2017 High Performance Computing

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Academic unit or major
Graduate major in Mathematical and Computing Science
Instructor(s)
Matsuoka Satoshi  Endo Toshio 
Course component(s)
Lecture     
Day/Period(Room No.)
Tue3-4(H119A)  Fri3-4(H119A)  
Group
-
Course number
MCS.T407
Credits
2
Academic year
2017
Offered quarter
3Q
Syllabus updated
2017/10/9
Lecture notes updated
-
Language used
English
Access Index

Course description and aims

Students delve into several of the newest topics of the constitution method through key articles, with a focus on supercomputers. The largest scale modern supercomputers have several million CPU cores, and internal parallelism from several million to 10's of millions through hardware multithreading. In addition, to provide them with data, there is a petabyte/seconds level memory system, and the whole is connected by a network with performance that rivals the internet as a whole. By discussing these technologies in class, the constituent factors are made clear, and students grasp the technological essence necessary for large-scale computing.

Student learning outcomes

The objective is for the students to gain deep insights into some of the latest key technologies that enable modern supercomputers to function, such as resilience, massive (big) data. Many core / GPU technologies. Such technologies as well as algorithms and programming to enable their usage is not only restricted to supercomputing, but will be the key enablers for large-scale computing in IT systems in general.

Keywords

Supercomputer, Supercomputing, HPC, many-core / GPU, big data, fast interconnect. High bandwidth memory, parallel processing, parallel programming languages, Resilience, Low power computing

Competencies that will be developed

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

Class flow

The class will be taught in flip teaching style + active discussions in “US University” classroom style. A designated student will select the topical paper and present in front of the entire class and required to answer to the questions. Other students will also pre-read the papers and will join in the active Q&A and discussions. The result is deep understanding of the subject matter by all participating members of the lecture.

Course schedule/Required learning

  Course schedule Required learning
Class 1 History of Supercomputing none
Class 2 Leading applications in supercomputing required reading of the paper before class
Class 3 hardware architecture in supercomputers: CPUs required reading of the paper before class
Class 4 hardware architecture in supercomputers: interconnect required reading of the paper before class
Class 5 hardware architecture in supercomputers: high bandwidth memory required reading of the paper before class
Class 6 big data handling in supercomputers required reading of the paper before class
Class 7 system software in supercomputers: OS required reading of the paper before class
Class 8 system software in supercomputers: runtime required reading of the paper before class
Class 9 parallel programming in supercomputers: OpenMMP required reading of the paper before class
Class 10 parallel programming in supercomputers: MPI required reading of the paper before class
Class 11 performance modeling in supercomputers: basics required reading of the paper before class
Class 12 performance modeling in supercomputers: performance tools required reading of the paper before class
Class 13 resilience in supercomputers: detection and checkpointing required reading of the paper before class
Class 14 resilience in supercomputers: recovery required reading of the paper before class
Class 15 Future of supercomputers 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, C Language

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