CS205: Computing Foundations for Computational Science
Computational science has become a third partner, together with theory and experimentation, in advancing scientific knowledge and practice, and an essential tool for product and process development and manufacturing in industry. Big data science adds the 'fourth pillar' to scientific advancements, providing the methods and algorithms to extract knowledge or insights from data.
The course is a journey into the foundations of Parallel Computing at the intersection of large-scale computational science and big data analytics. Many science communities are combining high performance computing and high-end data analysis platforms and methods in workflows that orchestrate large-scale simulations or incorporate them into the stages of large-scale analysis pipelines for data generated by simulations, experiments, or observations.
This is an applications course highlighting the use of modern computing platforms in solving computational and data science problems, enabling simulation, modeling and real-time analysis of complex natural and social phenomena at unprecedented scales. The class emphasizes on making effective use of the diverse landscape of programming models, platforms, open-source tools, computing architectures and cloud services for high performance computing and high-end data analytics.
Staff - Spring 2020
Office Hour: Thursday 3:00PM-4:00PM
Ignacio M. Llorente
Office Hour: 8:00AM-9:00AM on 3/26 Thur and 4/2 Thurs
|Dylan Randle||Hayoun Oh||Zhiying Xu||Zijie Zhao|
11:00 AM - 12:00 PM
6:00 PM - 8:00 PM
6:00 PM - 7:00 PM
3:00 PM - 4:00 PM
All lectures, labs, and office hours will be held through zoom, using the following link:
Lectures: Tuesday 1:30PM-2:45PM; Thursday 1:30PM-2:45PM
Labs: Wednesday 6:00PM-7:30PM
The course includes several guest lectures by the FAS Divison of Science, Research Computing Group at Harvard University about how to use the Cannon cluster for GPU, OpenMP, and MPI jobs.