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 computational and big data sciences. This is an applications course highlighting the use of modern computing platforms in solving computational and data science problems, enabling simulation, modelling 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 big data.


Lead Instructor:
Ignacio M. Llorente

David Sondak

Teaching Fellows:
Charles Liu
Matthew Holman

Time and Location

Lectures: Tuesday 2:30PM-4:00PM; Thursday 2:30PM-4:00PM
Location: MD G115

Labs: Wednesday 4:00PM-5:30PM
Location: Pierce 301

Office Hours

Ignacio: Wednesday from 12:00 PM to 1:00 PM in MD G107
David: Monday from 10:30 AM to 11:30 AM in MD G111
Charles: Thursday from 4:00 PM to 5:00 PM in IACS lobby
Matthew: Tuesday from 11:30 AM to 12:30 PM in IACS lobby


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