2	16

GPU servers

The gpusrv* servers are general purpose servers, running Linux, that contain GPUs into which anyone can login (via 'ssh'). They are intended for code development, testing, and short computations. Long running computations are discouraged, and are better suited to one of the GPU servers controlled by the job scheduler (see the SLURM section below).

(all GPU, Memory, CPU, etc. counts are per node).

Hostname	Memory (GB)	CPU Type	CPUs	Cores/CPU	Threads/Core	Total Cores	GPUs	GPU Type	GPU RAM (GB)
gpusrv[01-02,04-08]	256	Intel	1	10	2	20	4	Nvidia RTX 2080Ti	11
gpusrv03	256	Intel	1	10	2	20	3	Nvidia RTX 2080Ti	11
gpusrv[09-16]	512	Intel	2	10	2	40	4	Nvidia RTX 4000	8
gpusrv[17-18]	128	Intel	1	10	2	20	4	Nvidia RTX 2080Ti	11
gpusrv19	128	Intel	2	20	2	80	4	Nvidia RTX 2080Ti	11

Apptainer servers

The Apptainer servers, running Linux, allow users to instantiate and test containers. Once a user uses 'ssh' to login to the server, the user will execute module load apptainer to make the Apptainer container software available. Note that nodes available through the SLURM job scheduler will also allow general users to build and run Apptainer containers.

(all GPU, Memory, CPU, etc. counts are per node).

Hostname	Memory (GB)	CPU Type	CPUs	Cores/CPU	Threads/Core	Total Cores
apptainer01	256	Intel	2	10	2	40
apptainer02	64	Intel	1	4	2	8
apptainer03	8	Intel	1	2	2	4

Nodes controlled by the SLURM Job Scheduler

See our main article on Slurm for more information.

These servers, running Linux, are available by submitting a job through the SLURM job scheduler. They are not available for direct logins via 'ssh'. However, users can login to these servers without a job script using the 'srun -i' direct login command. See the SLURM section for more information.

Per CS Dept. policy, servers are placed into the SLURM job scheduling queues and are available for general use. Also, per that policy, if a user in a research group that originally purchased the hardware requires exclusive use of that hardware, they can be given a reservation for that exclusive use for a specified time. Otherwise, the systems are open for use by anyone with a CS account. This policy was approved by the CS Dept. Computing Committee comprised of CS Faculty.

This policy allows servers to be used when the project group is not using them. So instead of sitting idle and consuming power and cooling, other Dept. users can benefit from the use of these systems.

All GPU, Memory, CPU, etc. counts are per node.. This list may not be up to date. Use the 'sinfo' command for current server inventory.

