The software discussed and shown in these user guides is largely open source, can run on a desktop, HPC, or cloud environment, and can be installed with software management systems that support reproducibility (such as Conda, Singularity, and Docker). Below is a quick overview of some of the software, hardware, and confusing nomenclature that is used throughout this site.
SCINet vs. Ceres vs. Atlas
SCINet is the USDA ARS Scientific Computing INitiative that aims to improve access to high performance and cloud computing, improve networking to facilitate high speed data transfer, and facilitate scientific computational training. The Virtual Research Support Core (VRSC)
Ceres is one of the HPC systems (located in Iowa) connected to the SCINet infrastucture. The system is largely maintained by staff at Iowa State University.
Atlas is the more recently added HPC system
For more information on the HPC systems that SCINet offers, see the Computer Systems page.
Open OnDemand
Open OnDemand is an intuitive, innovative, and interactive interface to remote computing resources. The key benefit for SCINet users is that it can be used on any web browser, including browsers on a mobile phone, to access Ceres. See the Open OnDemand guide for more information.
There are several interactive apps that can be run in Open OnDemand including Jupyter, RStudio Server, Geneious, CLC Genomics Workbench, and more. The desktop app allows a user to run any GUI software.
SLURM
SLURM (Simple Linux Utility for Resource Management) is the workload manager used on the Ceres and Atlas HPC system to allocate computational resources. From the SLURM documentation, SLURM is “an open source… cluster management and job scheduling system for large and small Linux clusters. As a cluster workload manager, SLURM has three key functions. First, it allocates exclusive and/or non-exclusive access to resources (compute nodes) to users for some duration of time so they can perform work. Second, it provides a framework for starting, executing, and monitoring work (normally a parallel job) on the set of allocated nodes. Finally, it arbitrates contention for resources by managing a queue of pending work.”
Scientific Coding Languages - Python and R
Python and R appear to be the most common scientific programming languages used across ARS. However, many of these examples could also be run in other programming languages such as Julia, Go, IDL/ENVI, etc.