Facilities | Computer Science and Engineering

The department has spacious, well-ventilated, well-illuminated, well-furnished, state-of-the-art laboratories with equipment required for the program. It also contains other facilities such as Internet, printing, and departmental library etc.

Department is enriched with many high-end laboratories like Cloud Computing and High-Performance computing lab, Big data analytics lab, and Internet of Things lab etc.

Some features of the laboratories are as:

  • The department has also implemented and hosted a private cloud for academic and research purposes. This facility provides infrastructure as a service(IaaS) and storage as a service (SaaS) that can be accessed from anywhere through the intranet.
  • The department also has a high-performance desktop supercomputer having computational capacity of 5 Teraflops with 2880 core processing nodes available on a Quad-core Intel Xeon workstation with a K2040 GPU. The setup is used to implement parallel algorithms and achieve computational speed-up of the order of 20X to 100X or greater.
  • A Hadoop Map-Reduce cluster is also available in the Project Lab. This setup is used to develop data storage solutions for storing data in a distributed environment. Analysis of big data using HDFS is carried out using MAP Reduce paradigm. This cluster is also used to store unstructured and semi-structured big data using NoSQL database like HBase. Workstations in Pseudo-Hadoop mode are also used to create a virtual distributed environment for big data analysis using H Base.
  • The project lab also has a Cloud-Based Web development setup using Microsoft Azure. This setup provides an environment for developing and deploying web-based cloud applications. Students can understand and analyze load balancing during scale up, and scale down demand variations.
  • A Beowulf Cluster using Open MPI is also available in the project lab. This cluster provides an avenue for students to implement parallel processing using Open MPI standards. This allows for dynamic division of computational tasks on multiple nodes and gathering of data for compilation of final result.

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