Hostname	Mem (GB)	CPU	#CPUs	Cores	Threads / Core	Total Threads	GPUs	GPU Type	GPU RAM (GB)
adriatic[01-06]	1024	Intel	2	8	2	32	4	Nvidia RTX 4000	8
affogato[01-10]	128	Intel	2	8	2	32	0
affogato[11-15]	128	Intel	2	8	2	32	4	Nvidia GTX1080Ti	11
ai[01-04,06]	64	Intel	2	8	2	32	4	Nvidia GTX1080Ti	11
ai05	64	Intel	2	8	2	32	4	Nvidia GTX1080	11
ai[07-09]	128	Intel	2	8	2	32	4	Nvidia GTX1080Ti	11
ai10	128	Intel	2	8	2	32	4	Nvidia GTX1080	11
cheetah01	256	AMD	2	8	2	32	4	Nvidia A100	40
cheetah[02-03]	1024(2)	Intel	2	18	2	72	2	Nvidia RTX 2080Ti	11
cheetah04	1024	AMD	2	64	2	256	4	Nvidia A100	80(4)
cheetah05	768	AMD	2	16	2	64	4	Nvidia A100	80(4)
cortado[01-10]	512	Intel	2	12	2	48	0
doppio[01-05]	128	Intel	2	16	2	64	0
epona	64	Intel	1	4	2	8	0
heartpiece	160	Intel	2	10	2	40	0
hydro	256	Intel	2	16	2	64	0
jaguar01	1024	Intel	2	16	2	64	4	Nvidia A40	48 (3)
jaguar02	1024	Intel	2	8	2	32	8	Nvidia A16	16
jaguar03	256	AMD	2	56	2	224	8	Nvidia RTX A4500	20
jaguar04	256	Intel	2	16	2	64	4	Nvidia A40	48 (3)
jaguar05	256	Intel	1	8	2	16	4	Quadro RTX 4000	8
jaguar06	128	Intel	2	24	2	48	2	Nvidia A40	48
jinx[01-02]	160	Intel	1	24	2	48	1	Nvidia GTX1080	11
lotus	256	Intel	2	20	2	80	8	Nvidia RTX 6000	24
lynx01	64	Intel	4	8	2	32	4	Nvidia Titan X	12
lynx[01-04]	64	Intel	4	8	2	32	4	Nvidia GTX1080Ti	11
lynx[05-07]	64	Intel	4	8	2	32	4	Nvidia P100	16
lynx[08-09]	64	Intel	4	8	2	32	0
lynx10	64	Intel	4	8	2	32	1	Nvidia GTX1080Ti	11
lynx10	64	Intel	4	8	2	32	2	Nvidia GTX1080	8
lynx11	64	Intel	4	8	2	32	3	Nvidia Titan X	12
lynx11	64	Intel	4	8	2	32	1	Nvidia GTX1080	8
lynx12	64	Intel	4	8	2	32	4	Nvidia Titan X	12
optane01	1024(1)	Intel	2	16	2	64	0
panther01	512	Intel	1	8	2	16	0
pegasusboots	192	Intel	2	10	2	40	0
puma01	256	Intel	2	40	2	160	0
puma02	256	AMD	2	64	2	256	6	Nvidia RTX A6000	48
ristretto[01-04]	128	Intel	2	6	1	12	8	Nvidia GTX1080Ti	11
serval01	2048	Intel	2	56	2	224	4	Nvidia H100	80 (4)
sds[01-02]	512	Intel	2	10	2	40	4	Nvidia RTX A4000	16
slurm[1-5]	512	Intel	2	12	2	24	0
titanx[01-03]	256	Intel	1	8	2	16	1	Nvidia Titan X	12
titanx[04-05]	64	Intel	1	6	2	12	1	Nvidia Titan X	12

(1) 512GB Intel Optane memory, 512 DDR4 memory

(2) In addition to 1TB of DDR4 RAM, these servers also house a 900GB Optane NVMe SSD and a 1.6TB NVMe regular SSD drive

(3) Scaled to 96GB by pairing GPUs with NVLink technology that provides a maximum bi-directional bandwidth of 112GB/s between the paired A40s

(4) Scaled to 320GB by aggregating all four GPUs with NVLink technology

Job Scheduler Queues

See our main article on Slurm for more information on using queues (“partitions”)

The main partition contains nodes without GPUs, the gpu partition contains nodes with gpus, the nolom partition has nodes with no gpus and no time limit on jobs, and the gnolim partition has nodes with GPUs but no time limit on jobs. The time limit on jobs in the main and gpu partitions is shown using the 'sinfo' command.

Queue	Nodes
main	cortado[01-10], hydro, optane01, slurm[1-5]
gpu	adriatic[01-06], affogato[11-15], ai[01-08], cheetah[01-05], jaguar[01-03], lotus, lynx[01-12], ristretto[01-04], sds[01-02]
nolim	doppio[01-05], epona, heartpiece, pegasusboots
gnolim	ai[01-10], titanx[01-05]

Group specific computing resources

Several groups in CS deploy servers that are used exclusively by that group. Approximately 40 servers are deployed in this fashion, ranging from traditional CPU servers to specialized servers containing GPU accelerators.

Apptainer Servers

See our main article on Apptainer for more information.

The Apptainer servers, hostnames apptainer[01-03], are currently available to all department members. Apptainer containers can also be created and run on SLURM nodes.