12 Jun 1:00 pm 2:30 pmIn about 90 minutes, learn how to use the SciNet systems Niagara and Mist, from securely logging in to running computations on the supercomputer. Experienced users may still pick up some valuable pointers.THIS EVENT HAS BEEN CANCELED. Virtual | HPC105 - Jun 2024![Show in Google map](https://www.scinethpc.ca/wp-content/plugins/amr-ical-events-list/images/map_16.png) |
12 Jun 1:30 pm 4:30 pmThis course is intended to help learners with a basic understanding of fluid dynamics and CFD bridge the knowledge gap towards the effective utilization of CFD on modern HPC architectures. This course will take an end-user approach to CFD tools on HPC systems (no coding) and, despite some prerequisites, will be given at an introductory/intermediate level (we will not cover advanced topics such as GPU or dynamic load-balancing). At the end of the course, the learner will be able to: Develop a systematic approach to estimate the HPC cost of a CFD problem. Explain the impact of modelling assumptions on HPC cost. Optimize the parameters and simulations for effective HPC usage. The course will use an entirely open source suite of CFD toolsets to mesh (Gmsh), simulate (OpenFoam/SU2), and visualize (Visit/Paraview). It should be noted that this is not a CFD course; therefore, undergraduate-level knowledge of CFD and numerical methods is expected, as well as a basic understanding of the Compute Ontario HPC system. The focus is on the effective use of CFD tools in modern HPC systems. Level: Intermediate, Length: Three 1-Hour Sessions (3 Days), Format: Lecture + Hands-on, Prerequisites: Undergraduate-level knowledge of fluid dynamics (ideally with some knowledge of turbulence), CFD, and numerical methods. (part of the 2024 Compute Ontario Summer School) Virtual | COSS2024![Show in Google map](https://www.scinethpc.ca/wp-content/plugins/amr-ical-events-list/images/map_16.png) |
12 Jun 1:30 pm 4:30 pmSome popular Python libraries for data analytics, like Numpy, Pandas, Scikit-Learn, etc., usually work well if the dataset fits into the RAM on a single machine. When dealing with large datasets, it could be a challenge to work around memory constraints. This course introduces scalable and accelerated data analytics with Dask and RAPIDS. Dask provides a framework and libraries that can handle large datasets on a single multi-core machine or across multiple machines on a cluster. RAPIDS, on the other hand, can accelerate your data analytics by offloading analytics workloads to GPUs with less effort in code changes. Level: Introductory Length: Two 3-Hour Sessions (2 Days) Format: Lecture + Hands-on Prerequisites: Alliance Account Basic Python and Linux command line experience. (part of the 2024 Compute Ontario Summer School) Virtual | COSS2024![Show in Google map](https://www.scinethpc.ca/wp-content/plugins/amr-ical-events-list/images/map_16.png) |