Nexus/CBCB

From UMIACS
Revision as of 19:32, 2 December 2024 by Mbaney (talk | contribs) (→‎Network)
(diff) ← Older revision | Latest revision (diff) | Newer revision → (diff)
Jump to navigation Jump to search

The compute nodes from CBCB's previous standalone cluster have folded into Nexus as of mid 2023.

The Nexus cluster already has a large pool of compute resources made possible through college-level funding for UMIACS and CSD faculty. Details on common nodes already in the cluster (Tron partition) can be found here.

Please contact staff with any questions or concerns.

Submission Nodes

You can SSH to nexuscbcb.umiacs.umd.edu to log in to a submission node.

If you store something in a local filesystem directory (/tmp, /scratch0) on one of the two submission nodes, you will need to connect to that same submission node to access it later. The actual submission nodes are:

  • nexuscbcb00.umiacs.umd.edu
  • nexuscbcb01.umiacs.umd.edu

Compute Nodes

All compute nodes in CBCB-owned partitions (see below section) owned by CBCB faculty are named in the format cbcb##. The sets of nodes are:

  • 22 nodes that were purchased in October 2022 with center-wide funding. They are cbcb[00-21].
  • 4 nodes from the previous standalone CBCB cluster that moved in as of Summer 2023. They are cbcb[22-25].
  • A few additional nodes purchased by Dr. Heng Huang since then. They are all remaining 'cbcb' named nodes.
Nodenames Quantity CPU cores per node (CPUs) Memory per node (type) Filesystem storage per node (type/location) GPUs per node (type)
cbcb[00-21] 22 32 (Dual AMD EPYC 7313) ~2TB (DDR4 3200MHz) ~350GB (SATA SSD /scratch0), ~2TB (NVMe SSD /scratch1) 0
cbcb22 1 28 (Dual Intel Xeon E5-2680 v4) ~768GB (DDR4 2400MHz) ~650GB (SATA SSD /scratch0) 0
cbcb[23-24] 2 24 (Dual Intel Xeon E5-2650 v4) ~256GB (DDR4 2400MHz) ~800GB (SATA SSD /scratch0) 0
cbcb25 1 24 (Dual Intel Xeon E5-2650 v4) ~256GB (DDR4 2400MHz) ~1.4TB (SATA SSD /scratch0) 2 (1x NVIDIA GeForce GTX 1080 Ti, 1x NVIDIA GeForce RTX 2080 Ti)
cbcb26 1 128 (Dual AMD EPYC 7763) ~512GB (DDR4 3200MHz) ~3.4TB (NVMe SSD /scratch0), ~14TB (NVMe SSD /scratch1) 7 (NVIDIA RTX A5000)
cbcb27 1 64 (Dual AMD EPYC 7513) ~256GB (DDR4 3200MHz) ~3.4TB (SATA SSD /scratch0), ~3.5TB (NVMe SSD /scratch1) 8 (NVIDIA RTX A6000)
cbcb[28-29] 2 32 (Dual AMD EPYC 9124) ~768GB (DDR5 4800MHz) ~350GB (SATA SSD /scratch0), ~7TB (NVMe SSD /scratch1) 8 (NVIDIA RTX 6000 Ada Generation)
Total 30 1060 (various) ~49TB (various) ~94TB (various) 33 (various)

Here is the listing of nodes as shown by the Slurm alias show_nodes (again, all nodes are named in the format cbcb##): 

