Computation has joined theory and experiment as the third pillar in scientific discovery. Multi-threaded and parallel programs are needed to drive manycore devices of the future. Modern applications produce or require huge volumes of data requiring computing systems that have data-intensive capabilities.

Through the application of Georgia Tech Technology Fee grants, the College of Computing has acquired and co-operates two high-performance computing (HPC) clusters for use in courses in parallel/distributed algorithms, large-scale data analysis, multi-core programming, and other computing fields. Faculty teaching courses in the School of Computational Science and Engineering (CSE), the School of Interactive Computing (IC), the School of Computer Science (CS), the School of Cybersecurity and Privacy (SCP), and affiliated faculty in the School of Electrical and Computer Engineering may request access to these HPC resources prior to the semester for their class projects. TSO uses the following guidelines for granting requests to access these HPC resources:

  • To maintain software reliability for all courses involved, only faculty requests sent prior to the start of semester to academicresources@cc will be considered.
  • To optimize resource utilization, special projects course needs are considered only after all core and breadth course needs are met.
  • To optimize resource utilization, research jobs will only be allowed on the coc-general cluster at Pace.
  • Because HPC systems must often be specialized for particular classes of problems or specific programming tools, we will not be able to accommodate all requests.

These HPC resources are located in the CODA HPC Data Center. For details, please refer to the individual cluster pages linked in the table below:

Instructional HPC Resources

23x Compute Nodes
(28 cores, 128 GB RAM)

14x Compute Nodes w/ GPU
(12 cores, 128GB RAM, 2x NVIDIA K40)

12x Compute Nodes w/ GPU
(8 cores, 128GB RAM, NVIDIA P100)

Red Hat Enterprise Linux 7

Supports CSE, CS, IC, SCP, and ECE courses requiring parallel and/or distributed compute jobs and/or medium- to large-scale data processing jobs

Access can be requested here -




2x Compute Nodes
(28 cores, 128 GB RAM)

2x High Mem Compute Nodes
(28 cores, 512GB RAM)

2x Compute Nodes w/ GPU
(8 cores, 128GB RAM, NVIDIA P100)

Red Hat Enterprise Linux 7

Supports general Research HPC computing for the College of Computing. Primarily designed for non-sponsored research in pursuit of a graduate degree.

Access can be requested by sending email to


If additional software is required, it can be requested using the following form - CoC Instructional Resource Software Request Form