This year’s class will deal with “Immense acceleration of Big Data and AI through convergence with HPC – Scalable algorithms, systems and applications.
For details, please check http://matsu-www.is.titech.ac.jp/lecture/lecture-wiki/index.php?hpc2018
The grades will be marked in the following fashion
* Presentation: 50%
* Report: 50%
* For each class, if a student voluntarily asks a question or states a technical opinion, additional 5% credit will be added.
Supercomputer, Supercomputing, HPC, many-core / GPU, big data, Artificial Intelligence, Machine Learning, Deep Learning
✔ Specialist skills | ✔ Intercultural skills | ✔ Communication skills | ✔ Critical thinking skills | ✔ Practical and/or problem-solving skills |
In the first orientation, we will offer a list of English papers about “immense acceleration of Big Data and AI through convergence with HPC – Scalable algorithms, systems and applications.”
Each student will pick one of the papers, and give a presentation about the paper one or more times.
* There will be many questions that will be asked by the instructor, the TA, and the student audience. Questions will be asked to audiences as well from the instructor, and the TA, so full attention should be paid during the class.
* Each presentation will be 45 minutes to 90 minutes, varying with the number of students.
Students also have to submit a report about three papers they chose from the list, including the paper they presented.
Course schedule | Required learning | |
---|---|---|
Class 1 | History of Supercomputing | none |
Class 2 | Basics in High Performance Machine Learning | none |
Class 3 | Immense acceleration of Big Data and AI through convergence with HPC – Scalable algorithms, systems and applications | required reading of the paper before class |
Class 4 | Immense acceleration of Big Data and AI through convergence with HPC – Scalable algorithms, systems and applications | required reading of the paper before class |
Class 5 | Immense acceleration of Big Data and AI through convergence with HPC – Scalable algorithms, systems and applications | required reading of the paper before class |
Class 6 | Immense acceleration of Big Data and AI through convergence with HPC – Scalable algorithms, systems and applications | required reading of the paper before class |
Class 7 | Immense acceleration of Big Data and AI through convergence with HPC – Scalable algorithms, systems and applications | required reading of the paper before class |
Class 8 | Immense acceleration of Big Data and AI through convergence with HPC – Scalable algorithms, systems and applications | required reading of the paper before class |
Class 9 | Immense acceleration of Big Data and AI through convergence with HPC – Scalable algorithms, systems and applications | required reading of the paper before class |
Class 10 | Immense acceleration of Big Data and AI through convergence with HPC – Scalable algorithms, systems and applications | required reading of the paper before class |
Class 11 | Immense acceleration of Big Data and AI through convergence with HPC – Scalable algorithms, systems and applications | required reading of the paper before class |
Class 12 | Immense acceleration of Big Data and AI through convergence with HPC – Scalable algorithms, systems and applications | required reading of the paper before class |
Class 13 | Immense acceleration of Big Data and AI through convergence with HPC – Scalable algorithms, systems and applications | required reading of the paper before class |
Class 14 | Immense acceleration of Big Data and AI through convergence with HPC – Scalable algorithms, systems and applications | required reading of the paper before class |
Class 15 | Immense acceleration of Big Data and AI through convergence with HPC – Scalable algorithms, systems and applications | required reading of the paper before class |
none
Oyanagi, Sato, Nakamura, Matsuoka. "Supercomputer". Iwanami-Shoten (Iwanami Publishing), 2012 (in Japanese)
Presentation during class (40%), Q&A during class (30%), and the final report (30%).
Basic knowledge of computer architectures and parallel processing, Basic Knowledge of Machine learning