[root@nexusctl00 ~]# show_nodes | grep cbcb
NODELIST             CPUS       MEMORY     AVAIL_FEATURES                           GRES                             STATE
cbcb00               32         2061175    rhel8,x86_64,Zen,EPYC-7313               (null)                           idle
cbcb01               32         2061175    rhel8,x86_64,Zen,EPYC-7313               (null)                           idle
cbcb02               32         2061175    rhel8,x86_64,Zen,EPYC-7313               (null)                           idle
cbcb03               32         2061175    rhel8,x86_64,Zen,EPYC-7313               (null)                           idle
cbcb04               32         2061175    rhel8,x86_64,Zen,EPYC-7313               (null)                           idle
cbcb05               32         2061175    rhel8,x86_64,Zen,EPYC-7313               (null)                           idle
cbcb06               32         2061175    rhel8,x86_64,Zen,EPYC-7313               (null)                           idle
cbcb07               32         2061175    rhel8,x86_64,Zen,EPYC-7313               (null)                           idle
cbcb08               32         2061175    rhel8,x86_64,Zen,EPYC-7313               (null)                           idle
cbcb09               32         2061175    rhel8,x86_64,Zen,EPYC-7313               (null)                           idle
cbcb10               32         2061175    rhel8,x86_64,Zen,EPYC-7313               (null)                           idle
cbcb11               32         2061175    rhel8,x86_64,Zen,EPYC-7313               (null)                           idle
cbcb12               32         2061175    rhel8,x86_64,Zen,EPYC-7313               (null)                           idle
cbcb13               32         2061175    rhel8,x86_64,Zen,EPYC-7313               (null)                           idle
cbcb14               32         2061175    rhel8,x86_64,Zen,EPYC-7313               (null)                           idle
cbcb15               32         2061175    rhel8,x86_64,Zen,EPYC-7313               (null)                           idle
cbcb16               32         2061175    rhel8,x86_64,Zen,EPYC-7313               (null)                           idle
cbcb17               32         2061175    rhel8,x86_64,Zen,EPYC-7313               (null)                           idle
cbcb18               32         2061175    rhel8,x86_64,Zen,EPYC-7313               (null)                           idle
cbcb19               32         2061175    rhel8,x86_64,Zen,EPYC-7313               (null)                           idle
cbcb20               32         2061175    rhel8,x86_64,Zen,EPYC-7313               (null)                           idle
cbcb21               32         2061175    rhel8,x86_64,Zen,EPYC-7313               (null)                           idle
cbcb22               28         771245     rhel8,x86_64,Xeon,E5-2680                (null)                           idle
cbcb23               24         255150     rhel8,x86_64,Xeon,E5-2650                (null)                           idle
cbcb24               24         255150     rhel8,x86_64,Xeon,E5-2650                (null)                           idle
cbcb25               24         255278     rhel8,x86_64,Xeon,E5-2650,Pascal,Turing  gpu:rtx2080ti:1,gpu:gtx1080ti:1  idle
cbcb26               128        513243     rhel8,x86_64,Zen,EPYC-7763,Ampere        gpu:rtxa5000:7                   idle
cbcb27               64         255167     rhel8,x86_64,Zen,EPYC-7513,Ampere        gpu:rtxa6000:8                   idle
cbcb28               32         771166     rhel8,x86_64,Zen,EPYC-9124,Ada           gpu:rtx6000ada:8                 idle
cbcb29               32         771166     rhel8,x86_64,Zen,EPYC-9124,Ada           gpu:rtx6000ada:8                 idle

Network

The network infrastructure supporting the CBCB partition consists of:

  1. One pair of network switches connected to each other via dual 100GbE links for redundancy, serving the following compute nodes:
    • cbcb[00-21,26-29]: Two 100GbE links per node, one to each switch in the pair (redundancy).
  2. One pair of network switches connected to the above pair of network switches via four 40GbE links, one between every combination of switches across the two pairings for redundancy, and to each other via dual 10GbE links for redundancy.
    • cbcb[22-25]: Two 10GbE links per node, one to each switch in the pair (redundancy).

For a broader overview of the network infrastructure supporting the Nexus cluster, please see Nexus/Network.

Partitions

There are two partitions available to general CBCB SLURM users. You must specify one of these two partitions when submitting your job.

  • cbcb - This is the default partition. Job allocations on all nodes except those also in the cbcb-heng partition are guaranteed.
  • cbcb-interactive - This is a partition that only allows interactive jobs; you cannot submit jobs via sbatch to this partition. Job allocations are guaranteed.

