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This course reuses the content of AI/ML course to remove and add new contents. To be able to reserving orginal content, I duplicated it here to work felexieble.
This four-part deep dive walks through CETRA, a GPU-accelerated algorithm for detecting transiting exoplanets in space-based photometry. Built for the scale of ESA's upcoming PLATO mission, CETRA reworks the classic transit-search problem — normally solved with CPU-bound algorithms like BLS and TLS — to run efficiently on thousands of parallel GPU threads, achieving speedups of 40x or more while also improving detection sensitivity. The series is aimed at researchers who want to understand both the astrophysics motivating the design choices and the GPU programming principles that make them work. No prior CUDA experience is assumed, but familiarity with Python and basic HPC concepts will help.
This course created testing purpose. Mainly, will be using LTI- Moodle, Azimuth- Moodle integration works. Additionally, kubernetes pods will be testing via this course.Gokmen.