Spring 2018
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. CS205 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 and computing architectures for high performance computing and big data.
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 requirementd, collected the data, designed and implemented a parallel software, and demonstrated scaled performance of an end-to-end application.
Presented on 10 May 2018
| Group Number | Project Title | Team | Website |
|---|---|---|---|
| 1 | Parallelized Giant Sudoku Solver | Shiyun Qiu, Xiangru Shu, Yiqi Xie, Yuyue Wang | GitHub, Website |
| 2 | Real-time Tweet and Google trend analysis | Andrea Porelli, Yujiao Chen, Timothy Lee | GitHub |
| 3 | Genomic Sequencing Analysis Parallelization | Kar-Tong Tan, Nripsuta Saxena, Divyam Misra, Andrew Lund | GitHub |
| 4 | Real-time Image stitching and stabilization | Ziqi Guo, Weihang Zhang, Xuefeng Peng, Jiacheng Shi | GitHub, Website |
| 5 | Parallelize 2D Optical Flow Estimation Algorithm on Video | Shiyu Huang, Hongxiang Qiu, Zeyu Zhao, Zongren Zou | GitHub, Website |
| 6 | Parallel Rayleigh-Benard Convection | Shaan Desai, Yaniv Toledano, Bernard Kleynhans, Sebastien Lemieux-codere | GitHub, Website |
| 7 | Intercomparison of Historical Temperature Anomolies in Climate Models | Peter Sherman, Matt Stewart, Eimy Bonilla | GitHub |
| 8 | Transition Metal Dichalcogenide Interlayer Coupling Database | Shiang Fang, Steven Torrisi, Tianning Zhao, Eri Muramoto | Github Website |
| 9 | Parallelization of Data Preprocessing for Zoba, Inc. | Nate Stein, Justin Lee | Website |
| 10 | Distributed N-body astrophysical simulations using MPI | Ben Cook, Harshil Kamdar, Ana-Roxana Pop | GitHub, Website |
| 11 | Coordinated Sampler | Joel Dapello, Rui Fang, Erick Garcia, Zach Ward | GitHub |
| 12 | Parallelization and Optimization of Goldbach's Conjecture | Daniel Varon, Ada Shaw | GitHub |
| 13 | Parallelization and Optimization of Multigrid Solver | Luna Lin | GitHub, Website |
| 14 | Understanding Economic Complexity | Mali Akmanalp | Website GitHub |