There is one additional partition available solely to Dr. Heng Huang's sponsored accounts.

  • cbcb-heng - This partition is for exclusive priority access to Dr. Huang's purchased GPU nodes. Job allocations are guaranteed.

QoS

CBCB users have access to all of the standard job QoSes in the cbcb and cbcb-heng partitions using the cbcb account.

The additional job QoSes for the cbcb and cbcb-heng partitions specifically are:

  • highmem: Allows for significantly increased memory to be allocated.
  • huge-long: Allows for longer jobs using higher overall resources.

Please note that the partition has a GrpTRES limit of 100% of the available cores/RAM on the partition-specific nodes in aggregate plus 50% of the available cores/RAM on legacy## nodes in aggregate, so your job may need to wait if all available cores/RAM (or GPUs) are in use.

The only allowed job QoS for the cbcb-interactive partition is:

  • interactive: Allows for 4 CPU / 128G mem jobs up to 12 hours in length - can only be used via srun or salloc.

Jobs

You will need to specify --partition=cbcb and --account=cbcb to be able to submit jobs to the CBCB partition. 

[username@nexuscbcb00:~ ] $ srun --pty --ntasks=16 --mem=2000G --qos=highmem --partition=cbcb --account=cbcb --time 1-00:00:00 bash
srun: job 218874 queued and waiting for resources
srun: job 218874 has been allocated resources
[username@cbcb00:~ ] $ scontrol show job 218874
JobId=218874 JobName=bash
   UserId=username(1000) GroupId=username(21000) MCS_label=N/A
   Priority=897 Nice=0 Account=cbcb QOS=highmem
   JobState=RUNNING Reason=None Dependency=(null)
   Requeue=1 Restarts=0 BatchFlag=0 Reboot=0 ExitCode=0:0
   RunTime=00:00:06 TimeLimit=1-00:00:00 TimeMin=N/A
   SubmitTime=2022-11-18T11:13:56 EligibleTime=2022-11-18T11:13:56
   AccrueTime=2022-11-18T11:13:56
   StartTime=2022-11-18T11:13:56 EndTime=2022-11-19T11:13:56 Deadline=N/A
   PreemptEligibleTime=2022-11-18T11:13:56 PreemptTime=None
   SuspendTime=None SecsPreSuspend=0 LastSchedEval=2022-11-18T11:13:56 Scheduler=Main
   Partition=cbcb AllocNode:Sid=nexuscbcb00:25443
   ReqNodeList=(null) ExcNodeList=(null)
   NodeList=cbcb00
   BatchHost=cbcb00
   NumNodes=1 NumCPUs=16 NumTasks=16 CPUs/Task=1 ReqB:S:C:T=0:0:*:*
   TRES=cpu=16,mem=2000G,node=1,billing=2266
   Socks/Node=* NtasksPerN:B:S:C=0:0:*:* CoreSpec=*
   MinCPUsNode=1 MinMemoryNode=2000G MinTmpDiskNode=0
   Features=(null) DelayBoot=00:00:00
   OverSubscribe=OK Contiguous=0 Licenses=(null) Network=(null)
   Command=bash
   WorkDir=/nfshomes/username
   Power=

Storage

CBCB still has its current storage allocation in place. All data filesystems that were available in the standalone CBCB cluster are also available in Nexus. Please note about the change in your home directory in the migration section below.

CBCB users can also request Nexus project allocations.

Migration

Operating System / Software

CBCB's standalone cluster submission and compute nodes were running RHEL7. Nexus is exclusively running RHEL8, so any software you may have compiled may need to be re-compiled to work correctly in this new environment. The CBCB module tree for RHEL8 may not yet be fully populated with RHEL8 software. If you do not see the modules you need, please reach out to the CBCB software maintainers.

Retrieved from "https://wiki.umiacs.umd.edu/umiacs/index.php?title=Nexus/CBCB&oldid=12306"