cs205

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 2019

Lead Instructor:
Ignacio M. Llorente

Teaching Fellows:
Zudi Lin
Nicholas Stern
Kar Tong Tan

Time and Location

Lectures: Tuesday 1:30PM-2:45PM; Thursday 1:30PM-2:45PM
Location: Science Center Hall A

Labs: Wednesday 4:30PM-5:45PM
Location: Pierce Hall 301

Office Hours

Ignacio M. Llorente: Wednesday 12:00PM-1:00PM in Maxwell Dworkin, B125
Zudi Lin: Monday 11:00AM-12:00PM in IACS Lounge
Nicholas Stern: Tuesday 10:00AM-11:00AM in IACS Lounge
Kar Tong Tan: Thursday 3:00PM-4:00PM in IACS Lounge

Acknowledgments

The course includes several guest lectures by the FAS Division of Science, Research Computing Group at Harvard University about how to use the Odyssey cluster for GPU, OpenMP, and MPI jobs.

Research Computing