NJIT has three main computer clusters used by researchers:

CAPPL – Cappl is an Intel-based Dell cluster with 17 dual processor nodes, with 1 GB RAM per node.  Faculty, students, and researchers in Electrical and Computer Engineering have priority access to Cappl.  Cappl runs the Linux operating system. 

HYDRA – Hydra is an AMD Opteron-based cluster from Microway with 67 dual processor nodes, and 2 GB RAM per node.  Hydra was acquired as part of an NSF instrumentation grant.  Faculty, students, and researchers in Mathematical Sciences and the Center for Applied Mathematics and statistics have priority access to Hydra.  Hydra runs the Linux operating system. 

KONG – Kong is a Sun Microsystems Discovery cluster with 112 AMD Opteron dual-core nodes with 2GB of RAM per node.  Approximately 20% of Kong’s resources are prioritized to a group of NJIT researchers who contributed funds for its acquisition. The remaining resources are open to the NJIT community of researchers.

These three compute clusters are unified using the Sun Microsystems Grid Engine software, a widely used open source utility for managing computational grids.  The NJIT computational grid provides approximately 2.2 teraflops of computational power.

Staff within University Computing Systems is working with local research universities to link our grid with other resources to widen the opportunities available for collaborating researchers.   

Access to any of these resources requires a Highlander AFS account.

Computational Grids vs. Compute Clusters

Computational grids enable the sharing, selection, and aggregation of a wide variety of geographically distributed computational resources (such as supercomputers, compute clusters, storage systems, data sources, instruments, people) and presents them as a single, unified resource for solving large-scale compute and data intensive computing applications.

NJIT is in the process of standardizing on groups ("clusters") of commodity computers as the vehicle for providing high performance computing services for researchers.  A cluster is a computing approach with a relatively large number of processors with very high-speed interconnects under the control of specialized scheduling and resource management software.  Clusters are designed for parallel processing.  These compute nodes can act independently, or in parallel, to handle large-scale computationally demanding tasks.   The user interacts with the cluster via scheduling and resource management software. This software can perform these same functions for groups of clusters (or other computational elements).  Scheduling and allocation of resources are determined by policies that are dependent on various factors, including individual ownership of nodes in a cluster.

The key difference between clusters and grids is in the way resources are managed.  In a compute cluster, all resources are managed by a centralized resource manager and nodes work cooperatively as a single unified resource.  In a grid, each node has its own resource manager and the gird engine software works to find resources available on the grid to share and aggregate distributed computational resources and deliver them as a service.  Grids are useful in allowing small computation resources to contribute to the solution of a task that is far too large for any one of those resources to handle. This idea is analogous to electric power network grids, where power generators are distributed, but the users are able to access electric power without bothering about the source of energy and its location.

For further information on grids, visit the Grid Computing Information Centre .