Summary of Systems

Note that we have two generally-available “clusters,” faculty and ilab. The sections below will describe each. Each cluster has its own home directories. So if you have access to both ilab and faculty, you won’t be able to see your ilab home directory from the faculty systems and visa versa. There is a common file system shared by all clusters, /common/users. Your directory on it is /common/users/NETID. Quotas on /common/users are 100 GB.

IMPORTANT CS Linux System has enforced limitations that all users should be aware of. Make sure to check the page before running a big job.

Systems Status

Current System Status

Faculty

Faculty offices, plus some servers. There are two servers, constance.cs and porthos.cs. However we recommend logging in to faculty.cs.rutgers.edu. That will give you either constance.cs or porthos.cs, randomly. If one is down it will give you the one that is up.

These are not very powerful systems. They’re intended primarily to keep records in an environment with no students. Serious computing is normally done on the research cluster.

Faculty Machines

Student

Our student system is called iLab cluster consisting of a set of 7 large servers ( some with  RTX1080/2080 Ti GPUs), instructional Hadoop cluster and  85 desktop in iLabs (2nd floor Hill Center 248, 252, 254) and grad student offices. Most students access these systems remotely, by ssh, X2Go or XRDP.  Please use the alias ilab.cs.rutgers.edu,which will give you one of the three large servers. You can also look at the iLab status page to find a desktop system that isn’t in heavy use.

More informaton about Student systems, and instructions on using them

Instructional Laboratory (iLab) Systems: Announcements and information about rooms

Details iLab System Specs

  • 85 of the iLab desktop systems, 72 have Nvidia GT 630 or 730
  • 3 iLab servers each have 8 RTX 1080Ti GPUs  with 1TB of memory.
  • 4 iLab servers each have 2 RTX 2080ti  GPUs  with 256GB of memory,
  • 1 iLab server with 1TB of memory, no GPU
  • atlas.cs: Nvidia K40, Nvidia Quadro K4000
  • cray1.cs: 2 Nvidia K80
  • gpu.cs: Nvidia K40, Nvidia Titan

CBIM systems

CBIM has a set of servers with large memory and GPUs. Those outside CBIM need permission from Dimitri Metaxas to use them, but that is generally available for CS researchers.

  • 12 Dell systems with 4 Nvidia K80 each, 512 G memory
  • 3 HP systems, with 2 Nvidia K80 each, 256 G memory.
  • 1 SuperMicro  with 8 GTX 1080ti, 512GB memory

High-performance systems: OARC

OARC is a University group that provides high-performance computing. Computer science in general doesn’t have a conventional HPC cluster. We concentrate on GPUs and more specialized hardware. For large-scale HPC, OARC is the best source. They have a large cluster, Amarel. It is intended as a “condo” cluster. I.e. grants buy nodes, and are guaranteed at least as much capacity as they purchased. The cost is matched by the University. However some capacity is available for those who haven’t bought into the system, particularly for course work and student use.

For more information see the OARC web site.

Some of OARCs nodes have GPUs, typically Nvidia.

Virtual machines

LCSR can provide virtual machines, both for researchers and for use by classes. To request a system, please send mail to help@cs.rutgers.edu.

Researchers commonly use VMs for web servers and other support services. We normally put those VMs on the same servers used for LCSR infrastructure.

For course use, we talk with the instructor to find out the configuration and software needed, then create one small VM per user. We can also create limited VMs for grad students. These VMs are placed on one of two large (1 TB each) VM servers purchased specifically for instruction.

Virtual Machines for Student Academic Use

Virtual Machines for Faculty and Research

Others

Windows Remote Desktop — NOTE: This service is discontinued FALL 2018 due to no usage

Getting Help

1. If you have issues with any CS computing resources

2. If you have issues with Non CS computing resources