Spring 2019
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.
Main course site: Harvard-CS205.org
Extreme scale data science at the convergence of big data and massively parallel computing is enabling simulation, modelling and real-time analysis of complex natural and social phenomena at unprecedented scales. The aim of the projects is to gain practical experience into this interplay by applying parallel computation principles in solving a compute and data-intensive problem.
These final projects solve a data-intensive or a compute-intensive problem with parallel processing on the AWS cloud or on Harvard’s supercomputer: Odyssey (or both!). They have identified a compute or and data science problem, analysed its compute scaling requirements, collected the data, designed and implemented a parallel software, and demonstrated scaled performance of an end-to-end application.
Presented on 8 May 2019
| Group Number | Project Title | Team | Website |
|---|---|---|---|
| 1 | Your_Project_Title | Member_A, Member_B, Memver_C, Member_D | GitHub, Website |
| 2 | Your_Project_Title | Member_A, Member_B, Memver_C, Member_D | GitHub, Website |
| 3 | Your_Project_Title | Member_A, Member_B, Memver_C, Member_D | GitHub, Website |
| 4 | Parallel Newton Step for the SCOPF problem | Srivatsan Srinivasan, Aditya Karan, Cory Williams, Manish Reddy Vuyyuru | GitHub, Website |
| 5 | Your_Project_Title | Member_A, Member_B, Memver_C, Member_D | GitHub, Website |
| 6 | Your_Project_Title | Member_A, Member_B, Memver_C, Member_D | GitHub, Website |
| 7 | Your_Project_Title | Member_A, Member_B, Memver_C, Member_D | GitHub, Website |
| 8 | Your_Project_Title | Member_A, Member_B, Memver_C, Member_D | GitHub, Website |
| 9 | Your_Project_Title | Member_A, Member_B, Memver_C, Member_D | GitHub, Website |
| 10 | Your_Project_Title | Member_A, Member_B, Memver_C, Member_D | GitHub, Website |
| 11 | Your_Project_Title | Member_A, Member_B, Memver_C, Member_D | GitHub, Website |
| 12 | Your_Project_Title | Member_A, Member_B, Memver_C, Member_D | GitHub, Website |
| 13 | Your_Project_Title | Member_A, Member_B, Memver_C, Member_D | GitHub, Website |
| 14 | Your_Project_Title | Member_A, Member_B, Memver_C, Member_D | GitHub, Website |