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 | Real-Time Crowd Dynamics | Daniel Inge, Maddy Nakada, Raymond Lin, Stephen Slater, William Fu | GitHub, Website |
| 2 | Visualizing a Galactic Dark Matter Simulation | Alpha Sanneh, Sihan Yuan, Will Claybaugh, and Kaley Brauer | GitHub, Website |
| 3 | Parallel YouTube Classification | Filip Michalsky, Dylan Randle, Tommy Hill, Paxton Maeder-York | GitHub, Website |
| 4 | Parallel Newton Step for the SCOPF problem | Srivatsan Srinivasan, Aditya Karan, Cory Williams, Manish Reddy Vuyyuru | GitHub, Website |
| 5 | Thunderstruct | Nick Pagel, Jonathan Guillotte-Blouin, Santiago Vargas | GitHub, Website |
| 6 | Bayesian Additive Regression Trees (BART) using Spark and CUDA | Beau Coker, Patrick Emedom-Nnamdi, Isabella Grabski, Hali Hambridge, Matthew Quinn | GitHub, Website |
| 7 | Density Equalizing Maps | Millie Zhou, Benedikt Groever, Baptiste Lemaire | GitHub, Website |
| 8 | Large-Scale Distributed Sentiment Analysis With RNNs | Jianzhun Du, Rong Liu, Matteo Zhang, Yan Zhao | GitHub, Website |
| 9 | Parallel Echo State Networks | Zachary Blanks, Cedric Flamant, Elizabeth Lim, Zhai Yi | GitHub, Website |
| 10 | Parallelization on Single-Nucleotide Variant (SNV) Calling | Pu Zheng, Zijie Zhao, Weihung Hsu, Kangli Wu | GitHub |
| 11 | Parallelized Amazon Recommendation System based on Spark and OpenMP | Zhaohong Jin, Zheyu Wu, Abhimanyu Vasishth, Yuhao Lu | GitHub, Website |
| 12 | Parallelized analysis of CRISPR genetic screens | Bhaven Patel, Rory Maizels, Hugo Ramambason | GitHub, Website |
| 13 | Parallel Simulation of Federated Learning | Danyun He, Xin Dong, Meng Dong, Ziao Lin | GitHub, Website |
| 14 | Space Chemistry | William Burke, Drake Deuel, Esmail Fadae, Jamila Pegues | GitHub, Website |