QCL Computational Resources

High Performance Computing (HPC)

Supercomputing Allocations through ACCESS (formerly XSEDE)

ACCESS is a collection of integrated advanced digital resources and services that provide easy access to the most advanced computational resources and scientific research support in the world. QCL has two Campus Champions who are trained to help local users utilize supercomputing resources available through the Access program. From various national supercomputing facilities, QCL has awarded a large number of computing hours for testing and developing scientific applications (see below).

ACCESS Campus Champion Allocations (As of January 2023)

NameFacilitySU (Core hours)
Rockfish - GPUJohns Hopkins500 GPU hours
Rockfish - Large MemoryJohns Hopkins1,000 Core hours
Rockfish - Regular MemoryJohns Hopkins20,000 Core hours
KyRIC Large Memory NodesKentucky Research Informatics Cloud1,000 Core hours
JetstreamIndiana U50000 SUs
Bridges-2 Regular MemoryPSC50,000 SUs
Bridges Extreme MemoryPSC1,000 Core Hours
Bridges-2 GPUPSC2,500 GPU Hours
ANVIL CPUPurdue100,000 SUs
ANVIL GPUPurdue1,000 SUs
Stampede2TACC1,600 Node Hours
EXPANSE CPUSDSC50,000 Core Hours
DARWIN Compute NodeUD20,000 SUs
OSGMultiple200,000 SUs

The Service Unit (SU) is like a currency used to run an application on supercomputers. Supercomputer users are charged by SUs (hours of runtime on one core). For example, if an application ran on 100 cores for 10 hours, 1,000 SUs will be deducted from your account.

The Campus Champion allocations can be used to test computational research applications. To test out the supercomputers, please make an appointment with one of the CMC Campus Champions (email: qcl@cmc.edu).


A GPGPU (General Purpose Graphic Processing Unit) machine is a high performance computer system equipped with one or more GPUs. QCL has an NVIDIA DGX system having four Tesla V100 GPUs.

GPUs4X Tesla V100
TFLOPS (Mixed precision)500
GPU Memory128 GB total system
NVIDIA Tensor Cores2,560
NVIDIA CUDA Cores20,480
CPUIntel Xeon E5-2698 v4 2.2 GHz (20-Core)
System Memory256 GB RDIMM DDR4
Data StorageData: 3X 1.92 TB SSD RAID 0
OS StorageOS: 1X 1.92 TB SSD
NetworkDual 10GBASE-T (RJ45)

RStudio Server and JupyterHub

We have a dedicated server machine for RStudio and JupyterHub. Students taking a CMC course using R or Python, and participating in a QCL workshop may get access to the computational resources. To get access to the RStudio Server and/or JupyterHub, please contact qcl@cmc.edu for your account activated. The RStudio Server and JupyterHub are equipped with 2 CPUs (32 cores) and 512 GB of memory. So, it will perform better than your laptop or personal computer (for sure!) for those who need large memory space.