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2018　High Performance Computing

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Academic unit or major
Graduate major in Mathematical and Computing Science
Instructor(s)
Matsuoka Satoshi  Matsuoka Satoshi  Endo Toshio 
Class Format
Lecture     
Media-enhanced courses
Day/Period(Room No.)
Intensive ()  
Group
-
Course number
MCS.T407
Credits
2
Academic year
2018
Offered quarter
3-4Q
Syllabus updated
2018/11/21
Lecture notes updated
-
Language used
Japanese
Access Index

Course description and aims

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

Student learning outcomes

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.

Keywords

Supercomputer, Supercomputing, HPC, many-core / GPU, big data, Artificial Intelligence, Machine Learning, Deep Learning

Competencies that will be developed

✔ Specialist skills ✔ Intercultural skills ✔ Communication skills ✔ Critical thinking skills ✔ Practical and/or problem-solving skills

Class flow

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

  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

Textbook(s)

none

Reference books, course materials, etc.

Oyanagi, Sato, Nakamura, Matsuoka. "Supercomputer". Iwanami-Shoten (Iwanami Publishing), 2012 (in Japanese)

Assessment criteria and methods

Presentation during class (40%), Q&A during class (30%), and the final report (30%).

Related courses

  • MCS.T233 ： Computer Systems
  • MCS.T334 ： Compiler Construction
  • MCS.T213 ： Introduction to Algorithms and Data Structures
  • MCS.T214 ： Theory of Automata and Languages

Prerequisites (i.e., required knowledge, skills, courses, etc.)

Basic knowledge of computer architectures and parallel processing, Basic Knowledge of Machine learning

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