We offer an extensive range of training courses, from Software and Data Carpentry courses that teach core research IT skills, though to specialised courses on programming and applications. Below you will find a helpful introduction to HPC at Imperial and a link to Imperial reserach computing and data sceince coourses. You will also find some general training links on our wiki pages and a list of past courses.
If you have any questions please contact the training coordinator Katerina Michalickova. We welcome suggestions regarding new training topics.
Introduction to HPC at Imperial
Inside you will find the following list of topics:
- What and why of HPC in a nutshell.
- Imperial cluster login.
- File transfer and management on the RDS filesystem.
- Finding, installing software and preparing Python and R dependencies.
- Deploying your programs (jobs) on the cluster.
- Working directory for a job.
- Cluster queuing system and job parameters.
- Exploring data parallelism with array runs - part 1.
- Exploring data parallelism with array runs - part 2.
- Program-level parallelism.
- OpenMP example.
- MPI example.
- Parallel Python.
Please note some administrative details (such as queue parameters) may change over time. Please check the RCS documentation for up to date information.
The above content is usually taught twice a term by the Research Computing and Data Science Programme at the Graduate School. Please follow the information in the tab below.
Imperial research computing and data science courses
The Research Computing and Data Science Programme (RCDS) at the Graduate School offers short courses in over 30 topics that are open to all Imperial students, researchers, and staff.
Visit the Graduate School website for a list of courses, dates, and registration information.