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From Research Computing Website
Cray Urika-XC is a high performance big data software stack, which is optimized for multiple work-flows and runs on the Cray XC series systems. It features a comprehensive analytics software stack for capturing and organizing a wide variety of data types from different sources and for executing a variety of analytic jobs on them. In addition, the Urika-XC software stack features components for performing machine and deep learning tasks. Urika-XC consists of two components, Open Source Analytics (OSA) and Cray Graph Engine (CGE). They may be installed separately or together. OSA is based on images that run inside Shifter containers, while CGE is a user-level binary application. Urika-XC software can be used with CLE 6.0 UP02 and later CLE releases.
Containerization is becoming an important part of high-performance computing environments, offering portability and rapid deployment of applications and workflows previously difficult to accommodate on clusters and supercomputers. Singularity is an increasingly popular implementation of containerization targeted specifically to HPC systems. Importantly, Singularity allows users to effortlessly convert Docker containers into its native format. Singularity containers can also be used in Slurm job files without the need for any Slurm integration, special job directives, or any other fancy acrobatics! In fact, depending on how the container is built and on the application it encapsulates, users could conceivably employ singularity containers (at the command line or inside a Slurm job) thinking they are simply running a native application. Come and learn about this exciting containerization technology already available on raad2!
You can use RAAD to port your MATLAB compute intensive applications. You can use MATLAB either in batch or Interactive mode(With Graphical interface). In training, we will be using Interactive session of MATLAB. You must have installed MobaXterm on your laptops. MATLAB can also be used with our RAAD2-GPU cluster. To use Matlab with GPU, you need to have access to HPC Cluster. Once you login to RAAD, you need to submit an interactive job requesting access to GPU nodes.
Training on a newer version of MATLAB