Aalto Scientific Computing¶
This site contains documentation about scientific and data-intensive computing at Aalto and beyond. It is targeted towards Aalto researchers, but has some useful information for everyone. The data management section is useful even to non-computational researchers.
Aalto Scientific Computing maintains these pages with the help of the Aalto community. [twitter] We consist of Science-IT (HPC, the Triton cluster), certain department ITs, and other friends. You can join us.
8/09/2021 Research Software Hour Twitch show is back at a different time. Join us today at 15:00 to talk about “Computers for research 101: The essential course that everyone skipped”.
Join our daily zoom garage for any scientific computing related issue (not just Triton!) or to just chat and feel part of the community.
The Aalto environment¶
Aalto provides a wide variety of support for scientific computing. For a summary, see the IT Services for Research page. For information about data storage at Aalto, see the section on data management below.
- Aalto tools
- Aalto account
- Aalto Linux
- Aalto Mac
- Aalto Windows
- Data storage
- Data: outline, requesting space, requesting access
- Science-IT data policy
- Remote Access
- Remote Jupyter Notebook on shell servers
- Paniikki: Computer Lab For Students
- Aalto Gitlab
- Open Source at Aalto
- Standalone Matlab
In this section, you can find some information and instructions on data management. Concrete information: main Aalto services and global services. Main Theoretical information: Aalto-specific summary and Aalto’s Research Data Management pages.
Triton is the Aalto high performance computing cluster. It is your go-to resources for anything that exceeds your desktop computer’s capacity.
Aalto Research Software Engineering¶
Skills to do science are different than skills to write good research code. The Aalto Research Software Engineering group provides support and mentoring to those using computing and data.
In this section, you find general (not Aalto specific) scientific computing resources.
We have various recommended training courses for researchers who deal with computation and data. These courses are selected by researchers, for researchers and grouped by level of skill needed.
These docs are open source: all content is licensed under CC-BY 4.0 and all examples under CC0 (public domain). Additionally, this is an open project and we strongly encourage anyone to contribute. For information, see the About this site and the Github links at the top of every page. Either make Github issues, pull requests, or ask for direct commit access. Be bold: the biggest problem is missing information, and mistakes can always be fixed.